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Literature Review Topic: The Impact of Mental Health on Juvenile Recidivism Rates

I have uploaded the guidelines for the literature review. Please review and make sure it is something that can be accomplished. It needs to follow APA 7th edition.   It will be about 10 pages in length once complete. It will be paraphrasing only.

The Format:
I. Title Page
II. Abstract Page
III. Literature Review
IV. References Page

I have uploaded all 8 articles that need to be in the literature review. 


Professional Writing for Social Work Literature Review Assignment (Due, Friday, Week 7,
by 11:59 PM CT—50% of Letter Grade)

The Assignment:

Using SocINDEX with Full Text and Social Work Reference Center—each hosted by EBSCO
but offering different search results—locate 10 to 15 topically related, full-text journal articles or
reports, published within the last three years, and analytically evaluate each work’s Introduction,
Method, Evidence, Results, Discussion (IMRAD), Conclusion, References, Appendix and other
Back Matter, as well as the document’s potential contributions to the field. Some articles not
based on quantitative data and surveys or experimentation will be argumentative, and as such,
will follow a thesis, evidence, and conclusion model.

After doing so, choose eight articles with which you will construct your literature review.

The document-wide formatting is APA, with a complete and properly formatted Title Page; the
References page should use the Article in a Print Journal entry, with DOI, if included:

Ramirez, B. (2016). The value of psychoanalysis. Sociology Experiments, 21(4), 76-115.

Note the capitalization, the italics use, the punctuation, et cetera. (FYI, most journals use volume
and issue numbers. The volume number is an annual number, while the issue number refers to
each published issue. Thus, volume 25 is 2011, and volume 26 is 2012, and on. Most journals
publish quarterly, so there are four issues per volume/year.)

The document will be double spaced, with Paragraph After set at 0, with 1-inch margins, and
will be composed in Times New Roman 12-point font. The document will include running page
numbers and an abstract page.

Properly formatted and properly written, the document will be approximately 10 pages long.

See the sample Literature Reviews in APA Academic Writer for guidance when drafting the
introductory and concluding paragraphs. The former introduces the document as a literature
review and the cohesive theme among the documents reviewed, while the latter ties the reviewed
documents together in theme or in importance or in another manner and then often looks forward
after doing so. (APA Academic Writer is located in the library databases; open an account the
first time you enter the site.)

The academic-style body/review paragraphs—one for each or the eight articles reviewed—will
be approximately 100-150 words long, with topic sentences, evaluation of IMRAD or Thesis,
Evidence and Conclusion, in-text citation, overall assessment of the document, and concluding

The document will utilize APA-style in-text citation—in paraphrases, not quotations.


(Paraphrasing and summary paraphrasing are skills that all sociology students must learn. So, let
us start practicing proper APA-style paraphrasing now.)

The Format:

I. Title Page
II. Abstract Page
III. Literature Review
IV. References Page

The Submission:

Please upload the completed, copyedited, proofread, correctly formatted Literature Review as a
Word document to the Wk 7 Literature Review Assignment tab by 11:59 PM CT, Friday of
Week 7 of the semester.

Skinner-Osei et al. Justice Policy Journal, Fall, 2019

Justice-Involved Youth and Trauma-Informed Interventions 1

Justice-Involved Youth and

Precious Skinner-Osei,1 Laura Mangan,2 Mara Liggett,3 Michelle
Kerrigan,4 Jill S. Levenson5
Justice Policy Journal � Volume 16, Number 2 (Fall, 2019)

© Center on Juvenile and Criminal Justice 2019 �

Professionals working in the juvenile justice system must consider the impact of
trauma on justice-involved youth when creating interventions and policies. Most
youths involved with the justice system have a history of childhood adversity.
Juvenile justice service systems should work to implement trauma-informed
interventions that address the needs of youth with mental health and trauma-
related disorders. The adoption of a trauma-informed approach throughout the
juvenile justice system and the implementation of interventions for juvenile
offenders with a history of trauma exposure has enormous potential benefits for
justice-involved youth, the staff who work with them, their families, and the
community at large.

1 Florida Atlantic University
2 Florida Atlantic University
3 Florida Atlantic University
4 Florida Atlantic University
5 Barry University

Corresponding Author:
Precious Skinner-Osei
[email protected]

2 Justice-Involved Youth and Trauma-Informed Interventions

The United States leads the industrialized world in the rate at which young people
are incarcerated (Annie E. Casey Foundation [AECF], 2013). Approximately 45,000-
60,000 youth under age 18 are incarcerated in juvenile correctional facilities and
adult prisons on any given day (American Civil Liberties Union [ACLU], 2018;
Hockenberry & Sladky, 2018). In 2014, an estimated one million children were
arrested (Children’s Defense Fund, 2018). In 2015, 48,043 children were detained
overnight (Children’s Defense Fund, 2018). Although the numbers were declining in
2016, an estimated 856,000 children were arrested that year (Children’s Defense
Fund, 2018). Incarcerating youth poses lifelong consequences by cutting them off
from their families, compromising their education, disrupting their social
relationships, possibly increasing their chance of recidivating, and often exposing
them to further trauma and violence (AECF, 2013; OJJDP, 2016; Hancock, 2017;
ACLU, 2018).

One out of every 14 children in the United States has had an incarcerated parent
(Murphey & Cooper, 2015; Zoukis, 2017). Although a precise number is difficult to
ascertain, it is estimated that half of justice-involved youth [JIY] in custody have or
had a parent or close relative in custody (Butterfield, 1999). Delinquent behavior is
often a learned behavior and an inherited consequence of their parent’s
incarceration. Research has shown that having a parent in jail or prison has
produced severe trauma in some children, and parental incarceration is one of the
ten primary adverse childhood experiences (ACEs) identified by the Centers for
Disease Control (Skinner-Osei & Levenson, 2018). Exposure to parental
incarceration has shown a significant relationship with delinquency and other
maladaptive behaviors (Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards, &
Marks, 1998; Baglivio, Epps, Swartz, Huq, Sheer, & Hardt, 2014; Skinner-Osei, 2018).
Parental incarceration can create profound shame and stigma for children and
their families. When a parent is incarcerated, children are locked behind
metaphorical bars, and they must cope with erroneous and damaging assumptions
from their peers, teachers, and even other family members (Skinner-Osei &
Levenson, 2018). For many children, parental incarceration is an intergenerational
family legacy, mainly because they are at risk of repeating what has been modeled
to them (Skinner-Osei & Levenson, 2018; Levenson, 2019).

Children in the justice system are often viewed as beyond hope and
uncontrollable. They may appear angry and defiant when, in actuality, they are
stricken with loneliness, depression, abandonment, powerlessness, and fear (Office
of Juvenile Justice and Delinquency Prevention [OJJDP], 2012). What masquerades
as intentional defiance and aggression is often a defense against the despair and

Skinner-Osei et al. Justice Policy Journal, Fall, 2019

Justice-Involved Youth and Trauma-Informed Interventions 3

hopelessness that traumatic events have caused in their lives (Skinner-Osei &
Levenson, 2018). These characteristics are exacerbated by the use of the outdated
and harmful “training school” model that punishes JIY by placing them in remote
prison-like settings (ACLU, 2018). Many youths are incarcerated for non-violent
offenses; primarily low-level property offenses, public order offenses, and status
offenses, such as possession of alcohol and truancy (OJJDP, 2012; AECF, 2013;
Campaign for Youth Justice [CFYJ], 2016).

The consequences are far-reaching. Many of these youths are offending due to
pre-existing trauma stemming from some form of maltreatment and/or family
dysfunction. Moreover, abused or neglected children have an increased likelihood
of running away from home (Kaufman & Widom, 1999). Many who run away are
between the ages of 12-17 and have suffered some form physical, sexual, verbal, or
emotional abuse inflicted by relatives or close family friends (Kaufman & Widom,
1999; Kunz, 2017; Dowshen, 2018; Bryan, 2019). Other reasons involve family
conflict and dynamics, personal crisis, sexual orientation, divorce, death, school
problems, and addiction (Dowshen, 2018; Congressional Research Service, 2019).
Runaways are at increased risk for arrest, and when they are thrust into the
juvenile justice system [JJS], it serves as further abuse and may re-trigger or worsen
their trauma.

In nine states running away is considered a low-level status offense meaning
that youth may be fined, given probation, have their driver license suspended,
required to have a drug screening, or be forced to return to the chaotic home life
that they were fleeing. The combination of running away and childhood
victimization increases the likelihood that these youths will be arrested (Kaufman &
Widom, 1999). Runaway and homeless youth have higher rates of involvement in
the JJS (Youth.Gov, 2019). At least half of runaway and homeless youth had been
arrested at least once since they first ran away, and many others had been arrested
multiple times (National Conference of State Legislatures [NCLS], 2019; Youth.Gov,
2019). Many of their arrests can be attributed to the activities that they must
endure to survive, such as survival sex, substance use, and physical abuse (NCLS,

Over the last decade, a significant amount of attention has been given to
criminal justice reform, and notably, the reduction of juvenile offenses and the
effectiveness of the front-line staff who work with them. However, there is still a
substantial need for more evidenced-based trauma-informed interventions and
rigorous training protocols for professionals working in juvenile correctional
facilities. Also, other variables, such as trauma-informed understanding of
criminality, mental health, and neurophysiological development, need to be

4 Justice-Involved Youth and Trauma-Informed Interventions

considered. The consideration of these variables has amplified the U.S. Department
of Justice’s mission to create and support more trauma-informed interventions
(Branson, Baetz, Horwitz, & Hoagwood, 2017). This paper will outline a history of
the JJS, provide evidence supporting trauma-informed interventions, and conclude
with implications for education and training, policy, and advocacy, and prevention.

History of the Juvenile Justice System
The purpose of the JJS is to increase safety in the community, bring about justice for
crimes committed, and rehabilitate troubled youth (McCord, Widom, & Crowell,
2001; Downey, 2011; Russell & Manske, 2017; Troutman, 2018). Over the last 170
years, the juvenile justice paradigm has shifted continuously concerning age, nature
of the crime committed, punitive accountability, rehabilitation, and sustainable
community safety (Russell & Manske, 2017). Before 1899, the United States
operated under the old British system of justice, which considered the ages of
seven to fourteen a gray zone (Dialogue on Youth and Justice, 2007). Although
many presumed a child so young was incapable of knowingly committing a crime, if
it was determined that the child understood the difference between right and
wrong, they could receive the same punishment as an adult offender (McCord,
Widom, & Crowell, 2001; Dialogue on Youth and Justice, 2007; Taylor & Fritsch,

During the nineteenth century, institutions such as the Chicago Reform School,
Society for the Prevention of Juvenile Delinquency, and the New York House of
Refuge were created to address the treatment of JIY (Dialogue on Youth and Justice,
2007; Troutman, 2018). This system of juvenile social reform led to the first juvenile
justice court in Cook County, Illinois in 1899 (Mears, Pickett, & Mancini, 2014;
Russell & Manske, 2017). The focus was on the child, the approach was informal,
non-adversarial, flexible, and the cases were treated as civil actions instead of
criminal (Dialogue on Youth and Justice, 2007).

Although the American JJS claimed to be rehabilitative, it actually became more
punitive for several reasons: (1) Inconsistencies in policy and procedure. Initially,
there were fifty-one individual JJS across the U.S. that operated independently of
one another (McCord, Widom, & Crowell, 2001); (2) Out of consideration for victims,
there was an increasing demand for JIY to be held accountable (McCord, Widom, &
Crowell, 2001); and, (3) The number of violent crimes committed by juveniles
consistently increased (McCord, Widom, & Crowell, 2001). Although the causes and
consequences of crime seemed to justify increasingly punitive measures, the
constitutional rights of JIY were violated for decades in the early part of the 20th

Skinner-Osei et al. Justice Policy Journal, Fall, 2019

Justice-Involved Youth and Trauma-Informed Interventions 5

century. In the 1960s two controversial court cases, Kent v. the U.S. (1966), and In re
Gault (1967), changed how juvenile cases proceeded through the court system
(McCord, Widom, & Crowell, 2001; Downey, 2011). The outcomes led to increased
constitutional protections for minors, and they were given the same due process
rights as adults (Downey, 2011).

Following In re Gault, Congress passed the Juvenile Delinquency Prevention and
Control Act in 1968. The premise of the act relied on emerging research that
suggested that when pursuing punishment, courts should consider the social and
behavioral environment of youth. Courts were encouraged to take into account a
youth’s history of abuse and trauma, family cohesiveness, social connections,
education, and, more importantly, the likelihood of successful rehabilitation
(Downey, 2011). The act sought to prevent juvenile delinquency, deinstitutionalize
youth in the system, and keep JIY separate from adult offenders, which was
significant because evidence had long shown that juvenile crimes became more
extreme after they were confined with adults. Additionally, to better serve JIY, the
act created three entities: 1) The Office of Juvenile Justice and Delinquency
Prevention [OJJDP]; 2) The Runaway Youth Program; and, 3) The National Institute
for Juvenile Justice and Delinquency Prevention [NIJJDP] (Impact Law, 2019).

The intentions of the act were short-lived and contradictory. The act was
amended and abandoned its original goal of rehabilitation. Similar to the adult
system, it reverted to punitive measures. It was amended to include provisions that
allowed some states to try JIY as adults for some violent crimes and weapons
violations (Impact Law, 2019). The new provisions were fueled by prison
administrators, justice practitioners, policymakers, and the public. All parties
cohesively insinuated that rehabilitative measures were not effective, mainly
because juvenile crime continued to rise. A plethora of research about the needs
and well-being of JIY was minimized or ignored, while the publication of Robert
Martinson’s 1974 study concluding that “Nothing Works” was used as evidence and
reason to support increased punitive measures. The Nothing Works Doctrine
analyzed programs that were designed to reduce recidivism to determine if they
were effective, and furthered questioned if rehabilitation was possible (Martinson,
1974). The most detrimental consequence derived from the doctrine is that it
inspired mandatory minimum sentences and the removal of judicial discretion
(Levenson & Willis, 2018).

Even with the new extreme punitive measures, crime continued to rise in the
juvenile and adult systems. From 1980-1994 there was a significant surge in the
number of violent criminal offenses committed by JIY, which motivated states to
adopt even more aggressive policing, which bled into the school system (McCord,

6 Justice-Involved Youth and Trauma-Informed Interventions

Widom, & Crowell, 2001; Wald & Losen, 2003; Backstrom & Walker, 2006; Bryer &
Levin, 2013). This get-tough approach, including what became known as the “school
to prison pipeline” (Wald & Losen, 2003), propelled more stringent legislation that
immediately increased the number of youths incarcerated. Tens of thousands of
youth were placed in correctional facilities that offered little if any rehabilitative
programming (Bryer & Levin, 2013).

Although these increasingly punitive measures yielded results that illustrated
the tough on crime tactics were ineffective, there was a reluctance to consider
reasons why youth were committing crimes and how to intervene early and
preventively. Instead, politicians used the media to support a tough-on-crime
agenda, characterizing JIY as violent and irredeemable instilling fear in the public.
In 1996 John Delulio informed policymakers and the public of a dire threat of super-
predators, whom he defined and described as “radically impulsive, brutally
remorseless, rapists, murders, burglars drug dealers, and gang members” (Kelly,
2016, p.1; Fair Punishment Project, 2016). Instantly, politicians and most notably
First Lady Hillary Clinton, begin to use the label to help generate support for
tougher crime policies (Fair Punishment Project, 2016). As labeling theory infers, the
power of labels, particularly shaming and stigmatizing labels, further separates
justice-involved persons from society and reinforces deviant identity and criminal
behavior (Levenson & Willis, 2018). As Charles Cooley theorized, our impressions of
ourselves are shaped by how others treat us, which in return helps to shape our
constructions of social identity (Cooley, 1983 revision).

This cruel and unjust label helped to rapidly increase the number of JIY
transferred into adult prisons. Moreover, the label made it easier for the public to
endorse harsh policies such as the elimination of transfer restrictions and the ease
of thrusting JIY into adult courts even if they were younger and accused of lesser
offenses (Kelly, 2016). A study in Maryland found that the average sentence for a
17-year-old in adult court is approximately 41% longer than the 18-year-olds
(Gulstad, 2016). In 1996, the Department of Justice found that JIY in adult court were
more likely to be sentenced to prison (Gulstad, 2016).

Another culprit was racial disparities. Development Services Group, Inc. 2017
[OJJDP] stated that youths of color are more likely to be referred to the JJS than
whites. Although Delulio (1996) was not specific about the race of the super-
predators, society assumed that they were black and brown. In 1998 Frank Gilliam
published the Superpredator Script, finding that when people were shown a mug
shot of an African-American or Hispanic youth for just five seconds, they were more
afraid and more likely to support harsher punishments for youth (Gilliam & Iyengar,

Skinner-Osei et al. Justice Policy Journal, Fall, 2019

Justice-Involved Youth and Trauma-Informed Interventions 7

1998). Sadly, this was not surprising because the criminal justice system was
idealized out of oppression and discrimination (Alexander, 2012).

In the late 1990s, the criminal justice pendulum swung back a bit, and
policymakers agreed that reform was warranted. They encouraged research,
evidence-based interventions, mental health evaluations, and education and
training for professionals working with JIY (National Research Council, 2014).
Although these goals were well-intentioned and pragmatic, many politicians
ignored suggestions from research findings and continued to perpetuate fear even
when their insinuations were falsified by empirical evidence. Although minimal
changes were being made or suggested, many JIY were warehoused in horrific
conditions that created or worsened their conditions (Shields, 2011). They were
further abused and traumatized, and their mental health needs were ignored.

Mental health disorders are prevalent in the JJS (Development Service Group,
Inc. [OJJDP], 2017). An estimated two-thirds of JIY have a diagnosable mental health
disorder compared to an estimated 9 to 22 percent of the general youth population
(Teplin et al., 2005; Schubert & Mulvey 2014; Development Service Group, Inc.
[OJJDP], 2017; National Conference of State Legislatures [NCLS], 2019). In 2014 The
National Survey on Drug Use and Health estimated that 11.4 percent of adolescents
aged 11 to 17 had a major depressive episode in the past year (Center for
Behavioral Health Statistics and Quality, 2015). Fazel, Doll, and Langstrom (2008)
also found that youths in detention and correctional facilities were almost ten times
more likely to suffer from psychosis than youths in the general population.

The Pathways to Desistance Study followed more than 1,300 youths for 7 years
and found that the most common mental health problem was substance use
disorder (76 percent), high anxiety (33 percent), ADHD (14 percent), depression (12
percent), PTSD (12 percent, and mania (7 percent) (Development Service Group, Inc.
[OJJDP], 2017). As cited in Development Service Group, Inc. [OJJDP], (2017, p. 3)
Wasserman et al. (2010) conducted a multisite study that analyzed system intake,
detention, and secure post-adjudication and found that 51 percent of the youth
met the criteria for one or more psychiatric disorders. Furthermore, the
Northwestern Juvenile Project found that 46 percent of males and 57 percent of
females had two or more psychiatric disorders (Development Service Group, Inc.
[OJJDP], 2017). Also, a study in Texas, Louisiana, and Washington found that 79
percent of the youths diagnosed for one mental health disorder also met the
criteria for two or more diagnoses (Teplin et al., 2005; Development Service Group,
Inc. [OJJDP], 2017). Research shows that many of behavioral health disorders are
related to, and symptomatic of, early childhood trauma such as abuse, neglect,
family dysfunction, poverty, and violent communities (Fox, Perez, Cass, Baglivio, &

8 Justice-Involved Youth and Trauma-Informed Interventions

Epps, 2015; Baglivio, Wolff, Piquero, Greenwald, & Epps, 2017; Levenson & Willis,

Even with this knowledge, there is still a significant lack of services pre and post-
release in correctional facilities and communities. Instead of receiving adequate
treatment, many are warehoused in correctional facilities that lack psychotherapy
and other health services (Shields, 2011). The lack of services, or in many cases, the
non-existence of services, violates JIY’s 8th and 14th Constitutional rights, which
state that JIY with severe mental disorders must receive treatment while confined in
a secure public or private state correctional facility (Grisso & Underwood, 2004;
Teplin et al., 2005). The United States has a history of warehousing the mentally ill
and favoring institutionalization over rehabilitation. An example is the California
Youth Authority [CYA], who has a reputation for being dangerous for JIY (Kita, 2011).
CYA once housed an estimated 10,000 JIY (Kita, 2011). CYA was not set up to house
JIY, especially those with minor offenses (Kita, 2011). Like most correctional
facilities, CYA was made with the perpetrator in mind, with strong potential for re-
traumatization for youth with a history of childhood adversities (Levenson & Willis,
2018). At CYA, there was no separation of JIY based on age and severity of the crime
(Ulloa, 2019). So those with non-violent, low-level offenses were housed with violent
gang members, sexual offenders, and repeat offenders. Additionally, many
endured 23-hour lockdowns, beatings by staff, and being caged (Ulloa, 2019). A
Grand Jury found that the children received their schooling while in cages, and they
were frequently drugged and improperly cared for (Kita, 2011). The Grand Jury also
found that CYA used excessive chemical restraints (Kita, 2011). The CYA medical
staff admitted to the Superintendent that their workload was too large, which
prohibited them from adequately providing mental health care services (Kita, 2011).
At the time, there was only one full-time psychologist and one part-time psychiatrist
to serve 750 wards (Kita, 2011).

As with other components of the criminal justice system, racial disparities also
exist when it comes down to those who receive mental health services (Baglivio et
al., 2017). African American JIY are less likely to receive substance use or mental
health treatment (Development Services Group, Inc. 2017, [OJJDP]). Spinney et al.
(2016) completed a systematic review that analyzed articles published from 1995-
2014 that examined racial disparities in the JJS and concluded that there was some
race effect in deciding who received services. Aalsma et al. (2014) also concluded
that whites were more likely to see a mental health clinician within the first 24
hours of detention intake and to receive a referral for mental health services after

Skinner-Osei et al. Justice Policy Journal, Fall, 2019

Justice-Involved Youth and Trauma-Informed Interventions 9

Childhood Trauma and Justice-Involved Youth
At the turn of the millennium, the focus shifted again from confinement to

understanding why youth commit crimes. This time around was different because
some policymakers had expanded their views and were interested in discussing
what reform would entail. Also, there was a surge of research on adolescent
behavior, co-occurring disorders, and neurodevelopment. The research implied
that many youths offended because they were faced with a multiplicity of
psychosocial challenges, complicated family situations, and co-occurring mental
health and substance use disorders (Thomas & Penn, 2002). Further research
emerged concerning adolescent development and behavior, explicitly illustrating
that neurodevelopment in the prefrontal cortex of the brain is not fully developed
until people reach their mid-20s; these areas are responsible for cognitive
processing as well as the ability to inhibit impulses and weigh consequences before
acting (OJJDP, 2012). The way JIY internalize and externalize problems might be
related to their deficient emotional and behavioral regulation skills, supporting the
notion that children and adolescents may not be criminally responsible for their
actions because developmentally they are different from adults (McCord, Widom, &
Crowell, 2001; Marrow, Knudsen, Olafson, & Bucher, 2012).

Neurocognitive functioning is further compromised for children exposed to
traumatic incidents, chronic abuse, or neglect. Cognitive processing and self-
regulation can be under-developed when daily survival skills become prioritized in a
traumagenic environment (van der Kolk, 2006). The quickly expanding research
literature has informed the understanding of the impacts of chronic toxic stress on
the developing brain, and the relationships between early trauma, self-regulation,
and criminality (Wolff & Baglivio, 2016; Holley, Ewing, Stiver, & Bloch, 2017). Many
JIY experienced trauma-related neurodevelopmental changes in the brain that
manifest in disrupted cognitive and psychosocial development (Marrow et al.,
2012). The threat of childhood trauma is so severe that it is considered a public
health concern (Branson et al., 2017; Center for Disease Control and Prevention,
2018). More than half of young children ages 0-5 experience a traumatic event such
as physical trauma, abuse or neglect, and exposure to domestic and or community
violence (Marrow et al., 2012; Buss, Warren, & Horton, 2015). Traumatic events may
include exposure to actual or threatened death, serious injury, sexual abuse,
physical abuse, domestic violence, community and school violence, medical trauma,
motor vehicle accidents, acts of terrorism, war experiences, natural and human-
made disasters or the physical integrity of self or others (American Psychological
Association, 2008; Diagnostic and Statistical Manual of Mental Disorders-IV and V,
2013; De Bellis & Zisk, 2014).

10 Justice-Involved Youth and Trauma-Informed Interventions

In the United States, approximately 50% to 80% of JIY report some form of
victimization (Ford, Grasso, Hawke, & Chapman, 2013). The risk for posttraumatic
stress and mental health disorders is increased by at least twofold and could be as
far upward as tenfold for youth exposed to traumatic events such as emotional,
physical, and sexual abuse, intimate partner, family, or community violence (OJJDP,
2012). In 2009, one in ten children experienced poly-victimization, which increases
the risk of academic disengagement, gang affiliation, depression, suicidality,
relationship volatility, substance abuse, and participation in behaviors that increase
criminogenic risk (Finklehor, Turner, Ormrod, Hamby, & Kracke, 2009; OJJDP, 2012;
National Center for Mental Health and Juvenile Justice [NCMHJJ], 2016).

A culmination of research indicates that between 75% and 93% of JIY are
exposed to multiple types of violence and traumatic events before contact with the
JJS (Ford, Chapmen, Hawke, & Albert, 2007; Ford et al., 2013; Listenbee & Torre,
2012; Marrow et al., 2012; NCMHJJ, 2016; National Child Traumatic Stress Network
[NCTSN], 2016; Rapp, 2016; Branson et al., 2017). JIY have three times more adverse
childhood experiences when compared to other youth (Baglivio et al., 2014; Yoder,
Whitaker, & Quinn, 2017). Research has also shown that time spent in correctional
facilities contributes to producing or exaggerating traumagenic experiences for
most people (Levenson & Willis, 2018; National Alliance on Mental Illness, 2019).
Sedlak and McPherson (2010) reported that more than a third of young people in
juvenile placement feared attacks from staff or other youths. Using data collected
from state agencies, researchers found that between 2004 and 2007 there was an
average of 10 assaults a day and approximately 13,000 documented reports of
physical, sexual, or emotional abuse by staff members (Mohr, 2008; White, Shi,
Hirschfield, Mun, & Loeber, 2010). In correctional facilities, routine practices such as
solitary confinement and use of restraints can be re-traumatizing for abused or
neglected youngsters, causing additional harm and further compromising their
mental and physical health (Hayes, 2004). Thus, recognizing the prevalence and
impacts of ACEs is crucial in understanding the importance of evidence-based and
trauma-informed juvenile justice practices.

Trauma-informed interventions with justice-involved youth
Developing a trauma-informed JJS involves cultivating an environment that
recognizes the impact of traumatic childhood experiences while “striving for a
physically and psychologically safe environment for both youth and staff in
detention” (Pickens, 2016, p. 226). According to the Substance Abuse and Mental
Health Services Administration [SAMHSA], trauma-informed care [TIC] is an
evidence-based practice that teaches service providers and their organizations

Skinner-Osei et al. Justice Policy Journal, Fall, 2019

Justice-Involved Youth and Trauma-Informed Interventions 11

about the triggers and vulnerabilities of trauma survivors and employs effective
interventions to treat traumatic responses (2015). TIC “involves understanding,
anticipating, and responding to peoples’ expectations and needs, and minimizing
the chances of re-traumatizing someone who is trying to heal” (SAMHSA, 2015). TIC
provides an environment created on a foundation of safety, empowerment,
collaboration, trust, and respect (Fallot & Harris, 2009; Bloom, 2013). More
importantly, TIC is not intended to excuse delinquent behavior, but instead, its
primary goal is to recognize, conceptualize and respond to symptoms of trauma
such as behavioral and emotional dysregulation (Levenson, 2019).

In the late 1990s, the significance of ACEs garnered massive attention
surrounding trauma-informed interventions. In juvenile justice programs, such
models are designed to help advance coping strategies, improve problem-solving,
and implement positive self-correction skills rather than simply punitive responses
(Skinner-Osei & Levenson, 2018; Levenson, 2019). The Coalition for Juvenile Justice
advocated for a continuum of care that catered to the specific needs of JIY,
particularly mental health services and trauma-informed interventions (Thomas &
Penn, 2002). However, over the last twenty years, the number of JIY has increased
faster than those of adults, even as the need for trauma-informed interventions is
being recognized (Demeter & Sibanda, 2017). Scarce funding, as well as inadequate
training and lack of researcher-agency collaboration, may explain why the
implementation of TIC has not kept pace with the need. A study conducted in 1998
found that only 71% of juvenile correctional centers reported that they screened for
mental health issues (Desai, Goulet, Robbins, Chapman, Migdole, & Hoge, 2006).
The same study on PTSD in incarcerated adolescents reported that only 55.8% of
juvenile correctional settings offer psychiatric evaluation beyond mental health
screenings (Ulzen & Hamilton, 2003).

Assessments of childhood trauma and related mental health needs are essential
in providing appropriate care for JIY and potentially increasing the success of the JJS
in preventing recidivism. Although research has shown that early screenings are
significant, there is still a disconnect with policymakers providing adequate funding
and resources. In 2005 Gallagher and Dobrin utilized data from the 2000 Juvenile
Residential Facility Census (n = 3,690) and found that if every child and adolescent
that entered a correctional facility was screened within the first 24 hours, the risks
of serious suicide attempts may be reduced. Furthermore, Grisso and Underwood
(2004) concluded that, 1) Screening should be performed for all JIY at the earliest
point of contact with the JJS; 2) Assessments should be performed for JIY who
require further evaluation; 3) Care should be taken to identify the most appropriate

12 Justice-Involved Youth and Trauma-Informed Interventions

A lack of instruments designed specifically for identifying trauma in JIY is a
concern, as is the debate that is centered on nothing works versus what works and
what works well. The task at hand begins with appropriate trauma screening, and
also requires adoption of intervention models that provide more than the bare
minimum of services. TIC works from the top-down and the bottom-up and begins
with stakeholder buy-in to achieve policy changes, provide more funding, and help
change the perception of those who still view JIY as super-predators. Then, training
for all clinical, correctional, and other staff coming in contact with JIY need to be
trained in understanding trauma, so that behaviors can be conceptualized within a
TIC framework and correctional environments can be modified to become less

Some of the most common trauma-informed interventions, instruments, and
curriculums utilized in the JJS are the Trauma and Grief Components Therapy for
Adolescents (TGCTA), Cognitive Processing Therapy (CPT), Trauma-Adapted
Multidimensional Treatment Foster Care (TA-MTFC), the Attitudes Related to
Trauma-Informed Care [ARTIC] questionnaire, and the Think Trauma Curriculum.
For this review, the Sanctuary Model and Trauma Affect Regulation Guide for
Education and Therapy [TARGET] were selected as prime examples. Both are
considered effective psycho-educational programs for JIY and correctional staff.
When used correctly, these interventions highlight characteristics that address
trauma, build skills, create healing relationships, and reduce criminogenic risks.

Sanctuary Model. The primary objective of the Sanctuary Model is to create a
culture within an organization that provides “a trauma-informed, evidence-
supported template for system change based on the active creation and
maintenance of a nonviolent, democratic, productive community to help people
heal from trauma” (NCTSN, 2008). The model provides a universal language that is
accessible to staff, clients, and other stakeholders. It is not rigid and can be adapted
to many settings and populations. The model also involves creating a culture of
nonviolence, emotional intelligence, inquiry, social learning, and shared
governance, facilitating “open communication, social responsibility, as well as
growth and change” (NCTSN, 2008). These goals are accomplished using three key
components: a shared language of Safety, Emotion management, Loss, and Future
[SELF], development of a core implementation team, and concrete intervention
tools. The success of the Sanctuary Model requires implementation across all levels
of an organization or institution (Pickens, 2016).

Some of the strengths of the model are its easy adaptability across many
cultures, its recognition of the stigma of mental illness, its demonstrated reduction
in the use of restraints in residential facilities, and its track record in improving staff

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Justice-Involved Youth and Trauma-Informed Interventions 13

retention (NCTSN, 2008). The drawbacks to this model include the time it can take
to implement (up to 2.5 years) fully and the cost of implementation ($65,000), which
can make it difficult for many organizations to obtain the funding needed to
incorporate the model efficiently (NCTSN, 2008).

TARGET. TARGET is appropriate for intervention in cases of complicated poly-
victimization as well as traumatic loss, for anyone over the age of ten years (NCTSN,
2012). TARGET is designed to be successfully implemented concurrently with other
evidence-based interventions, in conjunction with any work with families, and with
substance abuse treatment (NCTSN, 2012). TARGET is comprised of seven skills-
based steps taught over ten sessions: self-regulation via Focusing; trauma
processing via Recognizing current triggers; Emotions and cognitive Evaluations;
strengths-based reintegration by Defining core goals; identifying currently
effective Options; and affirming core values by Making positive contributions
[FREEDOM] (Marrow, et al., 2012). These skills are based on three primary
therapeutic components. A psycho-educational component helps individuals
understand the effects of PTSD on neurobiology and how PTSD is an adaptive
response to a perceived threat that can be triggered in the absence of an actual
threat. This component helps children understand why they feel and react in the
ways they do and shows them how they can regain control of their symptoms. The
second component consists of the teaching and guided practice of the FREEDOM
skills, and the third is an experiential component in which youth create a timeline of
their lives to help organize autobiographical memory, which can often be
fragmented in traumatized youth (Marrow et al., 2012).

The strengths of the TARGET intervention are plentiful, beginning with the
extensive psycho-educational component for both youth and staff, which helps to
explain the effects of trauma on the brain, body, emotions, behavior, and
relationships in everyday language that de-stigmatizes trauma. TARGET also
provides instruction and modeling of skills for symptom management and emotion
regulation, and with training and materials for reinforcement of new skills by non-
professional workers (NCTSN, 2012). As with the Sanctuary Model, a significant
drawback is the cost of training and maintaining fidelity and quality of
implementation, which is higher than with other, less comprehensive intervention
models. Despite the financial and time challenges associated with both
interventions, evidence-based TIC programming within juvenile justice facilities has
the potential to improve outcomes for both JIY and staff.

14 Justice-Involved Youth and Trauma-Informed Interventions

Implications for Practice, Policy, Advocacy, and Prevention
With an estimated 200,000 JIY transitioning back into their homes each year after
residential programs, there are significant implications for practice, policy, and
advocacy (Hancock, 2017). There are also important implications for prevention for
at-risk children. When correctional staff, probation officers, social workers, judges,
attorneys, advocates, clinicians, and teachers are trained in neurobiological and
psychosocial impacts of trauma, the futures of JIY can be drastically changed.

Front-line practice

Practitioners working directly with JIY should be continuously educated and trained
about the impact of trauma on neurocognitive functioning and mental health.
Specifically, staff should be aware of how traumatic stress reactions can manifest in
dysregulation and respond with effective trauma-informed methods of managing
problem behaviors (Pickens, 2016). Levenson (2019) states that there are two
primary goals when deploying TIC: (1) View maladaptive, problematic behavior and
presenting problems through the lens of trauma (case conceptualization), and (2)
Avoid disempowering dynamics in the helping relationship, which can re-traumatize
clients (trauma-informed responding). Justice-involved practitioners should also be
trained to understand the role of trauma exposure in the development of youth
criminogenic risk factors, so that they can successfully create, revise, and
implement effective interventions (Pyle, Flower, Fall, & Williams, 2016). Creating
safe spaces for youth to trust others and practice self-regulation and self-correction
skills can reduce risk factors for recidivism.

Recently, the Juvenile Justice Reform Act of 2018 was passed. This legislation is
momentous because it incorporates decades of research and practice about
criminogenic risks and needs of JIY in correctional systems and best practices for
responding to juvenile crime. The most significant piece of this legislation is that it
recognizes the role trauma play in offending, rehabilitation, and recidivism. The act
has incorporated programs to reduce juvenile delinquency, assist runaway youth,
and locate missing children (, 2017). The act also requires states to
update their plans to include alternatives to detention, transitional services,
screening for victims of human trafficking, appropriate accommodations for
pregnant JIY, and requires administrators to focus on reentry, mental, and
behavioral health (, 2017). The act aims to achieve this by including (1)

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Justice-Involved Youth and Trauma-Informed Interventions 15

more evidenced-based trauma-informed interventions, (2) revision of policies
regarding dangerous and inhumane confinement practices, (3) improvements in
the quality of educational services, (4) more attention and services for special youth
populations, and (5) more accountability for practitioners and youth (AECF, 2018).
Policies and procedures should continue to be revised throughout the entire
criminal justice system. The revisions should specifically address how trauma-
related behavioral problems can adversely impact juvenile justice staff and youth.
Policy and procedures should empower JIY and staff to develop a real ability to
assist each other in rehabilitative efforts. This is vital because program
management, health care services, facility security, and intervention management
have significant inverse relationships with recidivism (Hancock, 2017).

Advocacy and Prevention

There are more than 180 agencies that advocate for JIY (AECF, 2018). Juvenile Justice
advocates goals are: (1) work towards deinstitutionalization, (2) provide direct
service, (3) teach JIY and their families how to become self-advocates, and (4)
encourage JIY to be active in their treatment (Youth Advocate Programs, 2018).
Although advocates in the past were confident that there were opportunities for
effective treatment, rehabilitation, and intervention, the question of “what works”
has been consistently raised (Russell & Manske, 2017). In an attempt to figure out
what works, many advocates educated policymakers about the need for more
federal and state legislation, funding, awareness of trauma and mental health,
justice programs and services, and the prospect of positive outcomes for youth and
public safety (AECF, 2018).

Advocates are also asking for more evidenced-based interventions not only for
JIY but also for juvenile justice practitioners. Front-line workers such as correction
officers, probation officers, social workers, and attorneys experience vicarious
traumatization when working with JIY (Branson et al., 2017). The high rates of
traumatic stress in front line staff play a critical role in performance, treatment of
JIY, and outcomes (Branson et al., 2017). Practitioners can be most effective when
provided with the training and resources to facilitate best practices and appropriate
outcomes for the youth they serve.

Finally, TIC has important implications for prevention using a public health
model (Khanlou & Wray, 2014; Larkin, Felitti, & Anda, 2014). Primary prevention
puts universal precautions in place, while secondary prevention targets at-risk
populations, and tertiary prevention provides services to ameliorate the problem
after it has occurred (German, Horan, Milstein, Pertowski, & Waller, 2001). When we

16 Justice-Involved Youth and Trauma-Informed Interventions

recognize early adversity as a risk factor for delinquent behavior, we can advocate
for primary prevention services such as parenting assistance for at-risk families,
safety nets for impoverished and marginalized communities, early educational
opportunities like Head Start known to facilitate resilience in children, programs
that enhance positive role modeling for disadvantaged youth, and access to
affordable health and mental health services. Attending to the traumagenic
conditions that contribute to delinquent behavior can mitigate risk while offering
more cost-effective ways to improve desired outcomes such as reduced recidivism
and community safety.

The JJS has made significant strides; however, a substantial amount of work
remains. This work should be inclusive of more evidenced-based trauma-
responsive programs, awareness of the impact of trauma, increased mental health
screenings and services, and the creation of trauma-informed federal and state
legislation. Attention should also be paid to modifying correctional facility
environments to offer a physical and psychological milieu that provides safety,
trust, empowerment, and hope through corrective relationships with staff and
other adult role models. As illustrated, TIC can help reduce behavioral and security
concerns within juvenile justice facilities and improve overall youth outcomes by
reducing recidivism, improving mental health outcomes, and increasing self-esteem
and sense of self-efficacy among JIY. TIC initiatives “lay the groundwork for
developing a system of care for youth that supports collaboration within the
juvenile justice system” and between various social service systems (Pickens, 2016,
p. 226). This foundation, if laid correctly, could potentially change the lives of
hundreds of thousands of youth involved in the justice system, making our
communities safer for everyone.


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Justice-Involved Youth and Trauma-Informed Interventions 25

About the Authors
Precious Skinner-Osei is an instructor in the Phyllis and Harvey Sandler School of
Social Work at Florida Atlantic University. Her research interests and publications
are in the areas of trauma-informed care, justice-involved youth, children impacted
by parental incarceration, incarcerated African American fathers, and criminal
justice reform, recidivism, and reunification. E-mail: [email protected]

Laura Mangan is a graduate of the Phyllis and Harvey Sandler School of Social
Work at Florida Atlantic University. Her research interests are criminal justice
reform and trauma-informed care, specifically how a history of trauma and criminal
justice policies impact justice-involved persons mental health, their families, and
communities. E-mail: [email protected]

Mara Liggett is a graduate of the Phyllis and Harvey Sandler School of Social Work
at Florida Atlantic University. Her research interests are within the realms of
trauma-informed care within the juvenile justice system, the history and impacts of
poly-victimization, and the correlation between post-traumatic stress disorder and
recidivism within the juvenile populations. E-mail: [email protected]

Michelle Kerrigan is a graduate of the Phyllis and Harvey Sandler School of Social
Work at Florida Atlantic University. Her research focuses on trauma-informed care
in addiction treatment with a particular interest in the impacts of trauma history
concerning an individual’s quality of life as well as the importance of appropriately
educating staff. E-mail: [email protected]

Jill S. Levenson is a professor of Social Work at Barry University and a SAMHSA-
trained internationally recognized expert in trauma-informed care. She has
published over 100 articles about policies, prevention, and treatment for people
involved in justice settings. She has also been a clinical treatment provider for over
30 years, using a scientist practitioner model to inform both her research and her
work with clients. E-mail: [email protected]

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The Relationship Between Trauma, Recidivism Risk, and Reoffending
in Male and Female Juvenile Offenders

Nina A. Vitopoulos1,2 & Michele Peterson-Badali1 & Shelley Brown3
& Tracey A. Skilling2

Published online: 27 November 2018
# Springer Nature Switzerland AG 2018

Elevated rates of traumatic experience in the juvenile justice population are well established. Nevertheless, the role of trauma and
its application to rehabilitation and recidivism in a criminal justice context remains hotly debated, particularly for female youth.
The Risk-Need-Responsivity framework, the predominant model for risk assessment and case management in juvenile justice,
does not consider trauma to be a risk factor for offending. This study examined– Posttraumatic Stress symptomology, maltreat-
ment history, and childhood adversity – in relation to RNR risk factors for reoffending (criminogenic needs) and recidivism in a
sample of female and male juvenile offenders. Rates of PTS symptomology, maltreatment, and childhood adversity were
significantly higher in this sample compared to prevalence in the general population. Females were more likely to have expe-
rienced maltreatment. Several maltreatment and childhood adversity types were significantly related to criminogenic needs. PTS
symptomology and adversity were not significant predictors of recidivism when entered alongside criminogenic needs; however,
maltreatment was the strongest predictor of recidivism for both male and female youth in a model that included criminogenic
needs. Gender did not moderate the relationship between maltreatment and recidivism. The importance of considering youths’
maltreatment history in their rehabilitative care is discussed.

Keywords Maltreatment . PTSD . Childhood adversity . Youth justice . Risk need responsivity (RNR) . Gender differences

Almost all juvenile offenders report experiencing at least one
traumatic event over their lifetime (Ford et al. 2012; Wilson
et al. 2013), a rate much higher than in community samples
(Costello et al. 2002). Rates of Posttraumatic Stress Disorder
(PTSD) (11–67%; Abram et al. 2004; Dixon et al. 2005; Moore
et al. 2013), childhood maltreatment (40–77%; Coleman and
Stewart 2010; Moore et al. 2013), and childhood adversity (77–
95%; Baglivio et al. 2014; Wilson et al. 2013) are much higher
in justice system-involved youth than in the general population
(Afifi et al. 2014). These rates are two to three times higher for

female than male juvenile offenders (Coleman and Stewart
2010; Foy et al. 2012; Moore et al. 2013).

Shifting from prevalence to relationships, how the
connection between trauma and (re)offending is conceptualized,
has critical implications for policy and practice within and be-
yond corrections. Although trauma is clearly relevant to the lives
of many justice-involved youth, it is unclear whether these
trauma-related constructs are direct risk factors for reoffending
in justice system-involved youth and whether the relationship
between these constructs and reoffending differs for female and
male youth, questions we explored in the present study.

This lack of clarity is due, at least in part, to the fact that the
connection between childhood trauma – and its resultant
symptoms – and later justice system involvement has been
studied in distinct, and generally siloed, research literatures.
Within the maltreatment literature, symptoms of PTSD
(Ardino et al. 2013; Becker and Kerig 2011), experiences of
childhood maltreatment (Evans and Burton 2013; Mersky
et al. 2012; Smith et al. 2005) and – in addition to maltreat-
ment – exposure to multiple forms of childhood adversity
(e.g., economic hardship, parental mental illness) (Fox et al.
2015; Wolff et al. 2015) have been conceptualized as risk

* Nina A. Vitopoulos
[email protected]

1 Applied Psychology and Human Development, Ontario Institute for
Studies in Education, University of Toronto, Toronto, Canada

2 Present address: Child, Youth and Family Program, Center for
Addiction andMental Health, 252 College St., Toronto, OntarioM5T
1R7, Canada

3 Department of Psychology and Institute of Criminology and
Criminal Justice, Carleton University, Ottawa, Canada

Journal of Child & Adolescent Trauma (2019) 12:351–364

factors for offending (e.g., Widom 2017) for both male and
female youth. In contrast, in the correctional psychology liter-
ature, they are not conceptualized as direct predictors of future
criminal justice involvement among adjudicated adolescent
offenders (Bonta and Andrews 2017). In the Risk-Need-
Responsivity (RNR) framework (Andrews et al. 1990), a
widely-used correctional rehabilitation model, the relationship
between early trauma and later offending is accounted for by
other, well-established, risk factors for offending:
‘criminogenic needs’ such as substance use, anti-social peer
relationships, and pro-criminal attitudes, among others
(Andrews et al. 2006). These scholars argue that studies
reporting a direct link between trauma and (re)offending have
failed to include these important risk factors in their analyses
(Rettinger and Andrews 2010).

Differing understandings of the relationship between trau-
ma and offending are also reflected in framings of the role of
trauma in rehabilitative interventions in the criminal justice
system. While in the maltreatment literature, trauma is viewed
as both a way of understanding the causes of offending and as
a primary target for treatment regardless of gender (Evans and
Burton 2013; Mersky et al. 2012), in the correctional rehabil-
itation literature, trauma is an important consideration to the
extent that it that impacts the effectiveness of interventions
aimed at other proximal factors related to offending, rather
than as a primary treatment target (Bonta and Andrews 2017).

The Relationship Between Trauma
and Reoffending in the RNR Framework

Central to the RNR framework is the assertion that effective
correctional rehabilitative service must attend to the principles
of risk, need, and responsivity (Andrews et al. 1990). The Risk
Principle states that the intensity of rehabilitative intervention
should increase with individuals’ risk to reoffend. Risk is de-
fined in terms of variables demonstrated across meta-analytic
studies to be strong and direct predictors of re-offending (e.g.,
pro-criminal attitudes, antisocial peers, and personality fea-
tures such as impulsivity); these are termed criminogenic
needs (Bonta and Andrews 2017). If the goal of service is to
reduce re-offending, criminogenic needs are the appropriate
targets of programming (Need Principle). The Specific
Responsivity Principle states that in order to effectively ad-
dress criminogenic needs, services must be delivered in a
manner that takes into account individuals’ personal charac-
teristics and/or circumstances that impact the effectiveness of
treatment (Andrews et al. 2006). As noted above, certain
trauma-related factors (e.g., past neglect and abuse, mental
health difficulties, and adverse living conditions) are concep-
tualized as specific responsivity factors that may be important
to address to permit, or enhance the efficacy of, treatment of
criminogenic needs (Hoge and Andrews 2011). However, to

date there is little guidance in the RNR literature around how
specific responsivity factors should be integrated into the re-
habilitation process.

RNR is a gender-neutral framework: the same criminogenic
needs have been empirically established for males and females
(Bonta and Andrews 2017). This approach has been criticized
for not taking into account the potential differential impact of
particular gender relevant factors, such as a previous history of
trauma, on the offending behaviors of women (Belknap 2015;
Van Voorhis et al. 2010). Nevertheless, several large scale and
meta-analytic studies have demonstrated comparable predictive
validity for males and females of RNR-based risk assessment
tools (Andrews et al. 2012; Olver et al. 2014). However, several
smaller studies have reported that juvenile risk assessment tools
(in particular, the Youth Level of Service/Case Management
Inventory; YLS/CMI, (Hoge and Andrews 2002) are less effec-
tive at predicting recidivism for female than male youth
(Onifade et al. 2008; Schmidt et al. 2011; Vitopoulos et al.
2012). There is also evidence of differences in the salience of
risk domains for males and females (Olver et al. 2014; Andrews
et al. 2012). These studies suggest that while the RNR frame-
work highlights important risk factors for re-offending for both
genders, there remains a need to explore the possibility and
implications of variations in relevance of specific criminogenic
needs to male and female juvenile offenders.

Trauma and (Re-) Offending in the Broader

In contrast to RNR’s approach to risk, gender, and
reoffending, a body of scholarship – often coming from a
feminist perspective (Belknap 2015; Chesney-Lind and
Pasko 2013) – highlights potential gender-specific risk fac-
tors. These are the factors deemed unique to females, and
gender-salient factors, identified as important factors for all
but even more meaningful for females, that could have a sub-
stantial impact on outcomes for female offenders (Bloom et al.
2003; Chitsabesan and Bailey 2006). In this perspective,
trauma-related factors such as mental health needs, relation-
ship dysfunction, and abuse histories are seen as particularly
relevant to the lives of female offenders (Gavazzi et al. 2006).
Although emerging (Van Voorhis et al. 2010; Conrad et al.
2014), there is limited empirical evidence that these factors
contribute to risk prediction for girls and women over and
above established criminogenic needs (Andrews et al. 2012).
However, there is a substantial literature outside the RNR
context examining not only the prevalence of, but the relation-
ships between, offending and trauma for bothmale and female
youth that calls attention to the need for research on these
phenomena that also takes into account the significant strides
already made in juvenile corrections practice.

352 Journ Child Adol Trauma (2019) 12:351–364

Post-Traumatic Stress Symptomology Post-traumatic stress
symptomology refers to the features of PTSD as defined in
the DSM-IV/V (American Psychiatric Association 2000,
2013) triggered by experiencing or witnessing one or more
traumatic events, including flashbacks, nightmares, severe
anxiety, and uncontrollable thoughts about the event(s).
Aside from higher rates of PTSD found in both male and
female juvenile offender samples, there is evidence that the
severity of juveniles’ PTSD symptoms is associated with de-
gree of delinquency, even controlling for total number of trau-
matic events reported (Becker and Kerig 2011). Trauma ex-
perts have suggested that the DSM definition of PTSD, ini-
tially developed to describe the experiences and guide the
mental health treatment of combatants returning from war,
does not adequately describe the developmental impacts of
exposure to sustained, repeated, or multiple ‘traumatic’ expe-
riences reported by many survivors of childhood abuse
(Cloitre et al. 2009). Indeed, Smith et al. (2006) found that,
while meeting partial or full criteria for PTSD did not predict
re-offending for female juvenile offenders, exposure to trau-
matic events (i.e., whether or not, how many times, and over
what time period) did.

Childhood Maltreatment Childhood maltreatment includes
Bphysical and emotional ill-treatment, sexual abuse, neglect,
and exploitation that occurs to children under 18 years… that
results in actual or potential harm to the child’s health, devel-
opment or dignity^ (WHO 2016, p. 1), and would be grounds
for monitoring by child welfare services. In addition to re-
search reporting elevated rates of maltreatment history in ju-
venile offenders (Coleman and Stewart 2010; Abram et al.
2004; Smith et al. 2006; Moore et al. 2013), studies have also
reported robust relationships between maltreatment in child-
hood and adolescence, and subsequent offending (Evans and
Burton 2013; Mersky et al. 2012; Smith et al. 2005) for both
males and females.

As noted above, a critical limitation of these studies is their
lack of inclusion of established criminogenic needs alongside
maltreatment variables in their predictive models. The few
studies that have explored this question support the notion that
maltreatment is related to offending for at least some youth. In
a meta-analysis of the predictors of female youth offending
that included studies of both criminogenic needs and factors
proposed in the gender-responsive literature, Hubbard and
Pratt (2002) found that criminogenic needs (e.g., antisocial
peers) were the strongest predictors of offending but factors
identified as important in the gender-responsive literature,
such as histories of physical and/or sexual assault, also had
modest effect sizes.

There is also evidence of multiple pathways to offending
for girls and women, specifically. Reisig et al. (2006) found
that an RNR-based risk assessment tool was a robust predictor
of recidivism in women classified as following a ‘typically

male’ pathway into offending but that it did not predict recid-
ivism in women whose pathway into the justice system was
defined as ‘gendered’, that is, characterized by involvement
with the justice system via histories of abuse and drug depen-
dence (Daly 1994). Similarly, in a recent study of female ju-
venile offenders, almost half the sample followed a pathway
characterized by childhood abuse while the remaining youth
followed a gender-neutral pathway (Jones et al. 2014). Taken
together, the research suggests that maltreatment may be a
criminogenic need, at least for some offenders. However,
studies of this ‘gendered’ pathway have typically included
only female offenders; thus, it is important to examine the role
of maltreatment in reoffending alongside established
criminogenic needs in male, as well as female, youth.

Childhood Adversity Childhood adversity refers to the accu-
mulation of exposure to chronic stressors in early life.
Adversity typically includes experiences of maltreatment
(outlined above) as well as other familial (e.g., parental mental
health problems and criminality, separation from caregiver)
and socioeconomic (e.g., poverty) factors, experiences of dis-
crimination and other adverse personal experiences (i.e.,
witnessing or being the victim of violence by a same age peer,
the sudden or violent death of a loved one, childhood illness)
related to negative outcomes such as academic difficulties,
poor physical and mental health, and substance abuse
(Copeland et al. 2007; Dong et al. 2005; Green et al. 2010).
Studies of childhood adversity have highlighted direct links
between exposure to adversity and subsequent offending for
both male and female juvenile offenders (Fox et al. 2015;
Wolff et al. 2015), as well to subsequent PTSD and other
mental health difficulties that co-occur with – but are not nec-
essarily directly related to – offending (Becker and Kerig
2011; Wilson et al. 2013). However, these analyses have gen-
erally not included other variables known to predict
reoffending. The few studies that have examined possible me-
diating effects, suggest negative affect and association with
delinquent peers (Maschi et al. 2008) to be mediators in the
relationship between early adversity and later offending, par-
ticularly for male and female non-violent offending behavior.

The goal of the present study was to elucidate the relation-
ships between post-traumatic stress symptoms, childhood
maltreatment, childhood adversity, and reoffending in a sam-
ple of justice system-involved youth. With the inclusion of a
male comparison sample, we also sought to examine gender
differences in these relationships to understand whether these
trauma constructs merit further exploration as unique gender-
specific/salient or gender-neutral criminogenic needs in juve-
nile justice risk assessment and case management. Beginning
with descriptive analyses, we hypothesized that both female
and male justice-involved youth would have significantly
higher rates of maltreatment and childhood adversity expo-
sure, as well as higher rates of post-traumatic stress symptoms,

Journ Child Adol Trauma (2019) 12:351–364 353

compared to those found in the general youth population, and
that female youth would have significantly higher rates than
males. Next, we examined relationships between RNR
criminogenic needs and the trauma variables. We expected
that maltreatment and childhood adversity exposure, as well
as post-traumatic stress symptomology, would be positively
related to criminogenic needs. Finally, we sought to examine
whether there was a direct relationship between the trauma
constructs and reoffending and, if so, whether this relationship
remained significant beyond the contribution of the already
well-established criminogenic risk factors and whether this
relationship would be moderated by gender.



The sample consisted of 50 male and 50 female 13- to 19-year
old (M = 15.98, SD = 1.48) youth who were ordered to a ju-
venile justice clinic of a mental health agency in a large urban
city in Canada for assessment to assist with sentencing.
Female participants represent consecutive admissions for as-
sessments. Male participants were matched to female partici-
pants by date of assessment, age, and recidivism risk based on

their score on an empirically validated risk measure—the
Youth Level of Service Inventory/Case Management
Inventory (Hoge and Andrews 2002). Given that female youth
represent approximately 25% of youth involved in the justice
system (Malakieh 2017) and that male participants were
matched to female participants in order to control for timing
of assessment, age, and YLS score, our sample size was lim-
ited by the pace at which female youth were referred to the
clinic. The matching of males and females across these char-
acteristics allowed us to more meaningfully compare the im-
pact of childhood maltreatment, adversity, and PTSD
symptomology on male and female youth while controlling
for possible confounding variables. In addition, this method-
ology allowed for enhanced internal validity of the study:
RNR factors were identified prospectively through a compre-
hensive, consistent, multisource, multimethod assessment
process unlikely to be available through regular youth justice
services, thus yielding high-quality data on youth’s individual
RNR needs, mental health needs, and trauma-exposure histo-
ries. Only clients for whom consent was obtained to use clin-
ical information for research purposes were included in the
study; 82% of clients consented. Institutional Review Board
approval for this study was obtained.

As Table 1 shows, participants were ethnically diverse. The
charges precipitating their referrals for assessment included

Table 1 Demographic, criminal
history, mental health, and
recidivism characteristics
by gender

Variables M (SD) t df

Males Females Total

Age (years) 15.98 (1.49) 15.98(1.49) 15.98 (1.49) .00 98

Mean number of DSM diagnoses 1.80(1.28) 2.04 (1.85) 1.92 (1.59) −.75 98

Days to recidivism (N = 49) 383.00 (177.36) 455.05 (209.34) 413.90 (193.01) −1.30 56

Variables Percentage % χ2 Φ

Males Female Total

Percent ethnicity 1.08 .10

White 13 17 30

Black 22 18 40

East/West/South Asian 6 5 11

Other 9 10 19

Percent index offense 1.17 .11

Nonviolent 11 15 26

Violent (nonsexual) 33 32 65

Sexual 6 3 9

Recidivism-yes 28 21 49 1.96 −.14
Type of recidivism (N = 49) 5.31 .33

Violent 2 3 5

Non-violent 24 12 36

Administrative 2 6 8

Note. Male and female youth were matched for age. DSM Diagnostic and Statistical Manual of Mental Disorders

*= p < .05

354 Journ Child Adol Trauma (2019) 12:351–364

nonviolent (e.g., failure to comply with probation, theft, drug
related, break and enter), sexual (e.g., aggravated sexual assault,
sexual assault, invitation to touching), and violent but not sex-
ual (e.g., robbery, assault, threatening) offenses. Themajority of
youth (81%) were diagnosed with at least one psychiatric dis-
order at assessment (range = 0–7). There were no significant
gender differences in ethnicity, category of index offense, rate
of recidivism, age, or type of and time to recidivism in youth
who did re-offend. 49% percent of the sample re-offendedwith-
in a 2-year period (42% of females and 56% of males), with an
average of 413 days to recidvism, similar to estimates of the
broader justice-involved youth population of approximately
402 days to recidivism (Thomas et al. 2002).


At the time of assessment, clinicians (psychologist, psychia-
trist, or social worker) with five to 15 years’ experience
assessing juvenile offenders completed the Youth Level of
Service/Case Management Inventory (YLS/CMI) (see
Measures and Coding, below). They produced a report fo-
cused on mental health, criminogenic needs, and risk using
information from multiple sources, including file material
(e.g., criminal records, previous probation and mental health
reports), interviews with the youth and collateral sources (par-
ents, probation officers, etc.), and standardized tests and
checklists. Participants’ clinical charts and assessment reports
were reviewed and double coded for reliability by doctoral
level graduate students to gather information on demo-
graphics, offense history, charges, recidivism risk and
criminogenic needs, post-traumatic stress symptoms, as well
as information regarding youths’ past experiences of child-
hood maltreatment and adversity.

Measures and Coding

Risk to Reoffend and Criminogenic Needs The Youth Level of
Service/Case Management Inventory (Hoge and Andrews
2002) is a standardized instrument used to assess youths’
criminogenic needs and risk to reoffend. A 42-item checklist
produces a detailed survey of youth risk factors in eight do-
mains; each item is coded as present or absent. The first do-
main covers the youth’s criminal history and current convic-
tions which, while a significant predictor of recidivism, is not
a treatment target given its static nature. The remaining seven
domains are amenable to change and therefore labelled dy-
namic risk factors, or criminogenic needs, including: Family
Circumstances and Parenting (e.g., child-parent relationship
difficulties, parental monitoring and control), Current
School/ Employment Functioning (e.g., low achievement, tru-
ancy), Peer Affiliations (e.g., anti-social peers), Alcohol and
Drug Use (e.g., substance use interfering with functioning),
Leisure and Recreational Activities (e.g., limited involvement

in organized activities), Personality and Behavior (e.g., impul-
sivity, inadequate guilt feelings, verbal and physical aggres-
sion), and Antisocial Attitudes (e.g., attitudes favorable to
crime). Items within each of the eight risk/criminogenic need
domains are summed and the score is assigned a categorical
descriptor (low, moderate, high). Across domains, items are
summed to create a total risk score, which also corresponds to
a risk category (low, moderate, high, or very high). The mea-
sure also contains a checklist of additional personal character-
istics or experiences, distinct from the eight domains of
risk/need and not used to determine risk, which highlights
case management issues relevant to treatment responsivity.
The YLS/CMI possesses strong internal consistency and con-
current validity (Schmidt et al. 2005) and moderate to strong
predictive validity (Olver et al. 2014). In the current sample,
interrater reliability for the YLS/CMI total score was high,
with correlations among clinicians ranging from .80 to .98
(average r = .93).

Post-Traumatic Stress The Youth Self Report (YSR;
Achenbach and Rescorla 2001) assesses behavioral and emo-
tional problems in 11–18-year-olds. Respondents rate them-
selves over the past 6 months on 112 items. The YSR’s 14-
item Post-Traumatic Stress (PTS) Problems subscale reflects
experiences that may be indicative of post-traumatic stress
(i.e., BI have trouble concentrating or paying attention^, BI can’t
get my mind off of certain thoughts^, BI have nightmares^);
α = .85 in the current study. Significant relationships have been
reported between YSR PTS scores and self-report scales of
PTSD and dissociation (Sims et al. 2005), and YSR PTS scores
discriminated abused children who did and did not meet diag-
nostic criteria for PTSD (Ruggiero and McLeer 2000), provid-
ing evidence for the concurrent validity of the YSR PTS scale.
The YSR PTS scale has also been found to have better concur-
rent validity than PTS scales derived from teacher or parent
reports, and to be as valid as other scales specifically developed
to screen for symptoms of post-traumatic stress in youth
(Dongyoung et al. 2015). However, the PTS scale has shown
poorer sensitivity in psychiatric than general population sam-
ples (Sims et al. 2005; Ruggiero and McLeer 2000). In the
current sample, the YSR PTS scale was significantly and mod-
erately related to the YSR Internalizing Syndrome Scale
(r = .47, p = .02) but the relationship with the Externalizing
Syndrome Scale was non-significant (r = .18, p = .60).

Maltreatment Exposure Participants’ clinical files contained
information on maltreatment exposure prior to age 16 derived
from reports (e.g., criminal records, previous probation and
mental health reports) and interviews with youths and collat-
erals (parents, probation officers, mental health workers, etc.).
Five types of exposure were coded yes/no based on the Core
Clinical Characteristics measure, originally a clinician-
administered interview developed by the National Child

Journ Child Adol Trauma (2019) 12:351–364 355

Traumatic Stress Network (Hodges et al. 2013), including
sexual abuse, physical abuse, neglect, emotional/
psychological abuse, and witnessed domestic violence. To
be coded ‘yes’, exposure had to be supported by reports of
at least one informant at time of assessment (typically the
youths themselves); almost all reports were corroborated by
more than one informant/source (90%) and a majority were
documented by child welfare (54%). A ‘total maltreatment’
variable was calculated by summing across the five exposure
types; thus, each youth received a score ranging from zero to
five for this variable. Due to the retrospective nature of the
data available, frequency or severity of maltreatment episodes
could not be discerned. The total maltreatment variable was
used as a marker of possible complexity, following the same
format as the adverse childhood experience (ACE) literature
(Baglivio et al. 2014; Wolff et al. 2015) research whereby
categories of experienced adversities are used as a measure
of cumulative adversity exposure. The five maltreatment ex-
posure types were also included in a childhood adversity
scale, but were examined on their own due to their predomi-
nance in the literature, as well as in real-world practice with
regard to children/youth requiring the care of child welfare
services as a result of these forms of exposure.

Childhood Adversity In addition to the five maltreatment types,
11 childhood adversity variables were coded as present/absent
from assessment reports. Nine of the 11 adversity variables were
derived from the National Comorbidity Survey-Revised (Green
et al. 2010) to examine the relationship between childhood ad-
versities and adult psychiatric disorders in a large national US
population survey. These included three types of interpersonal
loss (parental death, parental divorce, and other separation from
parents or caregivers – e.g., foster care placement), four types of
parental maladjustment (mental illness, substance abuse, crimi-
nality, and violence) and two other forms of adversity (life-
threatening childhood physical illness and extreme childhood
family economic adversity). In addition to the Green et al.
(2010) variables, a ‘childhood bullying’ variable was coded on
the basis of research linking early victimization by bullying to
delinquent behavior in adolescence (Wong and Schonlau 2013).
The final adversity item included adverse events not captured in
the previous categories (e.g., experiences of sexual or serious
physical assault by a same-age peer, witnessing a sudden or
violent death or reporting significant emotional distress due to
the death of someone other than a parent). As with the maltreat-
ment variables, to be coded ‘yes’, exposure had to be supported
by reports of at least one informant at time of assessment (typ-
ically the youths themselves). Corroboration of the individual
adversity variables by multiple informants varied a great deal
depending on the variable, ranging from 100% for parental
death to 25% for early victimization by bullying. Given the
breadth of adversities examined, consistent corroboration by
multiple informants was not anticipated nor required for

inclusion. These 16 items were added to form a ‘total childhood
adversity’ variable. Interrater reliability for coding of the mal-
treatment and childhood adversity variables was strong (Landis
and Koch 1977), with a Cohen’s Kappa of .82 (p < .001).

Recidivism Recidivism was defined as a conviction for one or
more new offenses anytime during the period after the sen-
tencing date associated with the charge(s) that prompted the
youth’s referral for assessment. Conviction data, rather than
arrest data, provided a more reliable description of youths’
offending within the study period given stipulations under
Canada’s Youth Criminal Justice Act emphasizing the expe-
dient expunging of non-convictions. Furthermore, it is be-
lieved that conviction, rather than arrest data, more accurately
accounts for youth belonging to marginalized communities
having a greater likelihood of police involvement and arrest
without subsequent conviction. A two-year fixed follow-up
period from time of assessment was used to provide adequate
time to elapse to collect a sample of youth who had and had
not re-offending, as well as time for new offenses to be proc-
essed by both the criminal justice system and to be reflected in
police criminal record databases. Data were obtained from a
national police criminal record database.


Question 1: Do Boys and Girls Differ in their
Post-Traumatic Stress Symptoms, Maltreatment
Histories, and Cumulative Childhood Adversity?

Females (M = 12.00, SD = 6.20) scored higher than males
(M = 9.00, SD = 4.90), t(91) = −2.55, p = .01, d = .26 on the
YSR PTS Problems Scale. However, the proportions of male
(30%) and female (34%) youth who fell within the ‘high post-
traumatic stress’ category (defined as scores falling at or above
the Borderline-Clinical range) were similar, χ2(1) = .15,
p = .70,Φ = .04. Both were substantially higher than estimates
in the general population of PTSD which are 3–6%
(Kilpatrick et al. 2003). PTS symptoms were correlated with
the number of maltreatment types for female (r = .31, p = .03)
but not male (r = −.09, p = .56) youth. Although retrospective,
it is of note that 28 of the 30 youth falling into the high PTS
category had documented histories of exposure to one or more
traumatic events. Adversity and PTS symptoms were not cor-
related for males or females.

When examining exposure-based measures of traumatic
experience, number of maltreatment types ranged from 0 to
5 (M = 1.2, SD = 1.3); 72% of females and 50% of males had
previously experienced at least one type of childhood mal-
treatment, much greater than even the highest estimates
(32%) in the general population (Afifi et al. 2014). Girls
(45%) were more likely to have experienced two or more

356 Journ Child Adol Trauma (2019) 12:351–364

types of maltreatment than were boys (26%). Overall, females
had experienced more types of maltreatment (M = 1.50, SD =
1.30) than males (M = .96, SD = 1.20), t (98) = 2.01, p = .05;
d = .20. There were no significant gender differences in histo-
ries of physical abuse, neglect, and witnessing domestic vio-
lence; however, females were more likely than males to have
experienced sexual abuse (15% v 4%, χ2 (1) = 7.86, p < .01,
Φ = 0.28) and emotional/psychological abuse (14% v 5%, χ2

(1) = 5.26, p < .05, Φ = 0.23).
On the total childhood adversity measure, 95% of youth

had experienced at least one of the 16 adversities, with females
(M = 5.30, SD = 2.50) exposed to more types than males (M =
4.10, SD = 2.50), t (98) = −2.49, p = .01, d = .24. This rate is
also much higher than that of the general population, wherein
approximately 66% of people are estimated to have experi-
enced at least one adverse event in childhood (Copeland et al.
2007). Females were more likely than males to have been
separated from a caregiver (38% v 29%, χ2 (1) = 3.66,
p < .05, Φ = 0.19), had a parent with mental illness (18% v
9%, χ2 (1) = 4.11, p < .05, Φ = 0.20), and been exposed to
‘other’ adversity, including victimization by a peer or
witnessing death (26% v 16%, χ2 (1) = 4.1, p < .05,Φ = 0.20).

Question 2: How Are Post-Traumatic Stress
Symptoms, Maltreatment Histories, and Childhood
Adversity Related to Youths’ Criminogenic Needs?

There were no significant correlations between the YSR PTS
problems scale and youths’ criminogenic need or total risk
scores (Table 2). However, for both females and males, num-
ber of maltreatment types was positively correlated with total
risk and criminogenic need scores in the domains of family
and personality. Number of childhood adversities was also

correlated with total risk as well as with need scores in the
domains of family, substance abuse and personality.

In terms of specific maltreatment types, using Pillai’s trace,
youth who had experienced physical abuse had significantly
higher scores across criminogenic need domains than youth
who had not, V = .153, F(1,99) = 2.03; p = .05; follow up t-
tests revealed significant effects in the domains of education
(t(98) = 2.01, p = .047, d = .20), family (t(98) = 2.08, p = .040,
d = .42) and personality (t(98) = 2.99, p = .003, d = .29).
Comparing the total risk and criminogenic need domain scores
of youth who had experienced sexual abuse and youth who had
not, Pillai’s trace approached significance, V = .14, F(2, 99) =
1.85; p = .08); follow up t-tests revealed higher needs in the
domains of substance abuse (t(98) = 3.12, p = .002, d = .30),
family (t(98) = 2.41, p = .02, d = .69) and leisure (t(98) = 2.05,
p = .047, d = .20). Youth who had experienced neglect, emo-
tional abuse, or who had witnessed domestic violence did not
differ in their criminogenic need scores from youth who had not
experienced these forms of maltreatment.

In order to more closely examine the nature of the relation-
ship between the childhood adversity scale and criminogenic
risk, the childhood adversity scale was cut at the mean score of
5 to create a ‘low adversity’ and a ‘high adversity’ group.
Previous literature has indicated that youth with more than
four adversities in childhood are at highest risk for subsequent
negative outcomes (Dong et al. 2005). Using Pillai’s trace,
youth in the ‘high adversity’ group had significantly higher
scores across criminogenic need domains, V = .17, F(1,98) =
2.23; p = .03 than their ‘low adversity’ counterparts; follow up
t-tests revealed that the ‘high adversity’ group’s substance
abuse domain scores (M = 2.60, SD = 1.9) were significantly
higher than the ‘low adversity’ group’s scores (M = 1.72,
SD = 1.6), (t(98) = 2.32, p = .02, d = .23).

Table 2 Correlations between age, maltreatment total, childhood adversity total, PTS symptoms total, and YLS/CMI total and domain scores

Variables 1 2 3 4 5 6 7 8 9 10 11 12

1.Age – – – – – – – – – – – –

2.Maltreatment total .01 – – – – – – – – – – –

3.Childhood adversity total −.04 .80** – – – – – – – – – –

4. PTS problems scale .13 .21* .20 – – – – – – – – –

5. YLS/CMI total −.22* .17 .17 .13 – – – – – – – –

6. Criminal history .07 −.01 −.06 .09 .58** – – – – – – –

7. Family problems .01 −.30** .33** .10 .70** .28* – – – – – –

8. Education/employment −.22* .11 .06 −.01 .69** .22* .34** – – – – –

9. Peer relations −.16 .02 .08 .04 .72** .41** .40** .41** – – – –

10. Substance abuse −.10 .12 .24* .22* .70** .39** .40** .29** .55** – – –

11.Leisure/recreation −.20* .04 −.01 .06 .65** .43** .49** .35** .46** .39** – –

12. Personality/behavior −.32** .23* .21* .14 .76** .22* .49** .62** .41** .43** .35** –

13.Attitude/orientation −.27** .07 .04 .04 .79** .36** .56** .52** .52** .47** .44** .54**

**p < .01, *p < .05

Journ Child Adol Trauma (2019) 12:351–364 357

Question 3: Do Post-Traumatic Stress Symptoms,
Maltreatment Histories, and Childhood Adversity
Contribute to Recidivism Alongside Known
Criminogenic Needs?

In a preliminary analysis, a logistic regression examining
whether the total criminal risk score (along with age and gen-
der) predicted recidivism 2 years’ post-assessment was not
significant, χ2(3) = 4.62, p = .20, with similar results when
males and females were analyzed separately. The predictive
ability of the YLS/CMI was further explored using ROC
curve plotting. For the full sample, this resulted in a small
effect size with an area under the curve value of 0.59 (95%
CI = 0.48–0.70; p = 0.05).

Next, in order to examine whether post-traumatic stress
symptoms, childhood maltreatment, and cumulative child-
hood adversity predicted reoffending when examined along-
side established criminogenic needs, we tested three logistic
regression models, with recidivism (yes/no) as the outcome.
Gender and either post-traumatic stress symptoms (Model a),
number of maltreatment types (Model b) or total childhood
adversity (Model c) were entered in the first step of each
model. In Step 2, the eight criminogenic need domain scores
were entered. Age was not included as it was not correlated
with recidivism.

Model a (post-traumatic stress symptoms) was not signif-
icant (χ2 (10) = 13.08, p = .22) and contained no individual

significant predictors in either step 1 or step 2. Model b (total
childhood adversity) was also non-significant (χ2 (10) =
13.75, p = .19) at both steps of the model, although among
the individual predictors, criminal history was significant
(B = .30, Wald’s χ2 = 4.12, p = .04) in the second step of
the model. Model c, which included number of maltreatment
types, was significant overall, at both step 1 (χ2 (2) = 5.80,
p = .05) and step 2 (χ2 (10) = 17.90, p = .05); criminal histo-
ry, B = .30, Wald’s χ2 = 4.12, p = .04 and number of maltreat-
ment types, B = .47, Wald’s χ2 = 5.42, p = .02 emerged as
significant individual predictors of recidivism in step 2 (see
Table 3). The addition of the maltreatment variable to the
model significantly improved the predictive ability of the
overall model. The final model explained 22% (Nagelkerke
R2) of the variance in recidivism and correctly classified
71.7% of cases. Interpreting the odds ratios, wherein an
exp.(B) = 1 means no effect, exp.(B) > 1 means that predictor
increases the odds of the outcome, and exp.(B) < 1 decreases
the odds of the outcome, Table 3 shows that for each addi-
tional type of maltreatment experienced, youth were approx-
imately 60% more likely to re-offend, while with each point
increase on the criminal history score, youth were 35% more
likely to re-offend. A moderated logistic regression examin-
ing the interaction between gender and number of maltreat-
ment types was also examined (χ2 (11) = 21.73, p = .03),
although cautiously due to the model approaching saturation.
Similar to Model c, only criminal history (B = .35, Wald’s

Table 3 Model C: hierarchal
logistic regression with gender,
criminogenic domains and
maltreatment total

Gender entered first as covariate

Model variables Β SEβ Wald’s χ2 df p exp(B) CI (95%)

Lower Upper

Model E

Step 1

Gender .72 .43 2.80 1 0.09 2.05 0.88 4.74

Maltreatment Total .35 .18 3.83 1 0.05 1.41 0.99 1.99

Constant −.79 .39 4.04 1 0.04 0.45

Step 2

Gender .79 .48 2.74 1 0.10 2.20 0.86 5.61

Maltreatment total .47 .20 5.42 1 0.02 1.60 1.08 2.37

Criminal history .30 .15 4.12 1 0.04 1.35 1.01 1.81

Family −.02 .19 0.02 1 0.90 0.98 0.67 1.42

Education/employment −.07 .15 0.23 1 0.63 0.93 0.69 1.26

Peer relations .42 .25 2.77 1 0.10 1.52 0.93 2.48

Leisure .24 .32 0.54 1 0.46 1.27 0.67 2.38

Substance abuse −.19 .16 1.32 1 0.25 0.83 0.60 1.14

Personality/behavior −.11 .16 0.49 1 0.49 0.90 0.66 1.22

Attitudes/orientation .01 .20 0.01 1 0.98 1.01 0.68 1.49

Constant −1.86 .83 5.07 1 0.02 .16

358 Journ Child Adol Trauma (2019) 12:351–364

χ2 = 4.70, p = .03) and a main effect of maltreatment
(B = .52, Wald’s χ2 = 5.84, p = .02) emerged as significant
in the model and no significant interaction was found, indi-
cating that gender did not moderate the relationship between
number of maltreatment types and recidivism.

Due to the relationships found between physical and sexual
abuse and youths’ criminogenic needs, follow-up analyses
were conducted to determine if two these forms of maltreat-
ment, alone, might contribute to the prediction of re-offending
in models containing gender and the criminogenic needs.
Neither models, containing physical abuse alone (χ2 (10) =
15.29, p = .12) or sexual abuse alone (χ2 (10) = 12.65,
p = .124), were found to be significant overall. However, in
the physical abuse model, criminal history (B = .29, Wald’s
χ2 = 3.83, p = .05) was significant and physical abuse
(B = .89, Wald’s χ2 = 3.26, p = .07) approached significance.


Trauma has consistently been posited as a gender-salient
criminogenic need by scholars who advocate for a gender-
specific approach to risk assessment and treatment for female
juvenile offenders. While a few scholars have examined the
potential contribution of trauma in the context of the Risk-
Need-Responsivity framework with adult populations, this is-
sue received scant attention with juvenile offenders. In the
current study we examined: whether elevated rates of
trauma-related exposure and symptomology functioned as di-
rect predictors of reoffending or were better described as co-
inciding vulnerabilities in a high-risk population, their rela-
tionship to the RNR model’s gender-neutral criminogenic
needs, and (alongside these known criminogenic needs)
whether they predicted reoffending.

Post-Traumatic Stress, Maltreatment, and Childhood

Consistent with previous literature (Becker and Kerig 2011;
Coleman and Stewart 2010; Moore et al. 2013; Smith et al.
2006; Wilson et al. 2013) both male and female justice in-
volved youth had higher rates of elevated post-traumatic stress
symptomology, exposure to maltreatment, and childhood ad-
versities than reported in the general population. Female youth
were significantly more likely than male youth to have been
exposed to at least one type of maltreatment and multiple
types of maltreatment. Female youths’mean childhood adver-
sity score was also significantly higher than males’. However,
while the girls’ scores on the PTS symptoms scale were sig-
nificantly higher than the boys’, the proportions of boys and
girls who fell into the ‘high’ PTS groups were similar.

There was a lack of relationship between the symptom-
based measure of post-traumatic stress and the two exposure-

based trauma measures; the only significant relationship was
between number of maltreatment types and PTS scores in
female youth. Explanations for this inexact relationship have
included genetic vulnerability to PTSD (Gilbertson et al.
2002) and the complexity of genetic and environmental fac-
tors related to the development of mental illness in general.
The pathway from exposure potentially traumatic experiences
to PTSD symptomology is influenced by a myriad of biolog-
ical and environmental factors such that one would not expect
a strong or uniform relationship between exposure and symp-
tom manifestation or diagnosis.

That said, childhood trauma experts have also noted that the
current DSM definition of PTSD, initially developed with lim-
ited or single instances of traumatic exposure, fails to include
relevant symptoms experienced by youth exposed to maltreat-
ment and adversity over the course of development (Cloitre
et al. 2009). Thus, youth exposed to frequent and prolonged
maltreatment and adversity may not be flagged for trauma-
related mental health issues because the current definition of
PTSD is overly narrow. Given the high exposure to childhood
adversity and trauma in our sample, symptoms of Complex
PTSD (c-PTSD) (Herman 1992) –which includes disturbances
in affective and interpersonal self-regulation, such as anxious
arousal, dissociation, and aggressive or socially avoidant be-
haviors – rather than PTSD alone may better describe the psy-
chological experiences of justice-involved youth who have his-
tories of maltreatment and multiple adversities. Studies exam-
ining symptoms of exposure to trauma in juvenile offenders
(Smith et al. 2006) have posited that the traditional definition
of PTSD does not capture these additional symptoms of youth
exposed to sustained maltreatment and adversity in childhood
that may be more directly related to subsequent offending (Ford
and Blaustein 2013) than the symptoms of traditional PTSD.
Indeed, many of the behaviors associated with self-regulation
vulnerabilities (e.g., dysphoria, anger) characterize a nontrivial
subset of juvenile offenders and are captured within RNR
criminogenic need domains.

Criminogenic Needs Related to Trauma Variables

While Post-Traumatic Stress symptoms were not correlated
with youths’ total risk scores, number of maltreatment types
experienced was positively correlated with total risk, as well
as elevated criminogenic need scores in the domains of family
and personality. Number of childhood adversities was also
found to be correlated with total risk, as well as with need
scores in the domains of family, substance abuse and person-
ality. Given that maltreated children are most often exposed to
trauma in the family milieu, it is not surprising that number of
maltreatment types was significantly related to youths’ risk
scores in the family criminogenic need domain. Similarly,
many items making up the childhood adversity scale involved
experiences directly related to parental absence, illness, or

Journ Child Adol Trauma (2019) 12:351–364 359

behavior such that the relationships between this scale and the
family criminogenic need domain logically follow. However,
despite the relatedness of the family criminogenic need do-
main, childhood adversity, and maltreatment variables, they
remain distinct concepts both theoretically and statistically.
For instance, while the family domain of the YLS/CMI is
concerned primarily with parental supervision (i.e., difficulty
controlling a youth’s behavior, lack of monitoring) and gener-
al relationship quality (i.e., a ‘negative’ relationship with
mother or father), the number of maltreatment types variable
captures experiences of physical, emotional/ psychological,
and sexual abuse, as well as neglect and exposure to domestic
violence. Thus, while connections between the presence of
‘high need’ on the family domain and an elevated score on
the maltreatment variable are possible (and indeed likely in
families where maltreatment has occurred) this relationship is
not inherent to the definition of the constructs, and scores on
the family domain may also be elevated in circumstances
where no maltreatment has occurred.

The Contribution of Maltreatment to Reoffending

In contrast to much of the previous literature examining the
YLS/CMI as a risk assessment tool, the total risk score did not
predict whether youth reoffended, though it did in previous
studies with similar samples (Peterson-Badali et al. 2015;
Vieira et al. 2009; Vitopoulos et al. 2012). It is possible that
the high prevalence of maltreatment in the current sample
marks these youth as a specific sub-group of juvenile of-
fenders for whom the links between criminogenic needs and
subsequent offending are not as readily captured by the YLS/
CMI. This interpretation is consistent with studies by Onifade
et al. (2014) and Li et al. (2015), which found that while the
YLS/CMI was a strong predictor of re-offending in non-
maltreated youth, it did not predict recidivism for maltreated
juvenile offenders. Youth with maltreatment histories may
present with high YLS/CMI scores reflecting multiple areas
of need, but may not follow the typical recidivist pathways of
the broader juvenile justice population.

Maltreatment researchers have consistently reported a rela-
tionship between childhood maltreatment and justice system
involvement, along with many other adverse outcomes such
as illicit drug use and risky sexual behavior, in both adoles-
cence and into adulthood (Evants and Burton 2013; Smith
et al. 2005) but analyses have generally not included
criminogenic needs. It is critical to understand the potential
relationship of maltreatment to re-offending behavior within
the context of these well-established targets of rehabilitative
treatment. Regression analyses revealed that, of the three trau-
ma variables, only the number of maltreatment types measure
added predictive power to models that included criminogenic
risk predictors. Interestingly, among the models, maltreatment
emerged as a stronger predictor of recidivism than any one of

the individual YLS/CMI domains. In addition, this variable
predicted reoffending for male as well as female youth, sug-
gesting that experiencing maltreatment in childhood may be a
gender-neutral criminogenic need. It may be that a portion of
male offenders follow a ‘typically female’ (Daly 1994) path-
way, marked by maltreatment in childhood, and that the im-
pact of this history is particularly salient for juvenile offenders
due to their developmental and legal reliance on others for
stability, monitoring, regulation and support. These results
suggest that the potential emotional, social and mental health
impacts of maltreatment are an appropriate target for correc-
tional rehabilitative intervention, alongside the other
criminogenic risk domains, for male and female youth alike.

It is also notable that the maltreatment measure, but not the
Post-Traumatic Stress Problems scale or the childhood adver-
sity variable, predicted recidivism in the models tested. The
tenuous connections between maltreatment exposure and re-
sultant PTSD symptoms discussed previously, together with
the notion that maltreatment may be more specifically linked
to the experience of c-PTSD symptoms than the DSM defini-
tion of PTSD, raise the possibility that maltreatment contrib-
utes to risk for (re)offending insofar as it leads to self-
regulation and interpersonal difficulties. Consistent with this
interpretation were results indicating that the maltreatment
variable, and not the symptoms measure of PTSD, was signif-
icantly related to elevated needs in the Personality/ Behavior
domain of the YLS/CMI; risk in this domain includes behav-
iors reflecting deficits in self-regulation such as poor frustra-
tion tolerance, tantrums, as well as verbal and physical aggres-
sion. An important direction for future research is to directly
investigate whether childhood maltreatment is an antecedent
of these interpersonal and behavioral characteristics that are
more proximally related to offending behavior. Of particular
interest is an examination of both the impact of self-regulation
deficits and interpersonal difficulties already captured in the
YLS/CMI alongside symptoms of c-PTSD, such as pervasive
mistrust and alterations in identity, that may be both the results
of maltreatment and subsequent contributors to offending
behavior. Finally, given the sample consisted of young
people referred by the courts for comprehensive mental
health assessments, it is a possibility that while the rates of
exposure to different forms of maltreatment were similar to
findings in the general youth justice populations, the fact that
youth were referred for assessments may be a marker of the
severity of their maltreatment exposure, thus potentially
making it a more powerful predictor in this sample.

Practice Implications

This study is not the first to find connections between mal-
treatment exposure and reoffending while also finding that
PTSD symptoms did not have the same predictive effect.
Smith et al. (2005) found that it was the experiential measures

360 Journ Child Adol Trauma (2019) 12:351–364

of trauma (i.e., maltreatment and adversity) – and not the
PTSD symptom-related measures – that were the strongest
predictors of adolescent re-offending in their sample of ado-
lescent female offenders. The study’s authors emphasize that
their results highlight that justice-involved female youth who
have been exposed to maltreatment but whose clinical symp-
toms do not currently fit into existing PTSD diagnostic criteria
might also benefit from trauma treatment services focused on
coping, emotional regulation, and interpersonal effectiveness
related to their experiences of maltreatment. Our results sup-
port that this may be true for juvenile offenders regardless of
gender. For instance, while not all maltreated youth experi-
ence the flashbacks associated with PTSD, working models of
a hostile and threatening world and resultant difficulties in
regulation of arousal, anger, and interpersonal mistrust may
strongly influence their behaviors. Thus, the development and
inclusion of trauma-focused interventions aimed at identifying
and treating the impact of maltreatment on youth in the juve-
nile justice system is a worthwhile endeavour.

Given the strong relationships found between maltreatment
and criminogenic need scores, there is also merit in examining
experiences of maltreatment using the RNR framework’s
responsivity lens (Andrews et al. 2006). Indeed, the most re-
cent iteration of the YLS/CMI includes previous maltreatment
in its responsivity checklist. While we have already discussed
the need for direct interventions targeting the impacts of trau-
ma for youth with PTSD and maltreatment histories, these
results suggest that within the RNR framework, these inter-
ventions may also be a means to more effectively target well-
established criminogenic needs that are directly related to, or
exacerbated by, experiences of past maltreatment.

In our study, exposure to maltreatment and childhood adver-
sity were related to higher criminogenic need scores in several
domains.We posit that several of the criminogenic domains such
as family, personality/ behavior, attitude, and substance abuse are
characterized by the same deficits in self-regulation associated
with the symptoms of complex PTSD linked to maltreatment in
childhood. Althoughmore research into the relationship between
symptoms of c-PTSD and offending behavior is needed, trauma
treatment may be an effective primary intervention with youth
who have histories of maltreatment as well as high needs across
the criminogenic domains. Indeed, the development of enhanced
self-regulation can reduce the tendency to reflexively, rigidly,
impulsively, and overemotionally or unemotionally espouse
criminogenic attitudes, choose criminogenic circumstances, and
engage in illegal or dangerous behaviors (Ford and Blaustein
2013). Furthermore, it has been posited that current criminogenic
need-focused treatments may miss the mark by addressing an
outcome rather than a core disturbance. For instance, substance
use among trauma-impacted youth is frequently a tool for man-
aging dysregulated emotions and physiology (Kaminer et al.
2010) but substance use treatment on its own does not target
the potential underlying need for self-medication that may be

the direct result of symptoms of post-traumatic stress and previ-
ous experiences of adversity and maltreatment. Thus, trauma
treatment may address many of the underlying psychological,
physiological, or social difficulties that fuel high need levels
across criminogenic domains. As such, the symptoms of c-
PTSD that we have hypothesized to be the result of experiences
of maltreatment in childhood can be understood as important
responsivity factors that – if recognized, integrated into current
modes of treatment, and targeted by intervention – could ame-
liorate youths’ outcomes across a range of criminogenic need
domains, resulting in a reduction of recidivism.

Limitations and Future Directions

Although all analyses met requirements for adequate statisti-
cal power, the results of the current study are somewhat
constrained by a relatively small sample size that did not allow
us to explore distinct models for male and female youth con-
taining multiple predictors and effects that were found are
generally small. Further research is needed to clarify the pos-
sible interaction between gender and maltreatment.
Additionally, the measures of childhood trauma were obtained
during a baseline assessment when youth were an average of
16 years old, resulting in a reliance on historical and retrospec-
tive reports of maltreatment experiences. As such estimates of
frequency or severity of maltreatment episodes could not be
measured. Cumulative maltreatment type exposure is used as
a marker of possible complexity in the current study, but the
findings cannot speak to the differential impact of severity or
frequency of maltreatment experiences (i.e. how likelihood of
re-offending is impacted by the severity versus the diversity of
maltreatment exposure in childhood). The study was also lim-
ited in its measurement of post-traumatic stress symptoms to
the results of a screening measure with strong concurrent va-
lidity (Dongyoung et al. 2015) but with some reports of poorer
sensitivity within psychiatric populations (Ruggiero and
McLeer 2000; Sims et al. 2005). As such, future studies
should examine the relationship between post-traumatic stress
symptoms and offending through clinician diagnostic inter-
views. Furthermore, given the literature on the link between
childhood maltreatment and subsequent c-PTSD, as well as
current findings that link maltreatment and reoffending, addi-
tional work is needed to examine, more proximally and direct-
ly, the relationship between c-PTSD symptoms and offending
in justice-involved youth.


The well-documented elevated rates of maltreatment, cumu-
lative adversity, and PTSD in the juvenile justice population,
as well as the relationships between maltreatment,

Journ Child Adol Trauma (2019) 12:351–364 361

criminogenic needs, and reoffending in this study, point to the
need to integrate evidence-based, trauma-informed interven-
tions into the practices of the juvenile justice system. In order
to be effective, this should take place at multiple levels, in-
volving probation practices, mandated treatment groups, as
well as custodial programs. Education on the impact of mal-
treatment and trauma symptomology for service providers
such as probation officers and corrections staff, the implemen-
tation of screening tools upon entry to the justice system to
assist in the identification of trauma-related needs (Maschi and
Schwalbe 2012), as well as enhanced cross-system collabora-
tion (Bender 2010) between child welfare and juvenile justice
such that concurrent case planning and sharing of caseloads
across service sectors are facilitated, are important first steps
in creating a more trauma-informed juvenile justice system.

Acknowledgments We gratefully acknowledge the clinicians at the
Centre for Addiction and Mental Health’s Child, Youth and Family
Program and the cooperation of the Ministry of Children, Community
and Social Services of the Province of Ontario. The authors would like to
express their deep appreciation to Kathy Underhill, formerly of the
Program Effectiveness, Statistics and Applied Research Unit of the
Ontario Ministry of Community Safety and Correctional Services and
Mr. Justice Brian Weagant of the Ontario Court of Justice for their con-
tributions to this study. This research was supported by Grant number
410101516 from the Social Sciences and Humanities Research Council
(SSHRC) to Michele Peterson-Badali and Tracey Skilling, and a SSHRC
doctoral scholarship to Nina Vitopoulos.

Compliance with Ethical Standards

Disclosure of Interest This research is based on a portion of Nina
Vitopoulos’ PhD thesis, submitted to the Ontario Institute of Studies in
Education/University of Toronto. On behalf of all authors, the corre-
sponding author states that there is no conflict of interest.

Ethical Standards and Informed Consent All procedures followed were
in accordance with the ethical standards of the responsible committee on
human experimentation [institutional and national] and with the Helsinki
Declaration of 1975, as revised in 2000. Informed consent was obtained
from all patients for being included in the study.

Publisher’s Note Springer Nature remains neutral with regard to jurisdic-
tional claims in published maps and institutional affiliations.


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Journal of Child & Adolescent Trauma is a copyright of Springer, 2019. All Rights Reserved.

  • The Relationship Between Trauma, Recidivism Risk, and Reoffending in Male and Female Juvenile Offenders
    • Abstract
    • The Relationship Between Trauma and Reoffending in the RNR Framework
    • Trauma and (Re-) Offending in the Broader Literature
    • Method
      • Participants
      • Procedure
      • Measures and Coding
    • Results
      • Question 1: Do Boys and Girls Differ in their Post-Traumatic Stress Symptoms, Maltreatment Histories, and Cumulative Childhood Adversity?
      • Question 2: How Are Post-Traumatic Stress Symptoms, Maltreatment Histories, and Childhood Adversity Related to Youths’ Criminogenic Needs?
      • Question 3: Do Post-Traumatic Stress Symptoms, Maltreatment Histories, and Childhood Adversity Contribute to Recidivism Alongside Known Criminogenic Needs?
    • Discussion
      • Post-Traumatic Stress, Maltreatment, and Childhood Adversity
      • Criminogenic Needs Related to Trauma Variables
      • The Contribution of Maltreatment to Reoffending
      • Practice Implications
    • Limitations and Future Directions
    • Conclusion
    • References


Mental health in the criminal justice system: A
pathways approach to service and research design

Andrew Forrester1,2 | Gareth Hopkin3

1Edenfield Centre, Greater Manchester

Mental Health NHS FoundationTrust,

Manchester, UK

2Offender Health Research Network,

University of Manchester, Manchester, UK

3Department of Health Policy, London School

of Economics and Political Science, London,



Andrew Forrester, Greater Manchester

Mental Health NHS FoundationTrust,

Edenfield Centre, Bury New Road,

Manchester, M25 3BL, UK.

Email: [email protected]


Background: Care pathway approaches were introduced

into health care in the 1980s and have become standard

international practice. They are now being introduced more

specifically for health care in the criminal justice system.

Care pathway delivery has the theoretical advantage of

encouraging a whole-systems approach for health and social

care within the criminal justice system, but how well is it

supported by empirical evidence?

Aims: The aim of this study is to review the nature and

extent of evidence streams supporting health care delivery

within interagency pathway developments since 2000.

Method: We used an exploratory narrative method to

review the nature and extent of evidence streams

supporting health care delivery within interagency pathway

developments since 2000. The available literature was

reviewed using a keyword search approach with three data-

bases: PubMed, Medline, and Google Scholar.

Findings: Research in this field has covered police custody,

courts, prisons, and the wider community, but there is little

that follows the entire career through all these elements of

offender placement. Main themes in the research to date,

regardless of where the research was conducted, have been

counting the disorder or the need, development and evalua-

tion of screening tools, and evaluation of clinical interven-

tion styles. Most evidence to date is simply observational,

although the possibility of conducting randomised con-

trolled trials of interventions within parts of the criminal

justice system, especially prisons, is now well established.

Received: 1 August 2019 Accepted: 1 August 2019

DOI: 10.1002/cbm.2128

Crim Behav Ment Health. 2019;29:207–217. © 2019 John Wiley & Sons, Ltd. 207

Conclusions: Access to health care while passing through

the criminal justice system is essential because of the dis-

proportionately high rates of mental disorder among

offenders, and the concept of structured pathways to

ensure this theoretically satisfying, but as yet empirically

unsupported. Further, substantial cuts in services, generally

following government economies, are largely unresearched.

Considerable investment in new possibilities, driven by both

pressure groups and government, tend to be informed by

good will and theory rather than hard evidence and are

often not evaluated even after introduction. This must



In the health sector, the organisation of care delivery into care pathways has become a standard international

process in the period since the concept was first introduced in the mid-1980s (Vanhaecht et al., 2006). The

purpose of these care pathways is to describe how, in general, optimal care should proceed, including how,

when, and by whom assessments and interventions should be delivered for any given condition or group of

conditions. As such, they are meant to offer a mechanism by which clinical services can be both organised and

measured. They therefore also offer the theoretical potential to set up a cycle of improvement in the quality of

care (Cheah, 1998).

Although evidence supporting the use of these care pathways has been mixed, they can be effective within a

number of limited circumstances and contexts. Successes include ensuring timely assessment and interventions, pro-

moting adherence to guidelines, and supporting decision making (Allen, Gillen, & Rixson, 2009). In the years since

they were first described, however, numerous different terms have also been used to document ways in which clini-

cal care can be organised. Terms such as “care model,” “care map,” or “multi-disciplinary care” have been used to

denote a similar meaning, causing some confusion within the developing literature (Kinsman, Rotter, James, Snow, &

Willis, 2010). In recognising these terminological difficulties and to provide some clarity, the European Pathway

Association (2019) has produced a consensus definition, which states that “care pathways are a methodology for the

mutual decision making and organisation of care for well-defined groups of patients during a well-defined period.”

Reflecting this literature, it has become fashionable to describe the journeys taken by people who enter the crim-

inal justice system and who access health services within it, in terms of pathways (National Institute for Health and

Care Excellence, 2019). In part, this reflects the theoretical progress that is made as people move from one part of

the criminal justice system to another and the need to undertake assessments and make clinical decisions at a num-

ber of defined stages, such as upon entering prison. This apparent pathway starts in the community at the point of

arrest and sometimes before in cases where people are already known to health or justice services. It continues fol-

lowing entry into police custody and attendance at lower (Magistrates’) and upper (Crown) courts if required. It then

extends into prison custody for those who are sent there on remand (pre-trial) or after they receive a sentence,

before then returning to the community at the point of release from prison. In real life, of course, this theoretical

pathway structure is often messy, with arrests and rearrests, multiple court attendances at different levels, bail and

community orders providing additional stages and complexity to the pathways followed by many. Nonetheless, the

idea of a progressive pathway running through the core of the criminal justice system—a spine, as it were—is a useful


conceptual model around which to consider the delivery and development of services and the organisation of

research priorities.

This paper aims to use this underlying pathways model as a conceptual guide to explore the extent to which

there is existing mental health research at each broad landmark on the criminal justice system pathway and consider

what is now required.


We used an exploratory narrative method to review the nature and extent of evidence streams supporting health

care delivery within interagency pathway developments since 2000.

The available literature was reviewed using three databases—PubMed, Medline, and Google Scholar—and we

used a keyword approach to search for relevant material, with the following keywords:

• Pathways (or healthcare pathways or health in justice pathways),

• AND (police or police custody),

• AND (court),

• AND (prison).

We also accessed relevant government documents in England and Wales from 2009, given that a pathways

approach became national government policy in these jurisdictions following the introduction of the Corston (2007)

and Bradley (2009) reports.

As a main goal behind this paper was to identify key areas for new research in the United Kingdom, and there

are jurisdictional differences in both criminal justice and health care delivery systems, there is a bias towards

reporting U.K.-based studies.

We focused on reports and articles that considered a pathways approach to service design and delivery and

excluded non-English language publications.


3.1 | Mental health in police custody

Police contact is the first step for any potential progress through the criminal justice system. Studies of any interac-

tion between police and mental health services are confounded by the fact that the police are increasingly having to

respond in the community to distressed people with mental disorders; indeed, police powers for removing a person

from a public place to a place of safety are written into mental health legislation, at least in the United Kingdom.

Since the publication of the Corston report in 2007, the literature on health care provision for people presenting

with mental disorders in police custody has moved towards a greater focus on providing care for people likely to be

charged with a criminal offence. Published data have been in three main areas—observational studies of need among

the people who are detained, simple, but effective methods of identifying mental disorder among such people and

evaluation of health care delivery models.

Observational studies have demonstrated high rates of mental disorder and substance misuse problems among

police detainees (Forrester, Samele, Slade, Craig, & Valmaggia, 2017; McKinnon & Grubin, 2010; Payne-James et al.,

2010). Large numbers of these detainees have histories of contact with psychiatric services or present with active

psychiatric symptoms when they are clinically reviewed. These findings are broadly consistent with samples from

other parts of the criminal justice system, in the United Kingdom, and overseas (Baksheev, Thomas, & Ogloff,

2010; Dorn et al., 2014; Ogloff, Warren, Tye, Blaher, & Thomas, 2011). There is also an overrepresentation of


neurodevelopmental problems, including intellectual disabilities and attention deficit hyperactivity disorder, among

police detainees (Young, Goodwin, Sedgwick, & Gudjonsson, 2013).

There is evidence that detainees who exhibit mental health and substance misuse problems can be identified

with acceptable reliability and validity in police custody by clinical staff using research-validated screening tools

(McKinnon & Grubin, 2012; Noga, Walsh, Shaw, & Senior, 2015). The application of screening processes in opera-

tional reality has not, however, kept pace with these research findings, and in England and Wales, this area presently

requires review and improvement (Forrester, Taylor, & Valmaggia, 2016). The Health Screening of People in Police

Custody (HELP-PC) project, for example, has produced an evidence-based screen that can be simply applied; how-

ever, in most areas, appropriate screening is simply not being done (McKinnon & Grubin, 2012). Meanwhile, attempts

to help police by flagging risk may be disadvantaging the people with mental disorder who come into contact with

them (Kane, Evans, & Shokraneh, 2018).

Mental health intervention models designed for police custody suites have been evaluated, including consider-

ation of crisis intervention teams, liaison and diversion services, and street triage schemes. There is some limited evi-

dence in the literature for the effectiveness of both liaison and diversion and street triage interventions (Reveruzzi &

Pilling, 2016; Scott, McGilloway, Dempster, Browne, & Donnelly, 2013). The developing literature favours an inte-

grated multiagency approach to service delivery, but the literature remains sparse, includes no randomised controlled

trials, and does not clearly favour a particular model (Kane et al., 2018), indicating that those who are involved in

making policy and designing services should review the national approach that is currently being taken within

England and Wales.

It is now 10 years since the publication of Lord Bradley’s (2009) review of people with mental health problems or

learning disabilities in the criminal justice system in England and Wales, and mental health services and multiagency

approaches have developed substantially in this area since then. These services—known as liaison and diversion ser-

vices—have been rolled out across the criminal justice system and explicitly include the possibility of diverting people

into mental health services before formal criminal charges are made. They have continued to receive political support

and treasury backing and, as a consequence, now cover almost three quarters of the population of England,

representing considerable change since they were first started as single-site pilots in the late 1980s (NHS England,

2019; Srivastava, Forrester, Davies, & Nadkarni, 2013).

Despite this developing evidence base and successes in service provision in the early stages of the criminal jus-

tice system, there are several issues that require more focus. There is currently an increasing recognition that people

from Black, Asian, and some minority ethnic groups have much higher arrest rates than White groups, leading to a

national review and a series of recommendations (Lammy, 2017). The extent to which these are being followed is

unclear, as is the impact this could have on the mental health of criminal justice-involved individuals from these

groups. Meanwhile, although there is evidence of a reduction in recorded crime, demand upon the police has risen

OR increased – not both in a number of areas, and at the same time, there has been a drop in overall police numbers.

In particular, there appears to have been an increase in the number of incidents in which mental health is a factor

(College of Policing, 2015). There has also been a renewed focus on deaths in police custody, of which there were

14 in 2015/2016, although 60 people died by suicide after being released from police custody during that same year.

In the 10-year period between 2005 and 2015, 51% of people who died in police custody did so from natural causes,

whereas drugs and/or alcohol were involved in 49% of cases (Angiolini, 2017). It is clear that much more needs to be

done in each of these areas to ensure the delivery of appropriate services and prevent adverse and harmful


3.2 | Mental health in courts

In 2017, the Magistrates’ Courts managed 1.5 million cases in England and Wales, of which 114,000 (7.6%) were

subsequently committed to Crown Courts (upper courts) for further action, including trial and sentencing (House of

Commons, 2018). Magistrates’ Courts (lower courts) in England and Wales are now mostly served by liaison and


diversion services that often work across several parts of the criminal justice system—mainly police custody and

courts, although they also sometimes provide liaison input into prisons and probation services—offering a pathways-

type service within geographical areas (NHS England, 2019). In the main, these teams are staffed by nurses, although

other staff, including psychiatrists, social workers, and psychologists, can also be provided (Forrester et al., 2018).

The aim of such services is to provide assessment of a range of mental disorders and clinical liaison across a number

of agencies and to promote the diversion of people from the criminal justice system to safe alternatives, such as hos-

pital or community pathways, where this is appropriate. Although there is a commitment to the provision of mental

health services in the lower courts, they are not routinely provided in Crown Courts. Apart from a handful of small

schemes offering a liaison function involving local secure psychiatric services, mental health input into these courts

is mainly provided by expert witnesses.

Published literature indicates a high prevalence of mental disorder among court attendees (James, 2009; Shaw,

Creed, Price, Huxley, & Tomenson, 1999), for whom a range of special measures can be made available. These mea-

sures are meant to provide protection to vulnerable witnesses and they can include the use of screens, live links, giv-

ing evidence in private, the removal of official clothing such as wigs and gowns, using video-recorded interviews, or

the assistance of an intermediary (Crown Prosecution Service, 2019).

A range of diversion outcomes are available at various stages within the court process, and the available legisla-

tion can enable them to be tailored to the specific circumstances. For example, defendants can be diverted to hospi-

tal under civil sections of the Mental Health Act 1983 while they are unconvicted, or convicted but unsentenced, or

following a conviction, to community orders or suspended prison sentences provided under the Criminal Justice Act

2003, or mental health disposals provided under the Mental Health Act 1983. In cases where either fitness to plead

or insanity is an issue, there are three possible disposals: a hospital order, a supervision order, or absolute discharge.

One option that can be particularly effective for some offenders with mental disorder who do not require

hospitalisation is the personal tailoring of the community order or suspended prison sentence. It is possible to add a

number of specific requirements, with supervision by a probation officer, the most commonly used of the 13 possi-

bilities. Of the three community health treatment requirements, which include alcohol and drug treatment require-

ments, the mental health treatment requirement has not only been underused, but its use was falling; the court, in

essence, makes a form of contract between the probation officer, the clinician, and the prospective patient, who

must agree to the order (Scott & Moffatt, 2017). It is reassuring that the offender patients involved appear to under-

stand these orders (Manjunath, Gillham, Samele, & Taylor, 2018), but empirical research on their effectiveness is

lacking. A similar kind of community order in New York, to which patients must also give consent, has proved highly

effective in reducing rearrest and incarceration (Steadman, Redlich, Callahan, Robbins, & Vesselinov, 2011) as well as

inpatient hospitalisation, among other benefits (Swartz, Swanson, Steadman, Robbins, & Monahan, 2009). A

government-funded programme to improve availability of relevant services has proved effective in terms of both

increasing overall numbers of people being given a mental health treatment requirement and in being given com-

bined health requirements where the contribution of substance misuse has been recognised (Her Majesty’s Prison

and Probation Service, 2014). Now, there should be research evaluation of the health and criminal justice outcomes

that follow from such orders. These must include reporting outcomes for the individual offenders and also test the

impact on the ever growing prison population. As yet, notwithstanding government policy since the publication of

the Bradley (2009) report to reduce the prison population in England and Wales, this has not happened in practice

(National Audit Office, 2017).

3.3 | Mental health in prisons

To date, the main approaches to the management of mental disorders in prisons have also fallen onto three main

categories: observations on the prevalence of mental disorders among prisoners, the development of screening

tools, and evaluation of services and interventions delivered—considering both treatment offered within the prison

environment and transfer to health service facility options.


A high prevalence of mental disorder among prisoners has been reported across the world and is well established

in the international literature (Fazel & Baillargeon, 2011). Specific concerns have also been investigated, including

alcohol and substance misuse (Kissell et al., 2014; Singleton, Farrell, & Meltzer, 2003), self-harm and self-inflicted

death among prisoners (Hawton, Linsell, Adeniji, Sariaslan, & Fazel, 2014), traumatic brain injury (Williams, Cordan,

Mewse, Tonks, & Burgess, 2010), attention deficit hyperactivity disorder (Young et al., 2018), and ultra-high psycho-

sis risk (Evans et al., 2017). Meanwhile, the idea that health care screening must take place at prison reception has

gained international approval (United Nations General Assembly, 2016). A number of tools are available to screen for

mental disorders, six of which are supported by studies demonstrating replication—including the England Mental

Health Screen, the Jail Screening Assessment Tool, the Brief Jail Mental Health Screen, the Referral Decision Scale,

the Correctional Mental Health Screen for Men, and the Correctional Mental Health Screen for Women (Martin,

Colman, Simpson, & McKenzie, 2013).

The management and treatment of people in prisons in situ has been considered broadly in terms of mental state

change during imprisonment. A systematic review of longitudinal studies internationally confirmed, perhaps surpris-

ingly, a tendency towards improvement in mental state, perhaps especially during the early weeks of stay (see

Walker et al., 2014, for a systematic review). In England and Wales, it is estimated that over a third of prisoners have

diagnosable mental health conditions and mental health in-reach teams have been developed to provide care and

treatment using a model that is meant to be equivalent to the services prisoners would receive if they were resident

in the community. Although these teams are now providing care to many people who would not otherwise have

received it, variations in their coverage and content, and persisting issues with the identification of mentally ill

prisoners, have remained problematic (Forrester et al., 2013; Senior et al., 2013).

If a prisoner has a disorder of health, including mental health, of sufficient severity to require inpatient treatment,

then, in the United Kingdom, that individual must be transferred to a health service hospital (under mental health leg-

islation in the case of mental disorder). In England and Wales, there were 1,081 such transfers during the year

2016/2017. Most of these people waited much longer for hospital admission than the recommended 14-day period

(National Audit Office, 2017). Although this group only represents around 3.5% of the 31,328 in prison thought to

have mental health problems (National Audit Office, 2017), they are acutely unwell, and delays to transfer generally

prevent effective treatment delivery. Continued commitment to both management in situ and reduced delays to

transfer is needed to ensure that transfer to hospital is completed promptly for the small number of acute cases and

that high-quality mental health care is available in prison for the larger group of prisoners with mental health prob-

lems that can be managed by teams working within prisons.

Developments in prison models of care using specific screening, referral, and stratification processes have been

implemented within large prison systems and described in the literature (O’Neill et al., 2016; Pillai et al., 2016). The

organisation of these processes into a particular model—the STAIR model—is one easily understandable way of signi-

fying what should happen within the prison health care pathway and when. It includes five key elements—screening,

triage, assessment, intervention, and reintegration (STAIR) (Forrester et al., 2018; McKenna et al., 2018; Pillai et al.,

2016). Although this model is broadly in keeping with guidelines for the management of adults who are in contact

with the criminal justice system, which particularly emphasise screening processes, further research is needed to

refine existing models to ensure they are sensitive to the wide range of mental disorders seen (National Institute for

Health and Care Excellence, 2017). Service developments are also needed to ensure that resources provided on the

ground are both adequate and appropriate (Patel, Harvey, & Forrester, 2018).

3.4 | Mental health following release

Literature on the postrelease period for prisoners with mental health problems is limited, but available evidence indi-

cates that outcomes in this period are poor. Continuity of care between prison and community-based services is

difficult to provide, and prisoners often lose contact with services after release, even where management in prison is

in place (Lennox et al., 2012). Mortality after release from prison is high, with elevated risk of both drug-related


mortality (Farrell & Marsden, 2008) and suicide (Pratt, Piper, Appleby, Webb, & Shaw, 2006). It has also been

suggested that prisoners with mental illness have higher rates of reoffending than those without mental disorder and

are more likely to return to prison due to lack of support (Baillargeon, Binswanger, Penn, Williams, & Murray, 2009).

Despite these outcomes, care targeted at the transition from prison to the community is seldom present. Services

based in prison have limited ability to provide care after release, and due to the chaotic nature of release, community

services have limited ability to respond to referrals. Targeted interventions are therefore needed to attempt to

improve outcomes in this area. A number of evidence-based interventions are available, and studies suggest that

interventions focused on the transition from prison to the community can improve outcomes with prisoners with

mental health problems (Hopkin, Evans-Lacko, Forrester, Shaw, & Thornicroft, 2018). In the United Kingdom, com-

munity mental health care services are well established, and short-term bridging interventions may be sufficient to

improve care by ensuring that continuity of care between existing services is achieved. Randomised controlled trials

are, however, feasible in this area (Lennox et al., 2018); one such trial having shown that providing additional care in

4 weeks before and 6 weeks after release, and planning more proactively for transition to the community, can

improve engagement with mental health services (Shaw et al., 2014).

Further research is now needed in this area to consider what initiatives work best to facilitate engagement with

services and improve outcomes in the postrelease period. In addition, we need to understand how these services can

best ensure that continuity of care is optimised. It will also be important to understand how these services can best

work alongside probation services by testing appropriate models.

It is not possible to conclude without referring to the offender personality disorder pathway programme, a large-

scale national initiative in England and Wales that provides a range of therapeutic services for people with severe

personality disorders and associated risk to others, across the criminal justice system (National Offender Manage-

ment Service and NHS England, 2015). Key components of this model include the use of case identification and

pathways planning, psychologically informed planned environments, and case management in the community, both

following release and through probation services (Benefield, Turner, Bolger, & Bainbridge, 2017). This model is pres-

ently being evaluated, and a review of its advantages and limitations will soon be possible.


It is clear that there have been many welcome research developments in this field and that these research develop-

ments have led to an improved understanding of service design and application. It is also apparent, however, that

research has not always been done either preparatory to service development or after a service has been put in

place. There has too often been a reliance on lower quality evidence, rather than introducing higher quality studies

including quasi-experimental and randomised controlled trial work. The many service developments in the field tend

to be driven centrally by political declarations and policy statements rather than being underpinned by robust

research evidence. This would not be acceptable in most, if any, other areas of health care provision.

Moving forward, it will now be important for us to develop our understanding in a number of key areas. When,

where, and how often should screening be applied, and what tools offer the best results? What qualities in liaison

and diversion services improve the uptake of community alternatives to prison custody and have a beneficial effect

on rates of recidivism? Can integrated service delivery work across sectors and achieve its theoretical potential by

improving the accessibility to and overall quality of services? How do we best resolve serious and persisting prob-

lems in respect of clinical complexity and mortality, and what interventions should now be targeted through research

to ensure better outcomes? How can environmental effects, such as the impact of overcrowding or segregation, best

be understood, and what environmental improvements are required?

Such questions cannot be easily answered without a considered and strategic approach to research planning

within this field, recognising the difficulties in ensuring research funding in an area where the acquisition of such

funding has always been difficult. In order to take this forward, we recommend that a suitable expert group is


brought together to identify the most important research questions and develop responses. What are the research

priorities for the next decade?




The authors declare no conflict of interests.


There are no funding sources to declare.


Andrew Forrester


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How to cite this article: Forrester A, Hopkin G. Mental health in the criminal justice system: A pathways

approach to service and research design. Crim Behav Ment Health. 2019;29:207–217.



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  • Mental health in the criminal justice system: A pathways approach to service and research design
    • 2 METHODS
    • 3 FINDINGS
      • 3.1 Mental health in police custody
      • 3.2 Mental health in courts
      • 3.3 Mental health in prisons
      • 3.4 Mental health following release

Assessing the Effect of Mental Health Courts on Adult
and Juvenile Recidivism: A Meta‑Analysis

Bryanna Fox1 · Lauren N. Miley1 · Kelly E. Kortright1 · Rachelle J. Wetsman1

Received: 26 December 2020 / Accepted: 25 May 2021

© Southern Criminal Justice Association 2021

Mental health courts (MHCs) are increasingly used across the United States as a
means of reducing contact with the criminal justice system for individuals experi-
encing serious mental health conditions. MHCs rely on diversion from incarcera-
tion to rehabilitation, services, and treatment to reduce recidivism and other nega-
tive outcomes among individuals with mental health disorders. While MHCs are
a potential evidence-based remedy for the intensifying mental health and criminal
justice crises in America, there is limited research indicating the overall effects these
courts have on recidivism, and whether the effects vary across different sub-groups
or research design and analytic features. Therefore, we present a meta-analysis of 38
effect sizes collected from 30 evaluations conducted from 1997 through 2020 on the
impact of mental health courts on recidivism for adults and juveniles with mental
health issues in the United States. Weighted meta-analytic results indicate that MHC
participation corresponds to a 74% decrease in recidivism (OR = 0.26). Notably, the
strength of MHC effects are similar for adult and juvenile participants, and stable
across varied follow-up periods, study design features, and when prior criminal his-
tory, gender and race/ethnicity are controlled for in the analyses. Implications for the
criminal justice system are also discussed.

Keywords Mental health courts · Corrections · Courts · Diversion · Recidivism ·

Recent events, such as the tragic death of George Floyd in Minneapolis and the
civil unrest it spurred, have led to a renewed focus on criminal justice reform across
America. While reform efforts are vital to improving the justice system and out-
comes for people the system is meant to engage and protect, there is a concurrent
need to understand the efficacy of programs and policies proposed to address the
foremost issues in the criminal justice system today.

* Bryanna Fox
[email protected]

1 University of South Florida, Tampa, FL, USA

Published online: 14 July 2021

American Journal of Criminal Justice (2021) 46:644–664

1 3

To that end, a principal concern relates to the overrepresentation and adverse
treatment of individuals with mental health issues who encounter the police, courts,
and corrections systems. Specifically, while eight million Americans experience
severe mental illness, these individuals are involved in one out of every ten calls
for police service, they are one in every five people in U.S. jails and prisons, and
are the victims of one in every four fatal encounters with police each year (Fuller
et al., 2015). In fact, the risk of being killed by police is 16 times greater for those
with mental illness than those without mental health issues (Fuller et al., 2015), and
all but six states are housing more people with serious mental illness in correctional
facilities than in state psychiatric hospitals (Torrey et al., 2014).

Consequently, efforts to address the disparate involvement in the justice system
and improve outcomes for people with mental illness are a primary objective of
recent calls for reform. Specifically, one major reform effort involves the increased
use of mental health courts (MHCs), which are problem-solving courts designed to
divert individuals with mental health issues away from incarceration, and towards
rehabilitation and individualized treatment to address underlying mental health
needs, and reduce current and future contact with the criminal justice system.

While MHCs are a promising potential remedy to the ongoing mental health and
criminal justice crises in America, they are also costly, time consuming, and lim-
ited research has examined their cumulative efficacy for reducing recidivism, and in
particular, whether the effects generalize across different sub-groups and evaluative
research designs. Therefore, the aim of this study is to examine the effectiveness
of MHCs on future recidivism using a meta-analysis of all peer-reviewed empirical
research from 1997 to 2020. Notably, this is the first meta-analysis to include evalu-
ations of both adult and juvenile MHCs on recidivism, and examine the moderating
effects of numerous methodological design and study sample features, in order to
more accurately estimate the generality of MHC efficacy on future offending.

Mental Health Courts: Intervention vs. Incarceration

The psychiatric hospital ‘deinstitutionalization’ in the United States since the 1960s
has ultimately led to a substantial increase in the proportion of individuals with
mental health issues being detained in correctional facilities, with over half of those
in U.S. jails formally diagnosed with or showing symptoms of a mental illness in
the past year (James & Glaze, 2006; Trestman et al., 2007). While MHCs were not
specifically designed as a response to deinstitutionalization, these types of mental
health diversion programs were created as a response to the overwhelming number
of people with mental illness engaged in the criminal justice system.

Mental health diversion programs take two forms: those which occur prior to
booking, and those that take place afterwards. Pre-booking diversion programs are
often referred to as co-response models or crisis interventions, and involve mental
health providers responding to police calls (instead of or alongside police) and facil-
itating access to mental health treatment and services for individuals in need, rather
than making an arrest and/or booking the person in jail (Dewa et al., 2018). After
an arrest and booking occurs, diversion options are restricted to the court system.

645American Journal of Criminal Justice (2021) 46:644–664

23456789)1 3

MHCs represent one form of problem-solving courts, supervised by a sitting judge
within a specialized docket, where qualifying individuals are diverted to commu-
nity-based mental health treatment as an alternative to incarceration (Boothroyd
et al., 2003; Moore & Hiday, 2006). The main goal of MHCs is to reduce recidi-
vism by addressing underlying mental health concerns that are typically not met in
jails and prisons (Baillargeon et al., 2009). Individuals who participate in the MHC
diversion program receive constant support and connection to services designed
to treat, rather than exacerbate or obfuscate, their mental health issues (Moore &
Hiday, 2006; Redlich et al., 2010).

While MHCs are among the newer criminal justice reforms, with the first imple-
mented in Broward County, Florida in 1997, MHCs are now the second most com-
mon post-booking diversion program (after drug courts) in the United States (Strong
et al., 2016). In fact, according to the U.S. Department of Health and Human Ser-
vices, Substance Abuse and Mental Health Services Administration (SAMHSA),
there are currently 477 MHCs in operation around the nation, with 421 serving
adults and 56 serving juveniles with mental health disorders who become engaged
with the criminal justice system (Treatment Court Locators, 2020). MHCs are only
available to eligible individuals after an offense, arrest, and booking has taken place,
although the hope is that by engaging in treatment to address the underlying men-
tal illness, this intervention is able to better reduce the risk of recidivism, lessen
the strain on the criminal justice system, and ultimately serve as a more effective
option than adjudication in traditional courts and sentence to incarceration (Goodale
et al., 2013; Miller & Perelman, 2009). While initial evidence suggests that MHC
participants exhibited significantly lower recidivism rates and a longer time until
re-arrest compared to those who go through the traditional court and corrections
system (Moore & Hiday, 2006), the invariance in findings across participants, study
designs, and follow-up period remains unclear (Honegger, 2015). However, in order
to fully embrace MHCs as an effective evidence-based solution to one of the major
issues facing the criminal justice system to date, a rigorous and comprehensive anal-
ysis of the generality of MHC efficacy is necessary.

Mental Health Courts: Efficacy and Invariance in Effects

While MHCs have been in existence for just over 20 years, given the rapid prolifera-
tion of these diversion courts across the nation, and intense interest in their effects
by academics, practitioners, and policymakers, several studies evaluating their effi-
cacy have already been undertaken. Notably, two initial meta-analyses synthesizing
the results of these evaluative studies have been conducted (Lowder et  al., 2018;
Sarteschi et al., 2011), providing a solid foundation for future meta-analytic work to
build upon. While these meta-analyses indicate that MHCs have a small to moder-
ate effect on recidivism in the intended direction, both examined only adult MHCs,
and therefore no estimate of the mean effects of youth MHCs on recidivism has been
evaluated. Furthermore, only a small sample of studies (e.g., n = 18, Sarteschi et al.,
2011; n = 17, Lowder et al., 2018) were included in these early meta-analytic assess-
ments, limiting generalizability of these findings. Finally, a very limited assessment

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of the potential treatment heterogeneity among MHC participants and evaluative
methodological design features has taken place, leaving much unknown regarding
whom, under what conditions, and in which study designs the observed effects can
reliably be expected to occur. Nevertheless, the extant meta-analyses provide prom-
ising results for MHCs that warrant further examination of these issues.

More specifically, in the first meta-analysis on the efficacy of adult MHCs on
recidivism, Sarteschi and colleagues (Sarteschi et al., 2011) examined 18 MHC eval-
uations conducted through July 2009, and estimated a cumulative mean effect size
of Hedges’ g = -0.54. This suggests that overall, MHCs correspond to a moder-
ate and statistically significant reduction in recidivism for the adults who received
this program, compared to adjudication in the traditional court system. Examina-
tion of methodological moderators indicated that “higher quality” studies produced
a smaller mean effect (Hedges’ g = -0.52) than “lower quality” studies (Hedges’
g = -0.56). Consequently, Sarteschi and colleagues note that the limited sample size
and the lower methodological quality of included studies could potentially lead to
upward bias in the observed effect size results.

A follow-up analysis by Lowder et al. (2018) built on the work of Sarteschi et al.
(2011) by extending the inclusionary period of research on adult MHCs through
December 2015, and by conducting a broader array of methodological moderation
analyses. This meta-analysis ultimately included 17 studies comprised of 19 unique
effect sizes on the impact of adult MHCs on recidivism measured in four ways:
arrest, charge, conviction, and jailing (Lowder et al., 2018). However, results of this
study represent a notable departure from prior findings, as the authors found a much
smaller cumulative mean effect1 of adult MHCs on recidivism (d = -0.20). Moreover,
the moderation analyses revealed that MHCs had a larger effect on the risk of future
charges (d = -0.36) and jailing (d = -0.36) versus arrest (d = -0.10) or conviction
(d = -0.11). Length of follow-up period produced no variation in effects (d = -0.19
for one year and over one year follow-up periods), and “low quality” studies pro-
duced much larger effect size estimates (d = -0.35) than “high quality” evaluations
(d = -0.13). As before, Lowder and colleagues note that the limited availability of
MHC evaluations reduced confidence in the reliability of the findings, and that more
robust moderation analyses (e.g., by MHC participant features, study-level modera-
tors such as methodological controls, etc.) should be conducted to better assess the
generality of MHC effects on recidivism across a variety of contexts, study designs,
and samples.

1 Hedges’ g is roughly equivalent to Cohen’s d, as both effect sizes represent the difference in means (M)
on an outcome for the treatment and control conditions, divided by the standard deviation (SD). How-
ever, as d= M1−M2

whereas g = M1−M2

, a slight upward bias in d may occur when sample sizes are small

(i.e. n < 20) and greater variation in SD’s across samples may exist. Therefore, Hedges’ g utilizes a
pooled SD weighted by sample size, and therefore produces a slightly more precise estimate of effects for
small samples than Cohen’s d (Hedges, 1981).

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Current Study

Results of prior meta-analytic research on MHCs suggest that these diversion court
programs may lead to small to moderate reduction in recidivism among adult partic-
ipants. Given the prevalence of MHCs across the nation, and comparatively limited
knowledge on the overall effects of MHCs on re-offending and potential variance in
effects for youth versus adults and/or more sophisticated methodological designs,
further meta-analytic research is necessary to systematically evaluate MHC effects
on recidivism and potential variation across key sub-groups and evaluative study
features. Therefore, the purpose of this study is to identify and analyze all of the cur-
rently available research on the effect of adult and juvenile MHC diversion programs
on future recidivism, and assess the generality of these effects using data from a
larger and more recent sample of MHC evaluations. Thus, two primary research
questions inform this analysis. First, what is the overall weighted effect of MHC par-
ticipation on re-offending? Second, how does the effect of MHCs on recidivism vary
by participant features, evaluative study design, and analytic model specifications?
We detail the process and methods associated with our meta-analysis in the sections

Data and Methods

This study aims to build on prior meta-analytic research on MHCs by 1) expand-
ing the search period to increase and update  the sample, 2)  including both adult
and youth MHC evaluations in the analysis, and 3)  conducting more comprehen-
sive moderator analyses to assess the reliability of findings across participant fea-
tures, study design, and methodological rigor. To do this, we first aim to identify
all relevant peer-reviewed evaluations of the effects of MHCs on recidivism pub-
lished between January 1, 19972 and December 1, 2020 using a comprehensive
search of major electronic databases and the reference sections of all identified
articles. Specifically, searches of databases including the Cochrane library, JSTOR,
Google Scholar, Criminal Justice Abstracts, Social Science Abstracts, Psychologi-
cal Abstracts, and Social Work Abstracts were conducted using the keyword terms
mental health courts and recidivism, and all related synonyms and variants such as
mental health diversion, mental health problem-solving courts, mental health diver-
sion program, post-booking diversion, mental illness, offending, crime, delinquency,
violence, and evaluation in order to identify all suitable scientific publications to be
included in the analysis.

2 We used 1997 as a starting point of our search given that this is the year that the first MHC was imple-

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Inclusion Criteria

Next, a series of inclusion criteria were selected and applied to the studies identified
in the initial searches. Specifically, potentially eligible studies were maintained for
inclusion in the meta-analysis if they met the following three inclusion criteria:

1. The study must be a quantitative evaluation of the effect of an adult or juvenile
mental health court diversion program3 on subsequent recidivism.

2. The study must contain an effect size (e.g., correlation, odds ratio, d) or raw data
(e.g. treatment and control means and standard deviations) to calculate an effect
size for this relationship between MHC participation and future recidivism.

3. The study must be an original evaluative assessment published between 1997
and 2020 in a peer-reviewed outlet such as academic journals, books, and book

In total, 30 published peer-reviewed evaluations, which contained 385 unique
effect sizes on the relationship between MHC program participation and future
recidivism met each of the inclusion criteria for use in this meta-analysis. These arti-
cles are denoted using an asterisk in the References section. A PRISMA diagram
illustrating the identification, screening, eligibility, and inclusion stages is provided
in Fig. 1.

Measures and Moderators

In meta-analyses, standardized effect sizes and associated estimates of variance are
collected from all included studies and utilized to estimate the overall mean effect
size in a weighted cumulative analysis (Borenstein et al., 2009; Lipsey & Wilson,
2001). As noted above, at least one effect size estimating the relationship between
MHC participation and future recidivism was collected or calculated for each article
in the sample. While the reported effect sizes included r (bivariate correlation), B
(regression coefficient), Cohen’s d and Hedges’ g (standardized mean effects) and
odds ratios, each of these were converted and standardized to odds ratios, given
the dichotomous nature of the outcome measure, to increase comparability (see
formulas in Borenstein et al., 2009; Lipsey & Wilson, 2001). When an estimate of

5 There number of effect sizes exceeds the total studies in the sample as certain studies contained multi-
ple results, as they analyzed effects from multiple MHCs in the same publication.

3 This is operationalized as any specialized court-based diversion program for individuals with mental
health conditions where support, services, and/or treatment are provided in lieu of incarceration and tra-
ditional court adjudication.
4 In line with related meta-analytic research, we opted to include only peer-reviewed studies to increase
the validity of the results, as unpublished or non-peer-reviewed studies may not reach the accepted bar
of quality required for publication in the field, due to a lack of evaluation through the peer-review pro-
cess. Moreover, selection bias is introduced when unpublished literature is included in the sample as it is
impossible to ensure that all unpublished works are identified for inclusion.

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variance was not made available, estimated standard errors (SEs) were calculated6
so all effect sizes could be included in the meta-analysis.

Additionally, a host of items reflecting the MHC sample composition, study
design, and methodological considerations for each of the 38 effect sizes in this
analysis was coded in order to conduct comprehensive moderation analyses and test
the generality of treatment effects across various contexts. Each of these items are
described below and presented in Table 1.

Year of publication refers to the year in which the article each effect size cor-
responds to was published. The years were separated into four categories for ana-
lytical purposes: 2000–2004 (n = 2), 2005–2009 (n = 7), 2010–2014 (n = 15), and
2015–2019 (n = 17). It should be noted that since the previous  MHC meta-analy-
sis spans only through 2015, as many as 17 new effect sizes (i.e. 45% of the total

Studies identified
through database


Studies screened and
met basic inclusion


Studies excluded for
not meeting basic


Studies met full
inclusion criteria

(n= 30)

Studies excluded for
not meeting inclusion


Studies identified
through reference


30 studies with 38 unique effect sizes /
samples included in meta-analysis
















Fig. 1 PRISMA diagram of study identification, screening, eligibility, and inclusion process

6 Standard error (SE) estimates were calculated when not provided for bivariate effects using the for-
mula: 1√ N -3, and for multivariate effects, the SE is calculated using the formula: r/(b/SE) where r is
the effect size and b/SE is the ratio of the unstandardized regression coefficient to its SE (see Pratt et al.,

650 American Journal of Criminal Justice (2021) 46:644–664

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sample) have been published and were not included in any prior meta-analysis of
this type.

Participant Type refers to whether the sample an effect size was calculated from
consisted of youth (n = 4) or adult (n = 34) participants in a MHC diversion program.
As no study to date has examined the overall effect of MHCs on recidivism among
juvenile participants or how these effects may vary from the effect sizes observed

Table 1 Descriptive statistics on mental health courts and recidivism study samples

n = 38. Sample size range: 64–8,237, Treatment group size range: 31–1,084. One study did not include a
location site for the mental health court, location effect size n = 37

Frequency Percent

Participant Type Youth 4 10.5
Adults 34 89.5

Analysis Type Bivariate 15 39.5
Multivariate 23 60.5

Study Design Within Individuals (Pre-Post) 7 18.4
Between Groups (Treat-Control) 31 81.6

Causal Effects No randomization 31 81.6
Randomization, PSM, Fixed effects 7 18.4

Follow-Up Period Less than 1 year 4 10.5
1 year 22 57.9
More than 1 year 12 31.6

Control for Criminal

No 24 63.2
Yes 14 36.8

Control for Mental Health Diagnosis No 29 76.3
Yes 9 23.7

Control for Mental Health Services No 34 89.5
Yes 4 10.5

Control for Gender No 21 55.3
Yes 17 44.7

Control for Race/Ethnicity No 22 57.9
Yes 16 42.1

Statistical Significance p < .001 16 42.1
p < .01 7 18.4
p < .05 11 28.9
Not Significant 4 10.5

Location North 3 7.9
South 10 26.3
Midwest 7 18.4
West 17 44.7

Publication Year 2000–2004 2 5.3
2005–2009 7 18.4
2010–2014 15 39.5
2015–2019 14 36.8

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for adult participants, inclusion of a participant type moderator is both novel and

Location is a categorial item representing where the MHC was implemented
(north, n = 3; south, n = 10; midwest n = 7; and west, n = 17) in order to account for
potential regional differences in efficacy that may exist for courts across the United

Study Design was coded to indicate if the effect size was generated from a within
individual (i.e. pre/post, n = 7) or between groups (i.e. treatment/control group,
n = 31) study design.

Analysis Type indicates the type of analysis used to produce the effect size con-
tained in this meta-analysis, and was coded as bivariate (n = 15) or multivariate
(n = 23), where multivariate analyses are able to account for potential confounding
factors that may correlate with the participation in MHC and/or risk of recidivism.
However, in some cases bivariate models reflect the use of random assignment to
MHC treatment, and no additional controls are needed in the analyses. As such,
additional moderation analyses are undertaken to assess causal effect designs.

Causal Effects reflects whether a model from which an effect size is derived is
able to establish causal effects through random assignment or a statistical approxi-
mation of randomization (e.g. propensity score matching, fixed effects analysis), and
was coded as no randomization (n = 31), or use of randomization (n = 7).

Follow-up period is the length of time that participants’ recidivism was evalu-
ated, and coded as less than one year (n = 4), one year (n = 22), or more than one
year (n = 12) follow-up time.

Multiple measures were coded to reflect whether each effect size was calculated
after accounting for major confounders either as a control measure in a multivari-
ate analysis, as a matching criteria, or through the use of randomization. Specifi-
cally, criminal history refers to whether each participant’s prior criminal history (i.e.
before the current MHC diversion court case) was accounted for in the effect size
calculation (n = 14), as this has been shown to strongly relate to the risk of future
offending (Farrington, 1987). Mental health diagnosis indicates whether the type of
mental health diagnosis was considered when evaluating MHC effects across par-
ticipants (n = 9), given that some forms of mental illness are more strongly related to
risk of recidivism than others (Abracen et al., 2014). Mental health treatment refers
to whether prior receipt of mental health treatment and/or services was accounted
for in the model (n = 4), as this may enhance the effectiveness of MHC for partici-
pants who already have a head start on receiving treatment for their mental health
issues. Gender indicates if the study controlled for self-reported gender affiliation
of participants (n = 17), particularly given the increased rate of mental health issues
for women and criminal behavior for men (Gove, 1978; Rennison, 2009). Race/
ethnicity refers to whether the effect size was calculated with race and/or ethnic-
ity included in the analytical model (n = 16), given the differential rates of mental
illness and offending reported among people of varying races/ethnicities (Piquero,
2015; Satcher, 2001).

Finally, the statistical significance of the relationship between MHC participant
and future recidivism was evaluated to consider potential publication bias, with
p < 0.001 (n = 16), p < 0.01 (n = 7), p < 0.05 (n = 11), or not statistically significant

652 American Journal of Criminal Justice (2021) 46:644–664

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(n = 4) coded for each effect size. To that end, a funnel plot (Fig. 3) of the included
effect sizes and associated error rates was conducted to evaluate whether any biases
toward publication of higher effect sizes exist within the current studies (Light &
Pillemer, 1984; Sedgwick, 2013). Visual inspection of this plot indicates no evi-
dence of publication bias, as there is a near even distribution above and below the
mean effect size for all samples included in the analysis (Fig. 2).

Meta‑Analytic Results

Figure 3 presents the weighted effect sizes for the relationship between MHC par-
ticipation and future recidivism for each sample included in this study, and Table 2
presents the weighted meta-analytic effect sizes for the full sample of effects and at
each level of the moderating variables in the analysis. A continuous random-effects
model is used to allow estimates of the effects to vary across studies due to differ-
ences in “treatment effect” (i.e. heterogeneity in services and treatments provided

Fig. 2 Plot to test publication bias

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in each MHC program, variation in judges, etc.) and sampling error and variabil-
ity. Effect sizes, standardized to odds ratios (OR) due to the dichotomous outcome
(recidivism vs. no recdivisim) are weighted according to sample size, using the
method recommended by Rosenthal (1984) and standardly utilized in meta-analyses
(see Pratt & Cullen, 2000; Pratt et al., 2010). This weighting places a greater empha-
sis on effect sizes stemming from larger samples, which are assumed to be more rep-
resentative of the population of interest (Rosenthal, 1984; see also Pratt & Cullen,
2000). In this study, the sample sizes for each of the included effect sizes range from
64 to 8,237, and total participants in the MHC treatment groups across studies range
from 31 to 1,084.

The cumulative weighted effect size for the relationship between MHC partici-
pation and future recidivism was OR = 0.26 (95% Confidence Interval [CI]: 0.14—
0.38, n = 38). This effect size corresponds to a 74% reduction in the odds of future
offending for those who participated in MHCs. To increase comparability to prior

Fig. 3 Forest plot of effect sizes for mental health courts on recidivism

654 American Journal of Criminal Justice (2021) 46:644–664

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Table 2 Mental health courts and recidivism effect sizes by moderating factors

n = 38. OR = Odds Ratio. *p < .05

OR %Δ SE 95% CI n Q

Effect Size (OR) Full Sample 0.26 -74% 0.06 0.14 – 0.38 38 13.2*

Participant Type Youth 0.28 -72% 0.29 -0.28 – 0.84 4 0.7
Adults 0.26 -74% 0.06 0.14 – 0.38 34 12.6

Analysis Type Bivariate 0.37 -63% 0.14 0.10 – 0.64 15 2.2
10.3Multivariate 0.24 -76% 0.07 0.10 – 0.37 23

Study Design Within Individuals (Pre-

0.30 -70% 0.20 -0.08 – 0.69 7 1.0

Between Groups (Treat-

0.26 -74% 0.06 0.14 – 0.38 31 12.2

Causal Effects No randomization 0.24 -76% 0.06 0.11 – 0.37 31 9.5
Randomization, PSM, Fixed

0.44 -56% 0.18 0.10 – 0.79 7 2.5

Follow-Up Period Less than 1 year 0.29 -71% 0.16 -0.03 – 0.60 4 0.5
1 year 0.24 -76% 0.08 0.09 – 0.39 22 10.6
More than 1 year 0.31 -69% 0.12 0.06 – 0.56 12 2.0

Control for Criminal History No 0.28 -72% 0.10 0.10 – 0.47 24 4.7
8.5Yes 0.25 -75% 0.08 0.10 – 0.41 14

Control for Mental Health

No 0.25 -75% 0.07 0.11 – 0.38 29 9.5
3.5Yes 0.32 -68% 0.13 0.07 – 0.57 9

Control for Mental Health

No 0.24 -76% 0.06 0.12 – 0.37 34 9.9
2.5Yes 0.40 -60% 0.18 0.06 – 0.75 4

Control for Gender No 0.28 -72% 0.11 0.07 – 0.50 21 3.8
9.4Yes 0.25 -75% 0.07 0.11 – 0.40 17

Control for Race/Ethnicity No 0.29 -71% 0.10 0.08 – 0.50 22 3.8
9.3Yes 0.25 -75% 0.07 0.10 – 0.40 16

Statistical Significance p < .001 0.20 -80% 0.08 0.04 – 0.35 16 6.3

p < .01 0.24 -76% 0.16 -0.07 – 0.55 7
p < .05 0.36 -64% 0.14 0.09 – 0.63 11
Not Significant 0.73 -27% 0.26 0.23 – 1.23 4 0.8

Location North 0.50 -50% 0.22 0.08 – 0.93 3 0.1
South 0.23 -77% 0.11 0.02 – 0.45 10 2.8
Midwest 0.10 -90% 0.11 -0.12 – 0.31 7 1.6
West 0.39 -61% 0.11 0.18 – 0.60 17 3.8

Publication Year 2000–2004 0.35 -65% 0.19 -0.02 – 0.72 2 0.1
2005–2009 0.33 -67% 0.16 0.02 – 0.63 7 1.2
2010–2014 0.18 -82% 0.09 -0.01 – 0.36 15 4.3
2015–2019 0.33 -67% 0.11 0.11 – 0.54 14 6.0

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meta-analyses, the weighted OR effect size is converted to Cohen’s d = -0.74, which
corresponds to a moderate to strong effect across all samples in the study.

An analysis of heterogeneity was also conducted to measure the magnitude of
between-study variability in the relationship between MHC participation and rate
of recidivism using the Q statistic. A statistically significant Q suggest that there is
considerable variability between studies, typically caused by unaccounted modera-
tor variables (Hedges & Olkin, 1984). In this case, the Q statistic for the full sample
of effect sizes was statistically significant, indicating heterogeneity exists within the
included effect sizes that is not likely to have been caused by sampling error alone
(Q = 13.2, p < . 05). Consequently, additional meta-analytic analyses for each of the
moderating factors are conducted, as the strength of the effect sizes is likely to be
significantly heterogeneous across these moderators.

Analyses of the mean effect size using odds ratios (OR), for the relationship
between MHC participation and recidivism in the 38 effect sizes from 30 studies
were conducted (see Table 2). In order to evaluate variation in effect sizes and vari-
ance across moderating variables including participant type, analysis type, causal
effects, study design, follow-up period, control variables accounted for, statistical
significance of findings, study location, and publication year were also analyzed.

The meta-analytic results indicated similar mean effect sizes for the impact of
MHCs on recidivism among youth (OR = 0.28) and adult (OR = 0.26) participants,
indicating for the first time the generality of MHC efficacy across these age popula-
tions. Models that included controls (i.e. multivariate analyses) yielded greater effect
sizes (OR = 0.24) compared to bivariate analyses (OR = 0.37). However, results
showed larger effects among studies that did not utilize random assignment to the
MHC condition (OR = 0.24) versus studies that estimated causal effects through the
use of random assignment or statistical approximations of randomization (e.g., PSM,
fixed effects models) (OR = 0.44). More modest variation was found across studies
that used a between groups (i.e. treatment and control conditions) (OR = 0.26) and
within individual (pre- and post-tests) (OR = 0.30) study designs. The length of fol-
low-up produced minor variations in MHC effectiveness. Specifically, studies that
used one-year follow-ups of recidivism yielded the strongest effects (OR = 0.24),
followed by those using less than one-year follow-ups (OR = 0.29), and follow-ups
longer than one year (OR = 0.31).

Analysis of the variation in effects across models that account for relevant con-
founding features indicate that accounting for prior criminal history (OR = 0.25)
yielded nearly similar results compared to models that did not control for prior
criminal history (OR = 0.28), whereas models that that controlled for mental health
diagnoses produced a lower average effect size (OR = 0.32) than those that did not
control for mental health diagnoses (OR = 0.25). A sizable difference in effects
were found among studies that controlled for receipt of mental health treatment
(OR = 0.40), compared to those that did not control for mental health treatment
(OR = 0.24).

Among participant feature sub-group analysis, similar but slightly larger effect
sizes were seen for the association between MHC participation and recidivism when
models controlled for gender (OR = 0.25) and race/ethnicity (OR = 0.25) compared
to the models that did not (ORs = 0.28 and 0.29, respectively).

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The largest variation in effect sizes was found across levels of statistical signifi-
cance for the relationship between MHC participation and recidivism. The weakest
effects were observed for findings that were not statistically significant (OR = 0.73),
but increased as the level of statistical significance increased (OR at p < 0.05 = 0.36,
p < 0.01 = 0.24, p < 0.001 = 0.20). Finally, a variation in effect size was observed
across geographical location, with the largest effects found for MHCs in the Mid-
west region of the United States (OR = 0.10), but decreased effects were found for
MHCs in the south (OR = 0.23), west coast (OR = 0.39), and north (OR = 0.50). In
terms of year of publication, studies published between 2010–2014 (OR = 0.18) had
the strongest effect sizes followed by 2015-2019 and 2005–2009 (ORs = 0.33) and
2000–2004 (OR = 0.35). Overall, there was a modest level of variation in findings
due to differences in methodological design, including study design, causal effects,
follow-up period, and the inclusion of some control variables. Surprisingly, geo-
graphic location of the MHCs was asspcoated with variation in effects, along with
statistical significance and publication year of the MHC evaluation.


Mental health courts and related diversion programs have been created as a response
to the overwhelming number of people with mental health disorders engaged in the
“revolving door” of the criminal justice system (Baillargeon et  al., 2009). Fortu-
nately, MHCs are a promising remedy that serve to intervene and provide treatment
for individuals in need, rather than incarcerate and further entrench those with men-
tal health issues in the justice system. However, the efficacy of these diversionary
courts, and the generalizability of effects across populations, methodological study
features, and research designs has not been well examined. This study represents
the first meta-analysis to synthesize the effects of both adult and youth MHCs on
recidivism using a sample of 38 unique effect sizes from 30 peer-reviewed studies
published between 1997 and 2020 in the United States. Moreover, we present the
most comprehensive analysis of the invariance of MHC efficacy across a variety of
sub-groups and research design features to date. As such, there are three major take-
aways from this study.

First, results of this analysis indicate that mental health courts have a sizable and
significant effect on future recidivism among justice-involved people with mental
health issues. Specifically, results indicate that on average, the weighted effect size
is OR = 0.26, suggesting a 74% reduction in the odds of future offending among
those who participated in MHCs, compared to the control group or pre-test period.
These results are stronger (OR converted to d = -0.74), but generally in line with
prior meta-analytic research on MHCs (g = -0.54: Sarteschi et al., 2011; d = -0.20:
Lowder et al., 2018), providing further support for the overall efficacy of this men-
tal health diversion program. Moreover, similar mean effect sizes were found for
shorter (OR = 0.29, less than 1 year) and longer (OR = 0.31, more than 1 year) fol-
low-up periods, indicating a sustained impact of MHCs on recidivism over time, as
suggested in prior meta-analyses on MHCs (Lowder et al., 2018). These results are
particularly promising, given that this study also contains over twice as many effect

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23456789)1 3

sizes as prior meta-analyses on MHCs, is the first assessment of MHCs with juve-
nile participants, all effect sizes in this analysis stem from peer-reviewed research,
and represents a more recent and expanded search period (an additional 5 years of
research are included, from 2015–2020).

Second, as this study represents the first meta-analytic evaluation of MHC effi-
cacy among youth participants, it is notable and promising to find that the ability
for MHCs to reduce recidivism appears invariant across adult (OR = 0.26, d = -0.74)
and juvenile (OR = 0.28, d = -0.70) populations. This is noteworthy given that not all
problem-solving courts have been equally effective among youth participants. For
instance, meta-analytic research on drug courts indicates that juvenile drug courts
tend to have weaker or null effects compared to adult drug courts (Mitchell et al.,
2012). Luckily, this does not appear to be the case for MHCs. One explanation for
this variation in efficacy by age groups for other problem-solving courts is that
drug use among adolescents may be primarily peer-influenced and more difficult
for criminal justice system initiatives to intervene upon than for adults, while the
mechanisms underlying the ability to effectively treat mental health issues may be
more uniform across adult and juvenile populations. Additionally, as suggested by
Mitchell and colleagues (2012), it is possible that drug courts accept higher risk par-
ticipants (in terms of risk of recidivism) in general and have less rigorous demands
for juveniles than adults, potentially leading to the disparate effects on recidivism by
age group. Our findings, therefore, are particularly encouraging, considering the fact
that rehabilitation, treatment, and prevention efforts associated with MHCs appear to
be effective in reducing youth recidivism, but also indicate the potential for related
diversion programs (such as those pre-booking) to further prevent youth engagement
in the criminal justice system, avoid the long-term costs of justice involvement and
incarceration, and reduce other negative outcomes for those who are routinely “shuf-
fled” through the criminal justice system.

Third, these findings pose significant implications for future research, given
the observed variations in program efficacy by methodological rigor and research
design. Specifically, studies estimating causal effects of MHCs through the use of
randomized experiments or statistical approximations of randomization (e.g., PSM,
fixed effects models) yielded a weaker overall effect size (OR = 0.44) compared to
studies less effective at addressing the influence of spuriousness when estimating
MHC efficacy (OR = 0.24). This is concerning, as it implies that when more rigor-
ously evaluated, MHCs are less effective. Such a phenomenon is not uncommon in
evaluative and meta-analytic research, as non-randomized designs may overestimate
treatment effects if they are unable to account for critical confounding factors related
to receipt of the treatment and/or outcome (Lipsey & Wilson, 2001; Weisburd et al.,
2001; Wilson et al., 2000). This finding also echoes those of previous meta-analyses
on MHCs, where “higher quality” studies produced weaker mean effect sizes than
“lower quality” evaluations (Lowder et al., 2018; Sarteschi et al., 2011). That said,
while randomized experiments increase internal validity, there is a necessary trade-
off in external validity, and the ability to accurately translate findings from a sci-
entific experiment to real world settings. In other words, a mixture of field studies
and natural experiments with higher external validity and randomized experimental
designs with high internal validity are necessary to obtain the best estimates of a

658 American Journal of Criminal Justice (2021) 46:644–664

1 3

program’s true efficacy in the field. However, in light of the relatively limited num-
ber of rigorous randomized evaluations to date (n = 7) compared to the non-rand-
omized designs (n = 31), more evaluations using randomization to estimate causality
in MHC effects on recidivism are needed.

Additionally, this analysis indicates notable variations in mean effect sizes for
studies that accounted for, through the use of statistical controls or randomization,
potential confounders such as participants’ mental health diagnosis and treatment
history. This suggests that studies not accounting for the severity or type of men-
tal health disorder of participants may overestimate MHC benefits on recidivism,
in part because this factor in itself may play a substantial role in the risk of future
offending due to increased stigma and rejection from society, or suitability of MHC
recommended treatment for various types of mental health concerns (Corrigan &
Wassel, 2008; Hack et  al., 2020). Similarly, studies that do not account for prior
mental health treatment appear to overestimate the effects of MHCs on recidivism.
This may be because buy-in is a major component to mental health treatment suc-
cess, and individuals who have sought treatment in the past may have increased
buy-in and commitment, thereby increasing their chances of desisting with broader
support and treatment made available by the MHC (Barnert et al., 2020; Thompson
et al., 2020). As such, future research not undertaking randomized designs should
absolutely account for these factors as statistical controls in their models.

Notably, only minor differences in mean effect sizes were observed depending
upon whether the studies accounted for prior offending, gender, and race/ethnicity
of the participants. This finding was surprising, but as these factors are all known to
correlate with either the increased risk of experiencing mental health issues and/or
justice system involvement (Sarteschi et al., 2011), they should still be controlled for
in non-randomized designs.

Taken together, these findings indicate that MHCs are a promising strategy to
address the so-called “revolving door” of incarceration and criminal justice involve-
ment for individuals experiencing mental health disorders. In fact, those who par-
ticipated in the MHC programs, on average, experienced a 74% reduction in risk of
recidivism. This is particularly notable in light of the fact that most criminal justice
programs tend to show lower efficacy rates, and MHCs have the ability to make a
sizable impact on decarceration and treatment efforts, given the unfortunately high
prevalence of mental health issues among justice-involved people.

Study Limitations and Implications

As the first meta-analysis to examine the efficacy of juvenile MHCs, the positive
findings identified in this study are highly encouraging. While more research must
be conducted to further increase confidence in the findings and better understand the
mechanisms that underline the most (and least) successful MHC programs, a clear
implication of this work is to expand MHCs to both adult and juvenile participants.
This will help reduce justice-involvement for those who may be in greatest need
of treatment and rehabilitation, particularly for younger people that are incredibly
impacted by early involvement in the criminal justice system.

659American Journal of Criminal Justice (2021) 46:644–664

23456789)1 3

From an empirical standpoint, this study underscores the need to strive for the
highest methodological rigor and a deeper examination of why MHCs work, for
whom, and under what circumstances, in order to better understand and improve
these promising diversionary programs. For instance, while the mean effects
of MHCs on recidivism are encouraging, as they are averages, further analysis
is needed to uncover why this variation exists, and the extent to which it is a
function of methodological rigor (e.g., randomized vs. non-randomized designs,
inclusion of various control measures), participant features (e.g., different mental
health diagnoses and needs, treatment history), or program implemention (e.g.,
types and quality of services and treatment provided).

To that end, while efforts were taken to assess heterogeneity in effects due to
study quality, as is the case with all meta-analyses, findings still rely upon the
underlying studies and potentially error in effects due to unaccounted for factors
such as data quality, imprecise measurement, and shortcomings in the analyti-
cal model or study design. Efforts to address these limitations include the use of
effects drawn solely from peer-reviewed publications to reduce the likelihood of
major errors, use of a continuous random effects meta-analytic model, and con-
sideration of the influence of a multitude of methodological and study design
features on the resultant effect sizes. Although potential skew towards positive
and significant effects is a common concern and limitation in meta-analytic work,
analyses undertaken in this study suggest that there was minimal indication of
publication bias. Moreover, while analyses of the effects of MHCs for youth were
nearly identical to results from adult populations, additional research is needed to
assess the reliability of these findings given that limited (n = 4) effect sizes were
available for inclusion in this study. Increasing our assessment of juvenile MHCs
will also allow for youth-specific moderation analyses to be conducted, and
potentially isolate the most effective components for use in future youth diversion
programs around the nation.

Finally, while considerable efforts were made to identify sources of variation in
MHC effectiveness, unfortunately, data were not available to evaluate the quality and
appropriateness of services provided by MHCs for this analysis. As little research
has examined the quality of community mental health resources, case management,
and psychiatric services made available to justice-involved people participating in
MHCs (Boothroyd et al., 2005; Erickson et al., 2006; Perlin, 2003), it is critical that
these components be evaluated as a potential source of variation in effects, and/or
as models of success. Future research can help address this limitation by reporting
and analyzing specifics aspects of MHC implementation, as this is both vital to the
broader success of MHCs and may relate to heterogenic treatment effects across
subgroups of the population (Erickson et al., 2006; Perlin, 2003). This is vital for
policymakers and practitioners to expand MHCs as a diversion option, and ensure
the “active ingredients” of MHCs are enriched to improve the program’s positive
effects across a variety of participants and contexts.

In sum, findings from this study underscore for policymakers the value of imple-
menting MHCs for both adult and juvenile populations, and the concurrent need for
rigorous evaluations to unpack the facets of MHC programming and increase the
efficacy of MHCs for a broader array of participants.

660 American Journal of Criminal Justice (2021) 46:644–664

1 3


This study set out to examine the overall effectiveness of MHCs on the reduc-
tion of recidivism among justice-involved people with mental health issues, and
whether these findings generalize across participants (particularly youth) or vary
by methodological design and study features. Results from this meta-analysis,
which draw upon the most contemporary and highest quality research available to
date, suggest that MHCs correspond to a sizable reduction in risk of recidivism
among participants, and these results generalize across adult and juvenile popula-
tions, and across many methodological and study design features. While there are
many avenues for future research, this study provides considerable evidence that
MHCs are a positive and effective alternative to incarceration for the substan-
tial number of people in the justice-system who experience mental health issues,
and a viable means of diverting those in need away from the penal system and
towards treatment, services, and opportunities for a brighter future.


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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.

Bryanna Fox is an associate professor in the Department of Criminology and co-director of the Center for
Justice Research & Policy at the University of South Florida. She earned her Ph.D. from the University
of Cambridge and is a former FBI Special Agent. Her research focuses on the developmental and psycho-
logical risk factors for offending across the life-course, and the development and evaluation of evidence-
based policing and crime prevention strategies.

Lauren N. Miley is a doctoral candidate in the Department of Criminology at the University of South
Florida. She is currently the lead research supervisor  in the SPRUCE lab. Her research interests
include criminal justice policy, mental health in the criminal justice system, and developmental and
life-course criminology.

Kelly E. Kortright is a doctoral student in the Department of Criminology, and a research supervisor in the
SPRUCE lab at the University of South Florida. She earned her M.S. in Criminal Justice from the Univer-
sity of Alabama. Her research interests include developmental and life-course criminology, strain theory,
risk factors for offending, and mental health in the criminal justice system.

Rachelle J. Wetsman holds a B.A. in Psychology from the University of South Florida, and is currently
pursuing an M.A. in Criminal Justice from the John Jay College of Criminal Justice. Her research inter-
ests include crime etiology, mental health and crime, and the risk-need-responsivity framework.

664 American Journal of Criminal Justice (2021) 46:644–664

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American Journal of Criminal Justice is a copyright of Springer, 2021. All Rights Reserved.

  • Assessing the Effect of Mental Health Courts on Adult and Juvenile Recidivism: A Meta-Analysis
    • Abstract
    • Mental Health Courts: Intervention vs. Incarceration
    • Mental Health Courts: Efficacy and Invariance in Effects
    • Current Study
    • Data and Methods
      • Inclusion Criteria
      • Measures and Moderators
    • Meta-Analytic Results
    • Discussion
    • Study Limitations and Implications
    • Conclusion
    • References
    • All studies used in the meta-analyses are denoted using an asterisk

Treating the Seriously Mentally Ill in Prison: An Evaluation of a
Contingency Management Program
Travis J. Meyers, Arynn A. Infante and Kevin A. Wright

Department of Criminal Justice, Temple University, Philadelphia, Pennsylvania, USA; School of Criminology
and Criminal Justice, Arizona State University, Phoenix, Arizona USA

The management and care of inmates with mental health needs creates
immense strain for correctional administrators and staff—leaving ques-
tions surrounding the best way to treat and house those with especially
acute mental health needs. At the same time, those with mental illness
experience a number of disproportionately adverse outcomes while
incarcerated. This study evaluates a contingency management program
aimed at treating seriously mentally ill inmates housed in a maximum-
security prison. Program effectiveness was evaluated using an assess-
ment of within-individual change in mental and behavioral health out-
comes 1-year following placement. Supplemental analyses were
conducted to identify characteristics of participants most likely to experi-
ence negative program outcomes. Results from this study suggest that
the contingency management program under investigation is a promis-
ing approach to the treatment of seriously mentally ill inmates. Future
research is needed, however, to build upon these findings.

Contingency Management;
Evaluation; Mental Health;

American prisons and jails oversee a disproportionate number of individuals withmental health
needs and severe mental illness (SMI) (Munetz, Grande, & Chambers, 2001; Osher & King,
2015). Figures vary considerably, but it has been estimated that anywhere from 14% to 25% of
people in U.S. correctional facilities have a diagnosable mental illness (James & Glaze, 2006;
Morgan, Fisher, & Wolff, 2010; Skeem, Manchak, & Peterson, 2011). This is more than three
times the rate documented in the community, which is estimated at about 4% of the adult
population (Beck, 2015; Center for Behavorial Health Statistics and Quality, 2016 ). Further,
estimates suggest that there are more than three times as many individuals with SMI in prison
than receiving treatment in a psychiatric hospital (Abramsky & Fellner, 2003). It is clear that
correctional facilities have become one of the primary outlets for the treatment and control of
those with elevatedmental health needs (Adams& Ferrandino, 2008; Torrey, Kennard, Eslinger,
Lamb, & Pavle, 2010). These sobering facts have not gone unnoticed—policy makers and
practitioners have devoted significant attention toward developing practices that can improve
the response to people with mental illness who come in contact with the criminal justice system
(Griffin, Heilbrun,Mulvey, DeMatteo, & Schubert, 2015). Equally important, a number of high-
profile lawsuits, such as that brought on by inmates in a federal supermax facility in Colorado,
have drawn the attention of the general public to how prison conditions may exacerbate

CONTACT Travis J. Meyers [email protected] Department of Criminal Justice, Temple University,
Gladfelter Hall, 5th floor, 1115 Polett Walk, Philadelphia, PA 19122

2020, VOL. 5, NO. 4, 256–273

© 2018 Taylor & Francis

existing mental illness. All of this raises a critical question: “What is the best way to manage the
growing number of those with mental health needs housed in our correctional facilities?”

The purpose of the current work is to provide an evaluation of an in-prison mental health
program that serves as treatment for those who are classified as SMI. Specifically, the Arizona
Department of Corrections (ADC) developed a program (hereafter referred to as the “Saguaro
Unit”) that targets maximum-custody male inmates for placement in a separate unit reserved
for inmates with elevated mental health needs. The program aims to be a more progressive
approach toward improving the mental health of a subpopulation of inmates that are often
simply locked away from the rest of the inmate population and society. We evaluate whether
programming in the Saguaro Unit leads to positive individual-level outcomes for their SMI
participants, including outcomes related to improvements in mental health and behavioral
functioning during a one-year period. Our broader purpose is to determine whether the
program in the Saguaro Unit is effective in the treatment of SMI and whether the program can
serve as an option for correctional administrators and staff interested in implementing
programming to address the mental health needs of their inmate population.

Current issues in the management of those with mental illness

Those with SMI have long presented a challenge to correctional officials (Adams & Ferrandino,
2008). The order, safety, and security of correctional facilities—the primary goals of correctional
officials—can be disrupted by inmates who are either unwilling or unable to follow written and
unwritten rules and expectations (Mears & Castro, 2006). They can be a danger to themselves,
staff, and other inmates (Felson, Silver, & Remster, 2012). In addition, a significant amount of
time and resources can be spent simplymanaging one inmate, and the associated costs with care
and treatment of inmates with SMI can be quite expensive (Osher, D’Amora, Plotkin, Jarrett, &
Eggleston, 2012). It has been argued that managing those with SMI can, at times, exacerbate the
pervasive level of job stress and strain for correctional administrators and staff who are tasked
with managing those struggling with mental illness, especially in the absence of available
treatment programs (Dvoskin & Spiers, 2004). Correctional staff are faced with the need to
maintain the safe and orderly function of a facility while responding to the needs of those
struggling with SMI. Correctional staff, however, are often provided with minimal training in
the management and response to those with SMI, leading many to not fully understand mental
illness and its effect on institutional adjustment and behavior (Fellner, 2006).

At the same time, those with SMI experience added difficulties adjusting to prison life,
which can lead to a host of negative outcomes (Adams, 1986; Kupers, 1999). Inmates with
SMI are more likely to serve longer sentences (Ditton, 1999) and are more likely to be
victimized than their nonmentally ill counterparts (Abramsky & Fellner, 2003; Wolff,
Blitz, & Shi, 2008; James & Glaze, 2006). While in placement, those with SMI are more
likely to engage in institutional misconduct (Lovell & Jemelka, 1996; Toch & Adams,
2002), especially substance use, which commonly cooccurs with mental illness (Hartz
et al., 2014; National Institute on Drug Abuse, 2018). As a result, those with SMI generate
significantly more disciplinary infractions, which could result in additional charges, a
longer prison sentence, and placement in a more restrictive setting within the institution
(Chandler, Peters, Field, & Juliano-Bult, 2004; O’Keefe & Schnell, 2007). In fact, those with
SMI serve an average of four additional months on their sentence when compared to a
general prison population (James & Glaze, 2006). They are also more likely to be


victimized during their incarceration; inmates housed in state prison facilities are twice as
likely to be injured in a fight, for example, than those without a mental health problem
(James & Glaze, 2006). These disproportionately negative outcomes continue once the
individual is released. Those with SMI are more likely to be returned to prison, especially
for technical parole violations (Messina, Burdon, Hagopian, & Prendergast, 2004). Most
concerning, it has been argued that there remains a lack of empirical research on the
effectiveness of in-prison treatment programs for people with mental illness, especially
those using a contingency management approach to treatment (Morgan et al., 2012).

Contingency management in the correctional setting

The effective management of incarcerated adults with SMI requires correctional admin-
istrators and staff to develop a balanced approach between treatment services and institu-
tional control (Fellner, 2006). Correctional administrators use a number of different
management approaches to address the unique needs of those with SMI that are housed
in their facilities. Individuals with mental illness may be removed from the general prison
population and placed in a secure housing unit to ensure their safety and the safety of
others within the institution (see generally, Kapoor & Trestman, 2016). They may also be
treated with the use of psychotropic medication, which is one of the more common
management approaches when responding to those with mental illness (Adams &
Ferrandino, 2008). It is estimated that among those in state prisons who had been treated
with psychotropic medication in the past, roughly 69% had taken medication to treat a
mental disorder since their incarceration (Wilper et al., 2009). Those with SMI may also
be placed in treatment programs or mental health units within the institution. The specific
modalities of these treatment programs vary, however, as often these individuals are
assigned a case manager who can provide individualized services and monitor compliance
with medication (Blackburn, 2004).

One of these approaches, contingency management, involves the use of incentives and
disincentives to modify behavior (Petry, 2000). Contingency management programs were
among of the first treatment programs used in U.S. prisons and jails (Gendreau, Listwan,
Kuhns, & Exum, 2014). These programs are based on theories of operant conditioning
where positive behaviors are reinforced whereas negative or deviant behaviors are pun-
ished (see generally, Skinner, 1953). There are a number of benefits of a contingency
management that make its use suitable for correctional populations. First, contingency
management programs target observable behaviors and provide immediate punishment or
reinforcement using a high degree of structure (Gendreau & Listwan, 2018). Second, from
a theoretical perspective, it is argued that contingency management approaches are
grounded in known theories of crime and deviance, are complementary to other known
treatment modalities such as social learning and cognitive behavior theories, and are
consistent with theories of effective correctional intervention (Gendreau et al., 2014; see
Morgan & Ax, 2018; Skeem, Winter, Kennealy, Eno Louden, & Tatar, 2014). Third,
contingency management programs can be incorporated into other cognitive and beha-
vioral-based programs so that individual criminogenic needs can be better addressed
(Gendreau & Listwan, 2018; see generally, Andrews, Bonta, & Hoge, 1990). A recent
meta-analysis provides support for the potential of contingency management in improv-
ing various behaviors and institutional adjustment. Using a total of 64 effect size estimates


across 29 studies, Gendreau and colleagues (2014) found that contingency management
programs reduced problematic behaviors by 54%.

What is undeveloped, however, is contemporary research that examines the use of
contingency management programs that target a specific population of inmates with SMI.
Contingency management programs have been found to be an effective treatment for
various psychiatric disorders, however, the use of these approaches have declined in the
last several decades (Comaty, Stasio, & Advokat, 2001). In a correctional setting, the use of
contingency management allows correctional administrators and staff to target those
inmates who have previously responded negatively to traditional punitive methods, such
as segregation (Gendreau et al., 2014). Growing evidence suggests that those with mental
health needs are more likely to be placed in segregation and are more likely to experience
mental deterioration as a result of their placement in these environments (Beck, 2015;
Haney, 2003; Metzner & Fellner, 2010). In contrast to this approach, evidence suggests
that contingency management approaches are effective in not only the prison setting, but
also in psychiatric settings (see, e.g., Ellis, 1993; Glowacki, Warner, & White, 2016; Milan
& McKee, 1976; Milan, Throckmorton, McKee, & Wood, 1979). At the same time,
contingency management programs allow correctional departments to rely less on expen-
sive and punitive forms of segregation. For example, the meta-analysis mentioned above
concluded that the use of contingency management is effective in prison and psychiatric
settings, improving behavior by as much as 69% and 64%, respectively (Gendreau et al.,
2014). These results, however, are based on a relatively small number of studies. The
current study, as a result, fills an important gap in knowledge on the use of these
approaches. Critically, evidence suggests that use of contingency management programs
and policies may be increasing in correctional departments in the United States (Gendreau
& Listwan, 2018).

Data & methods

Study setting

Starting in July 2014, ADC implemented mandatory programming for all maximum-
custody male inmates designated as SMI.1 This change in policy required the removal of
inmates with elevated mental health needs from the general prison population to the
Saguaro Unit. The Saguaro Unit is designed to encourage prosocial behaviors through a
three-step contingency management approach comprising psychotherapy and psychoedu-
cational groups. Thus, the program is meant to represent a progressive approach toward
improving the mental health of a subpopulation of inmates that are often simply locked
away from the rest of the inmate population and society (Beck, 2015). The goals of the
program include the following: (1) to select inmates with elevated mental health needs
who are willing to participate in a therapeutic program; (2) to create an environment that
allows inmates with elevated treatment needs to build trust with one another and with the
staff; (3) to develop social interaction and relationship skills; (4) to promote medication
compliance; (5) to decrease and even extinguish self-harm behavior; (6) to promote the
expression of thoughts, feelings, and emotions; (7) and to develop sound critical thinking.
The design was to implement contingency management programs utilizing a three-tiered
incentive process.


To identify inmates as SMI, ADC relies on a standardized classification system in which
inmates are assigned one of five mental health scores depending on their level of treatment
needs (see Table 1). Inmates designated as a Mental Health 3A (MH-3A) or higher are
classified as “SMI.” Those classified as SMI generally demonstrate frequent suicidal
ideations, psychotic episodes, and delusions, often requiring immediate intervention—or
in this case, placement into the Saguaro Unit. Upon reception to the unit, inmates are
interviewed within one business day by the Correctional Officer III (COIII) or psychology
associate. During this initial interview process the inmate is provided a Memo of
Expectations form and a program matrix detailing the incentives available at each of the
three steps. The program is explained to the inmate including what will be required to
progress through the program.

In addition, the psychology associate will conduct a needs assessment to include a
medication review within the first three business days of the inmate’s intake into the
program. The psychology associate will conduct an interview as part of the assessment and
to begin to establish a rapport and level of trust with the inmate. The inmate will be
encouraged to be honest and to relate to the therapist the symptoms of his mental health
disorder. The therapist will chart the interview and begin to develop a treatment plan,
which may include a referral to a psychiatrist for medication assessment.

Step program
The Saguaro Unit is structured as a three-step contingency management plan in which
inmates advance through program steps, earning incentives along the way, with the end goal
being program completion and movement to a higher functioning mental health program or

Table 1. Description of mental health scores in the Arizona Department of Corrections (ADC).
Mental Health 1: Inmates who have no history of mental health issue or treatment. These inmates will not be regularly
monitored by mental health staff, but may request mental health services in accordance with the ADC protocols.

Mental Health 2: Inmates who do not currently have mental health needs, and are not currently in treatment but have
had treatment in the past. These inmates will not be regularly monitored by mental health staff, but may request
mental health services in accordance with the ADC protocols.

Mental Health 3: Inmates with Mental Health needs, who require current outpatient treatment.

● Category A: Inmates in acute distress who may require substantial intervention in order to remain stable (Example: A
floridly psychotic or delusional inmate with current or frequent suicidal ideation) All inmates classified as severely
mentally ill (i.e., inmates with a qualifying mental health diagnosis and have a severe functional impairment that is
directly related to their mental illness) in ADC and/or the community will remain in this Category.

● Category B: Inmates who may need regular intervention but are generally stable and participate with psychiatric and
psychological interventions. (Example: An inmate with a major depressive or other affective disorder who benefits
from routine contact with both psychiatry and psychology staff.)

● Category C: Inmates who need infrequent intervention and have adequate coping skills to manage their mental
illness effectively and independently. These inmates are managed only by psychiatry. (Example: An inmate with a
general mood or anxiety disorder who has learned to manage his symptoms effectively through the use of
medication and infrequent contact with mental health staff.)

● Category D: Inmates who have been recently taken off of psychotropic medications and require follow up to ensure
stability for a minimum of 6 months.

● Category E: Inmates who may benefit from infrequent interventions by mental health clinicians only and are not in
need of medications to remain stable.

Mental Health 4: Inmates who are admitted to a residential mental health program and require a more structure
program setting.

Mental Health 5: Inmates with mental health needs who are admitted to an inpatient psychiatric treatment program.


another less restrictive prison complex. To advance through and complete the program,
inmates are expected to demonstrate progress toward achieving treatment goals, including
actively participating in psychoeducational group and individual therapy sessions,2 demon-
strating trust in other inmates and staff, achieving medication compliance, desisting in the
engagement of self-harm, and thoughtfully completing self-study packets.3

Step 1. In the first step of the program, participants are expected to begin to engage in
weekly therapeutic groups conducted by a psychology associate. Participant are expected
to participate in individual counseling sessions with a psychology associate once every
30 days. They are further required to participate in weekly psychoeducational groups that
are normally conducted by psychology associates, but may also be conducted or cofaci-
litated by COIIIs. Psychoeducational groups may also be cofacilitated by sergeants and
correctional officer IIs assigned as cadre staff to the programs. The inmate will also be
assigned self-study material that is cognitive behavioral in design and empirically based. In
addition, participants are expected to abide by all rules and regulations and to demon-
strate respect and courtesy to others. Participants are also expected to be compliant with
their prescribed medication.

There are a number of incentives for following the above-stated requirements.
Participants are allowed to participate in individual outdoor recreation 3 days per week
for 2 hours. Participants are also allowed one, 2-hour noncontact visit and one 15-minute
phone call per week. SMI inmates are allowed out of their cell 10 hours per week for
unstructured activity. Advancement from Step 1 to Step 2 must be approved by the
treatment team. The inmate must have satisfied the minimum time requirement of
30 days. Criteria for advancement out of this step includes rule compliance, respect for
others, active participation and involvement in programming activities, medication com-
pliance, and remain free of self-harm and mental health watch for at least 30 days.

Step 2. Upon approval by the treatment team, participants then advance to the inter-
mediate step of the program. As in Step 1, participants are expected to continue to actively
participate in weekly therapeutic groups conducted by the psychology associate. Further,
participants are expected to begin to open up and emotionally engage the group and the
therapist. They must also actively participate in individual counseling sessions once per
month with the psychology associate. Participants in Step 2 are expected to continue to
remain compliant with rules and to begin to encourage others to be compliant as well.
They must continue to demonstrate respect and courtesy to others and are expected to be
compliant with any prescribed medication(s).

There are a number of incentives available to participants in Step 2 of the treatment
program. Participants are allowed one, 2-hour recreation period in a group recreation
enclosure. In addition, participants are allowed two recreation periods in an individual
recreation enclosure per week. Further, participants can receive one, 2-hour contact visit
per month and can make one 15-minute phone call per week. The SMI participants are
also allowed out of their cell for 10 hours a week for unstructured activity. Participants are
also allowed to participate in Work Incentive Pay Programs (WIPP) at this step and can
attend organized, group religious services.

Advancement from Step 2 to Step 3 must be approved by the treatment team. This step
is considered a significant reflection of the inmate’s level of progress in his treatment.


Inmates must meet the time requirement of 60 days in the program. They must also be
compliant with rules and regulations and to encourage others to do so. To advance, a
participant must be actively participating in therapy and demonstrating to members of the
program team that he is making noticeable positive changes toward his treatment goals. If
individuals are participating in WIPP, they must demonstrate a positive work ethic and
receive above average work evaluations. Further, to advance to Step 3, the participant must
be absolutely compliant with medication and may not have any recent incidents of self-
harm or placements on mental health watch.

Step 3. In the final step of the program, participants are expected to not only actively
participate in groups conducted by the psychology associate on a consistent basis, but to
also open up emotionally and share thoughts and feelings with the group in a therapeutic
manner. The participant is further required to show progress in developing trust and
sharing his emotions during the monthly counseling sessions with the psychology associ-
ate. Overall, the participant is expected to demonstrate to the therapists and program staff
that he is progressing significantly in his treatment plan. Participants in this step are
expected to be able to serve as a role model for the other inmates in the program. They
should not only encourage others to abide by rules and actively participate in program
activities, but to also show ability to begin to mentor other inmates who may be struggling
in the program. Participants in this step must remain compliant with medication and, if
the acuity of his mental health disorder allows, he should begin to show an ability to
utilize the understanding and skills that he has gained from the program to begin to lessen
his medication dosage. Clearly, there are some inmates who will not be able to reduce
their dosage without suffering negative consequences; therefore, it is understood that a
reduction in medication dosages based on actualization of skills learned may not be
possible for a number of programming inmates.

The final step of the program involves a number of incentives. They are allowed 3 days
of recreation in a group enclosure and are allowed to participate in WIPP and hobby craft
if they choose. Participants who advance to this step are allowed one contact visit every
weekend. Participants are allowed to go to the commissary in a controlled group move-
ment and can receive commissary weekly. They are also allowed to participate in orga-
nized, group religious activities; participate in fundraising activities; and participate in
Alcoholic Anonymous meetings. SMI inmates in Steps 2 and 3 are allowed out of their cell
10 hours per week for unstructured activities.

Participants who remain at Step 3 for 60 days are eligible to be utilized as a formal
mentor in the mentor pod in the unit. The selection of these formal inmate mentors is a
treatment team decision. All participants who have been at Step 3 for 30 days or more are
expected to be informally mentoring inmates who need it at the lower levels. The idea of
assisting others to understand and be successful is an integral part of both mental health
programs. Mentoring is recognized as a form of caring and reaching out to others in a
helpful manner and is evidence that the inmate has transcended his circle of self.

Program completion
Participants who meet and sustain their treatment goals during Step 3 of the program for
90 days are eligible for graduation and movement. An eligible inmate at the Saguaro Unit
may be transferred to a unit with higher functioning inmates. These transfer decisions are


made by the treatment team. Some inmates may remain in the program for months or
even years based on their acuity level. The treatment team develops and decides on
individual goals designed to stimulate and sustain the gains in treatment over the long

Program staff
The cadre of day and swing shift correctional officers assigned to the Saguaro Unit are
selected by their respective shift commanders based on their skills, experience, and will-
ingness to work in such a program. The COIIIs assigned to the program are selected by
the program supervisor (Correctional OfficerIV) who oversees the program based on their
commitment level to programming, skills with group and individual counseling, and
desire to be part of this program. The sergeants assigned are selected based on their
interactive skills, patience, understanding of the symptoms of this type of population, and
commitment to a team concept. All of these staff are highly encouraged to frequently
interact with the inmates in the program and to encourage and support their participation.

The psychology associates from a third-party health provider that are assigned to the
program are required to have a commitment to the treatment of the individual inmate
participants and to the overall therapeutic success of the programs. The psychology
technicians who are assigned to the Saguaro Unit must be fully engaged in the holistic
nature of the treatment to include recreation, art work, journaling, and skill development.
The third-party staff must fully understand, appreciate, and participate as a team with
operations staff for the success of the programs.

Treatment team
The treatment team for the Saguaro Unit consists of the deputy warden, associate deputy
warden, program supervisor, case managers, programs sergeants, psychology associates,
psychology technicians, and cadre-line officers. The chaplain may also participate. The
team meets once per week to discuss individual inmate cases, advancements, reversions,
removals, therapeutic and/or innovative program strategies, and operational issues. The
decisions of the team regarding inmate advancements, reversions, and removals are final.
Minutes of these meetings shall be taken and once approved and signed shall be dis-
tributed to team members.


Participants included 58 male inmates who met criteria for placement. In consultation
with ADC, it was decided that all program participants as of July 23, 2015, with program
start dates prior to January 29, 2015, would constitute the sample used in the study.
Additionally, only inmates who had been in the program for at least one year were
included. This permitted a comparison of within-person outcomes one-year prior to
program placement with outcomes one-year after program start. Preprogram measures
were included at 6 and 12 months prior to entrance into the program and compared with
outcomes 6 and 12 months after entrance in the program.

As illustrated in Table 2, the sample is diverse. Participants were, on average, age
37.57 years, and were Mexican American (34%), White (29%) and African American
(24%). The sample also consisted of a small number of Mexican Nationals (4%) and


Native Americans (4%). The majority of participants had completed mandatory literacy
(62%), whereas less than one half had earned a General Educational Diploma (GED)
(40%). On average, the mental health score at program start was around 3.17.4

Outcome measures
The critical outcome variables of interest focus on misconduct and the mental health
status of inmates before and after program start. Specific variables of interest were
identified 12 (Time 1) and 6 months (Time 2) prior to placement, and 6 (Time 3) and
12 months (Time 4) after program start.

The primary data source for the evaluation was the Adult Inmate Management
System (AIMS) database. AIMS contains information on inmate demographics, incar-
ceration history, movements, and current programming information. Importantly, it
also contains significant information related to an inmate’s mental health history. Thus,
we were able to include several measures essential to assessing mental health program
effectiveness, including inmate mental health score (see Table 1) and number of mental
health watches. When inmates are identified as being at significant risk of suicide or
self-harm, they are placed in individual watch cells for observation, which ADC refers
to as “mental health watches.” We also included a measure to capture the number of
protective custody requests. Those with SMI often have a significant number of requests
for placement in protective custody. The reduction in the number of protective custody
requests is a critical outcome that ADC targets during programming. Protective cus-
tody requests indicate an inability or unwillingness to house in the general prison
population (Gendreau, Tellier, & Wormith, 1985). This creates an immense strain on
correctional administrators and facilities that are faced with overcrowding and the
added commitment of provide housing to those who are unable to live in the general
prison population.

AIMS also allows for qualitative notes to be provided by ADC staff regarding an
inmate’s overall progress in programming. This includes specific details related to viola-
tions and any difficulties experienced throughout programming such as refusal to com-
plete required educational packets. Thus, to evaluate inmate cooperation with the
program, we included several program-specific measures. The number of step reductions

Table 2. Sample characteristics (N = 58).
Variables n %

Agea Range: 19–74 (M; SD) 37.57; 11.77
White 17 29.31
African American 14 24.14
Mexican American 20 34.48
Mexican National 2 3.45
Native American 2 3.45
Other 3 5.17

GED 0 = no; 1 = yes 23 39.66
Mandatory literacy 0 = no; 1 = yes 36 62.07
MH Scoreb Range: 1–4 (M; SD) 3.17; 0.50

Note. GED = General Educational Development; MH = Mental Health.
a Age was calculated at the start of data collection.
b MH Score at program start.


(due to misbehavior), program refusals (i.e., refusal to participate in programming), and
number of visits were all documented.

Lastly, AIMS provides a comprehensive list of violations acquired over the entire length
of the inmate’s prison stay. To assess general compliance with ADC rules before and
during placement in the Saguaro Unit, we included a range of individual-level behavioral
measures to capture the severity of disciplinary misconduct, including number of major
violations (e.g., drug possession/manufacturing, promoting prison contraband, possession
of a weapon), number of minor violations (e.g., failure to maintain grooming require-
ments, being out of place, littering, horse playing, smoking, or use of tobacco in an
unauthorized area), number of drug violations, number of assaults on staff, and number
of assaults on inmates (i.e., violations for fighting with other inmates, aggravated assault
on another inmate, and/or rioting).

Analytic strategy

The analyses proceed in two stages. First, we assess whether program participants demon-
strated improvement in behavior one year after program start relative to one year prior.
One-group designs are common in program evaluations, especially in instances where
random assignment is not possible (Posavac, 2011). Given this, we use paired-sample t
tests to determine whether significant differences exist between pre- and post-program
behavior. Paired-sample t tests can be used when outcome measures are measured
continuously and when using repeated measures among the same sample of participants
across two time points (Posavac, 2011). Second, we conduct supplemental analyses to
examine possible relationships between inmate characteristics and program outcomes
using cross-tabulations and independent samples t tests. As shown below, cross-tabula-
tions and independent samples t tests were used to compare two separate samples.
Specifically, we examine the inmate characteristics associated with two negative program
outcomes: requiring at least one mental health watch, and generating two or more
program refusals in the one-year period following program start.

Table 3. Comparison of key outcomes pre- and postprogramming (N = 58).
Time 1 (M; SD) Time 4 (M; SD)

MH Scorea * 3.18; 0.66 3.63; 0.65
MH watch * 1.93; 4.17 0.66; 1.42
Protective custody requests * 0.66; 1.74 0.16; 0.72
Minor violations * 0.54; 0.84 1.04; 1.21
Major violations 1.85; 1.95 1.43; 2.50
Drug violations 0.17; 0.38 0.07; 0.26
Inmate assaults 0.22; 0.56 0.26; 0.66
Staff assaults 0.17; 0.63 0.14; 0.61
Visits 1.32; 3.02 2.81; 7.40

Note. MH = mental health.
Differences across Time 1 and Time 4 were tested using paired t tests. Time 1 is one year prior to
program start whereas Time 4 is one year after program start.

a One inmate did not have a Time 1 MH Score. Thus, the test of differences across Time 1 and Time
4 for MH Scores only reflect inmates with valid scores (n = 57).

*p ≤ .05



Main analyses

Table 3 demonstrates significant differences across several critical outcomes, including
protective custody requests and mental health watches. The data suggest that protective
custody requests (M = .16, p < .05) and mental health watches (M = .66, p < .05)
decreased, on average, from the year prior to the program start to the year following
program start. Despite this decrease, the findings also suggest that mental health scores
slightly increased after program start (M = 3.63, p < .05). This increase is not surprising
given the nature of the program. Inmates are assessed more frequently during program-
ming, and thus mental health issues present prior to program start are made more readily
apparent during programming (manifesting as an increase in mental health scores). Still,
this increase could also indicate the alternative, that overall mental health is getting worse
during programming; however, given the significant decrease in mental health watches—a
significant indicator of mental health functioning—it is important to interpret this finding
in context. With regard to violations, we see that minor violations increased, on average,
from Time 1 (M = .54) to Time 4 (M = 1.04, p < .05).

Examining this finding further, once changes in violations are assessed at mutually
exclusive time points5 (i.e., first and last 6 months preceding program start and the first
and last 6 months after program start) it becomes clear that participants generate high
rates of minor violations in the year leading up to and within the first 6 months of
program start, and begin to slowly taper thereafter. Thus, though the findings show a
significant increase in minor violations from one year preceding to one year following
program start, this difference is calculated without regard to changes occurring within
those one-year time frames.

Supplemental analyses

Additional analyses were conducted to contextualize our main findings. Specifically, these
analyses focus on significant differences among key individual-level differences between
participants—including those with mental health watches, two or more major violations,
and two or more program refusals in the one-year period following program start. These
are inmates who are arguably the least successful in the Saguaro Unit as they continue to
generate new mental health watches and major violations, even after programming has
commenced. If we can identify characteristics and behaviors of inmates who are not
performing well in ADC’s mental health program, then perhaps we can better predict
which inmates are most likely to be problematic during the program as well as those who
pose a risk for mental and behavioral health issues in the future.

Table 4 demonstrates the significant pre-program characteristics of participants who
had at least one mental health watch in the year following program start (n = 16) relative
to participants who did not have any mental health watches during this same period
(n = 42). On average, the findings show that participants with at least one mental health
watch had higher mental health scores (p < .05) and mental health watches (p < .05) at six-
months and one year prior to program start than participants with no mental health
watches. In other words, inmates who continued to go on mental health watch during the


first year of programming typically had worse mental health scores, were on mental health
watches more frequently, and had more minor violations in the year preceding program
start. Inmates with at least one mental health watch during the first year of programming
also had significantly higher assaults on inmates (M = .56, p < .05) and staff (M = .44,
p < .05) during this same period. They averaged more than three times as many major
violations (M = 1.63, p < .05) within 6 months of programming compared to inmates who
did not go on mental health watch in the year following program start.

Table 5 shows the significant pre- and post-program start characteristics of participants
whom had two or more program refusals during the first year of programming (n = 18)
relative to participants who had no more than one program refusal. The findings show
significant demographic differences in these two groups. Specifically, participants with two
or more program refusals were more likely to be White (61% vs. 15%, p < .05) relative to
participants with no more than one program refusal. Moreover, only 33% of the two or
more refusals group had completed mandatory literacy as compared to more than 75% of
participants who had one or fewer program refusals (p < .05). Thus, inmates who were
more compliant with program expectations tended to have more advanced educational

Table 4. Comparison of participants with MH watches relative to
participants with no MH watches in Time 4 (n = 58).

Mental Health Watch

Variablesa No (%) Yes (%)

General demographics
Ageb (M; SD) 38.10; 12.48 36.19; 9.89
White 28.5 31.2
African American 26.1 18.8
Mexican American 31.0 43.7
Mexican National 4.8 0.0
Native American 4.8 0.0
Other 4.8 6.3
GED 42.9 31.3
Mandatory literacy 61.9 62.5

Time 1
MH Scorec (M; SD)* 3.05; 0.59 3.50; 0.73
MH watch (M; SD)* 1.07; 2.28 4.19; 6.67

Time 2
MH Score (M; SD)* 3.17; 0.62 3.63; 0.81
MH watch (M; SD)* 0.79; 1.75 2.31; 4.18
Minor violations (M; SD)* 0.29; 0.46 0.75; 1.24

Time 3
Major violations (M; SD)* 0.41; 0.86 1.63; 2.87
Inmate assaults (M; SD)* 0.02; 0.15 0.19; 0.40
Staff assaults (M; SD)* 0.0; 0.0 0.38; 0.89

Time 4
Major violations (M; SD)* 0.93; 1.28 2.75; 4.09
Inmate assaults (M; SD)* 0.14; 0.35 0.56; 1.09
Staff assaults (M; SD)* 0.02; 0.15 0.44; 1.09

Total 42 16

Note. GED = General Educational Development;; MH = mental health.
Differences across program refusal groups were tested using a chi-square for
categorical indicators, and t-tests for continuous indicators.

a Table only reports significant measures from Time 1 through Time 4.
b Age calculated at start of data collection.
c One inmate did not have a Time 1 MH Score. Thus, the test of MH Score differences
in Time 1 across MH Watch groups only reflect inmates with valid scores (n = 57).

*p ≤ .05


histories, tended to be younger, and were more likely to be classified as a racial/ethnic
minority relative to inmates who were noncompliant.

With regard to key program outcomes, the findings illustrate significant differences in
the frequency of staff assaults and visits during the 6 months and one year leading up to
program start across the program refusal groups. Specifically, participants with two or
more program refusals during the first year of programming received fewer visits on
average than their counterparts 12 and 6 months prior to program start and had a higher
volume of staff assaults 6 months prior to program start. Once the program started,
participants with two or more program refusals had a significantly higher volume of
minor violations at both time points. Taken together, inmates who frequently refused to
participate in program activities were less likely to have visits in the both the year prior to
program start (M = .06, p < .05), and the 6 months prior to program start (M = .0, p < .05).
They also had more staff assault violations in the 6 months prior to program start
(M = .17, p < .05). Once in the program, these inmates were also more likely to continue
to generate minor violations. Specifically, in the year following program start, participants
with two or more program refusals averaged 0.71 more minor violations than participants
with no more than one program refusal during the first 6 months of the program and 0.67
more minor violations during the first year of the program.

Table 5. Comparison of participants with two or more program
refusals relative to participants with no more than one program
refusal (n = 58).

Two or More Program Refusals

Variablesa No (%) Yes (%)

General demographics
White * 15.0 61.1
African American 30.0 11.1
Mexican American* 45.0 11.1
Mexican National 5.0 0.0
Native American 5.0 0.0
Other* 0.0 16.7
Mandatory literacy* 75.0 33.3

Time 1
Visits (M; SD)* 1.90; 3.49 0.06; 0.24

Time 2
Staff assaults (M; SD)* 0.03; 0.16 0.17; 0.38
Visits (M; SD)* 1.20; 2.31 0.0; 0.0

Time 3
Minor violations (M; SD)* 0.35; 0.62 1.06; 1.31

Time 4
Minor violations (M; SD)* 0.83; 0.98 1.50; 1.54

Total 40 18

Note. Differences across program refusal groups were tested using a chi-
squared for categorical indicators and t tests for continuous indicators.

a Table only reports significant measures from Time 1 through Time 4.
b Age calculated at start of data collection.
*p ≤ .05.



U.S. prisons oversee an overwhelming number of individuals with SMI, which presents a
significant challenge to correctional administrators and staff who are often untrained and
unprepared to respond to their attitudes and behaviors (Dvoskin & Spiers, 2004). At the
same time, those with SMI often struggle adjusting to prison life leading to a number of
adverse outcomes. Stacked against this reality, prison officials are tasked with the main-
tenance of safe and secure facilities while providing adequate physical and mental health
care. Outcome evaluations of in-prison treatment programs, however, are relatively scarce
in the literature. The purpose of the current work was to determine whether a contingency
management program designed to house and treat those with SMI affected the future
behavioral and mental health of inmates. Our work here leads to several broad

Although mental health treatment has been found to be more effective when delivered in
the community (Martin, Dorken, Wamboldt, & Wootten, 2012), Saguaro Unit’s contingency
management approach produced a number of positive individual-level outcomes. Specifically,
the number of mental health watches, protective custody requests, and drug violations were
lower in the first year of programming compared to the year before placement. Given the high
occurrence of misconduct, specifically substance use violations, and the seriousness of being
placed on mental health watches, these results are encouraging. Participants also received, on
average, more visits in the first year of programming. Critically, social support, in the form of
visits, has been found to lead to a number of positive outcomes including increased percep-
tions of support upon reentry—a critical element for successful reentry outcomes (Meyers,
Wright, Young, & Tasca, 2017). This is especially important for those struggling with elevated
mental health needs (Wallace et al., 2016).

Despite this, the program still encountered issues, including inmates not meeting criteria
for step advancement due to placement on mental health watch or by flat out refusing to
participate in program activities. Although mental health watches were significantly reduced
as a whole, within one year of program start 28% of the sample had experienced a mental
health watch. Those who went on mental health watch during this time had a more
significant history of watches and minor violations leading up to program start, as well as
more assaultive behavior during the first year of programming compared to inmates who
did not go on watch. These findings underscore the need to separate lower functioning or
troublesome inmates from higher functioning inmates; this may allow correctional admin-
istrators and staff to divert their limited resources to those who need it most (e.g.,
Lowenkamp & Latessa, 2005). This is especially critical given the large amount of resources
and training that is required to effectively implement and maintain programs that use a
contingency management approach (Gendreau & Listwan, 2018).

Too often we think of SMI in a vacuum when it comes to the treatment and evaluation of
in-prison programs without adequate consideration of the role of co-occurring risks and
needs. Within one year after program start, for example, 62% of the sample had at least one
instance of refusing to program. On average, they were less likely to have earned a GED or
achieved mandatory literacy, were less likely to be visited in the year leading up to program
start, and averaged more placements on mental health watches. Once in the program, those
with co-occurring issues averaged more incidents of minor violations and placements on
mental health watch than inmates not refusing to program. Broadly speaking, individual


characteristics of inmates and their co-occurring needs and risks may affect whether
placement in the Saguaro Unit leads to null, negative, or positive future outcomes. This
suggests that an individual’s risk and needs are important and a more comprehensive
program aimed at SMI that includes these areas is likely to be more effective (Morgan &
Ax, 2018). This is critical in the implementation of contingency management programs and
policies where punishment and rewards in response to behavioral patterns need to be
individualized to the greatest extent possible (Gendreau & Listwan, 2018).

Overall, the Saguaro Unit presents a promising first step on the long road to under-
standing effective approaches to the treatment of inmates with serious mental health
needs. It is important to note, however, that this research is limited in a number of
ways. Most importantly, we have no comparison group or counterfactual to isolate the
true effect of the program. We believe, however, that the major threats to validity of our
one-group pretest–posttest design are limited (Cook & Campbell, 1979). For example,
history effects, such as a change in policy in how mental health scores are defined, could
be responsible for the observed outcomes. To be sure, no major change in ADC’s mental
health policy occurred during the study period. In addition, the use of a one-group
pretest–posttest design must account for maturation as inmates may simply be growing
too old to engage in misconduct. To that end, should maturation be a factor, we also could
not rule out that it was the program itself that contributed to the reductions in proble-
matic behavior discussed above.

The research team was also limited in terms of data available to assess program
outcomes. A qualitative analysis of the program, for example, would allow for a better
understanding of the existing challenges in the program, as well as a more targeted
evaluation of whether inmate functioning is actually improving. Finally, the program is
restricted to one facility; therefore, inferences cannot be made as to the systemic improve-
ments in mental health programming occurring across all ADC facilities or to facilities in
other states. Balanced against these limitations, we have access to information on a
historically difficult population to reach, and we have reason to believe that the program
did little further harm for a population that will continue to be a challenge for correctional
practitioners. It is our hope that this work may serve as a step toward a longer line of
research examining inmates with SMI in correctional facilities.

1. Information in this section was obtain from ADC’s Mental Health Program Manual (Arizona

Department of Corrections, 2014).
2. The six group counseling programs offered to inmates (Social Values, Self-Control, Responsible

Thinking/Healthy Personality, Substance Abuse, Core Skills, and Feelings) are cognitive and
evidence-based programs that are a product of The Change Companies.

3. The self-study packets are selected for each individual inmate by the mental health staff based
on their individual needs. Self-study packets include Making Decisions, Values and Personal
Responsibility, Refusal Skills, Attitudes and Beliefs (Hazelden Publishing), and Anger
Management (Substance Abuse and Mental Health Services Administration). The program
staff also offered ETV (educational television) modules on a needs-basis, including Conflict
Resolution, Resources for Change, Living a Better Way, Commitment to Change (FMS
Productions), Domestic Violence (Altschul Education Group), Victim Awareness (Greystone
Educational Program), and Substance Abuse (Hazelden Publishing).


4. In July 2014, the month that the Saguaro Unit was implemented, the overall ADC inmate
population was primarily Hispanic (40.4%), followed by Caucasian (39.4%), African American
(13.2%), and Native Americans (5.2%) (Ryan, 2014).

5. Time 2 was subtracted from the Time 1 average to capture outcomes for the first and last six
months leading up to the program start date. The same was done for Time 3 and 4 averages to
capture the first and last six months after program start. Thus, for the purpose of illustrating
changes in minor violations across time, the time periods in Table 4 represent mutually
exclusive, six-month snapshots of participant outcomes over a two-year period.


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  • Abstract
  • Current issues in the management of those with mental illness
  • Contingency management in the correctional setting
  • Data & methods
    • Study setting
      • Intake
      • Step program
      • Program completion
      • Program staff
      • Treatment team
    • Sample
      • Outcome measures
    • Analytic strategy
  • Results
    • Main analyses
    • Supplemental analyses
  • Discussion
  • Notes
  • References

The Behavioral Health Needs of First-Time Offending Justice-Involved Youth:
Substance Use, Sexual Risk, and Mental Health

Marina Tolou-Shamsa,b, Larry K. Brownc,d, Brandon D. L. Marshalle , Emily Dauriaa,b,
Daphne Koinis-Mitchellc,d, Kathleen Kempc,d and Brittney Poindexterc

aUniversity of California, San Francisco, San Francisco, CA, USA & Weill Institute for Neurosciences; bZuckerberg San Francisco General
Hospital, San Francisco, CA, USA & Weill Institute for Neurosciences; cWarren Alpert Medical School of Brown University, Providence,
RI, USA; dRhode Island Hospital, Providence, RI, USA; eBrown University School of Public Health, Providence, RI, USA

This study examines substance use, emotional/behavioral symptoms, and sexual risk among
first-time offending, court-involved, non-incarcerated (FTO-CINI) youth. Youth and caregivers
(n¼ 423) completed tablet-based assessments. By the time of first justice contact (average
14.5-years-old), 49% used substances, 40% were sexually active and 33% reported both.
Youth with co-occurring substance use and sexual risk had more emotional/behavioral
symptoms; youth with delinquent offenses and females had greater co-occurring risk. Time
of first offense is a critical period to intervene upon high rates of mental health need for
those with co-occurring substance use and sexual risk to prevent poor health and
legal outcomes.

adolescent; HIV/STIs;
juvenile justice; mental
health; sexual risk;
substance use


Estimates indicate that over 2 million youth
under the age of 18 are arrested annually
(Puzzanchera, 2009) and 31 million are under
juvenile court jurisdiction (Puzzanchera, 2011).
Involvement in the juvenile justice system (JJS) is
associated with a variety of adverse health out-
comes, such as substance use (Dembo et al.,
2007), psychiatric symptoms (Teplin et al., 2002),
sexual risk behavior (Elkington et al., 2008;
Teplin et al., 2003) and sexually transmitted
infections (STIs) (Belenko et al., 2008). Most past
research has focused on the high-risk subsample
of incarcerated juvenile offenders but little is
known about the nearly 80% (Furdella &
Puzzanchera, 2015) of non-detained youth.
Examining the rates of drug use, HIV/STI risk
behavior, and emotional/behavioral symptoms
among juveniles at their earliest point of juvenile
court contact will critically inform the develop-
ment and implementation of early public
health screening, prevention, and treatment

Studies involving juvenile detainee samples
document high rates of drug and alcohol use,
psychiatric symptoms, and HIV/STI risk behav-
iors (Abram et al., 2003; McClelland et al., 2004;
Romero et al., 2007; Teplin et al., 2002). Nearly
half of juvenile detainees have one or more sub-
stance use disorders (Mauricio et al., 2009).
Estimates of diagnosable psychiatric disorders of
detained juvenile offenders range between 50 and
70% (Abram et al., 2003; Fazel et al., 2008;
Teplin et al., 2002; Wasserman et al., 2004). The
likelihood of acquiring HIV/STIs is also substan-
tially increased among justice-involved youth due
to high rates of sexual activity, and problems are
compounded when these behaviors co-occur
(Conrad et al., 2017; Tolou-Shams et al., 2019).
Mental health problems are linked to crimino-
genic risk and when paired with substance use,
contribute to poor outcomes (Doherty et al.,
2008; Elkington et al., 2008; Schubert et al.,
2011). Studies of juvenile detainees with co-
occurring substance use and psychiatric concerns
demonstrate that most are sexually active and

CONTACT Marina Tolou-Shams [email protected] University of California, San Francisco, Division of Infant Child and Adolescent
Psychiatry, Zuckerberg San Francisco General Hospital, 1001 Potrero Ave., San Francisco, CA 94110, USA.
� 2020 Taylor & Francis Group, LLC

2019, VOL. 28, NO. 5, 291–303

more than half have had multiple partners and
unprotected sex during the past month (Teplin
et al., 2005, 2003).

The current study and theoretical framework

Project EPICC (Epidemiological Project Involving
Children in the Court) is a 2-year longitudinal
study of male and female first-time offending,
court-involved, non-incarcerated (FTO-CINI)
youth assessed within a month of initial juvenile
court contact and uses ecodevelopmental theory
as guiding framework, which has been widely
used in the HIV prevention literature and with
substance-using, delinquent youth (Szapocznik
& Coatsworth, 1999). Ecodevelopmental
theory (Szapocznik & Coatsworth, 1999) extends
Bronfenbrenner’s ecological model of human
development (i.e., micro-, meso-, exo- and
macro-system influences on behavior; Bronfenb
renner, 1986) by providing a framework to
understand risk and protective factors for adoles-
cent substance use, psychiatric symptoms, and
HIV/STI risk behavior while accounting for the
role of different contexts and developmen-
tal processes.

Prior community-based study pathway model
studies, such as those within the Office of
Juvenile Justice and Delinquency Prevention’s
program of Research on Causes and Correlates of
Delinquency (e.g. Pittsburgh Youth Study, the
Denver Study; Loeber & Hay, 1997; Loeber et al.,
1997; Loeber et al., 1993), help identify “at-risk”
youth to develop primary prevention interven-
tions prior to the onset of delinquency; these
youth may or may not ever come into contact
with the justice system. Tertiary prevention stud-
ies, such as the Pathways to Desistance Study
(Mulvey et al., 2004), provide data on factors that
may reduce recidivism among the most violent
and dangerous of juvenile offenders and are not
designed to capture juvenile risk behavior trajec-
tories prior to their severe and violent criminal
offenses. Teplin and colleagues’ seminal juvenile
detainee studies have highlighted the importance
of studying HIV/STI risk behavior among juven-
ile offenders to prevent infection into adulthood
and focus on detention and community reentry
(Teplin et al., 2005, 2003). To date, there is one

other study, aside from Project EPICC, with pub-
lished data that examines similar relationships
and trajectories among FTO offenders, but differs
from Project EPICC by only including FTO male
offenders arrested for a range of low-level
offenses and does not include a focus on HIV/
STI risk behavior (Fine et al., 2016, 2017). To fill
an essential gap in the field, Project EPICC uses
ecodevelopmental theory to achieve two primary
aims: (1) examining (from the caregiver and
juvenile perspectives) initial risk behavior profiles
subsequent to the first point of contact with the
juvenile justice system and (2) identifying multi-
level factors associated with those initial profiles
to inform intervention development in a setting
that lacks evidence-based programing (Schwalbe
et al., 2012). Distinct from other studies, Project
EPICC focuses on a secondary prevention per-
spective by measuring youth’s risk behaviors
from the time of very first court contact, which
may serve as a “turning point” for substance use
and co-occurring risk behaviors (Hussong et al.,
2008). This information is urgently needed given
that most diversion programs for CINI youth do
not improve behavioral health outcomes or
reduce recidivism (Schwalbe et al., 2012).

The current analysis focuses on the first ecode-
velopmental theory layer, the microsystem, that
encompasses the youth (e.g., their emotional and
behavioral health functioning) and their relation-
ships within immediate social contexts, including
peers and family. We sought to fill a gap in the
literature by examining youths’ initial risk behav-
ior profiles and intersecting risks. Literature sup-
ports the importance of assessment and
intervention for the sexual and reproductive
health needs of justice-involved youth (Tam
et al., 2019), but only a few studies of justice-
involved youth have incorporated measurement
of substance use and HIV risk (Tolou-Shams
et al., 2019). Of those studies, rates of co-
occurrence of these behaviors among justice-
involved youth are high (e.g., Abram et al., 2017;
Tolou-Shams et al., 2007; Tolou-Shams et al.,
2010; Tolou-Shams et al., 2017). Yet, to our
knowledge, there are no published studies to
inform the field as to whether and how co-occur-
ring substance use and HIV/STI risk behaviors
may potentiate the need for mental health


intervention at time of first offense.
Understanding how these behavioral risk factors
co-occur to promote or protect against mental
health needs at this early justice contact can
inform the development of resource-efficient,
multi-component integrated interventions to
potentially offset poor public health and legal
outcomes for these underserved youth.


This paper presents baseline Project EPICC data
collected between June 2014 and July 2016 from
423 FTO-CINI youth and involved caregiver dyads,
with specific emphasis on demographics and youth
risk behaviors (e.g., substance use and HIV/STI
risk) and mental health needs. The mental health
focus includes emotional symptoms, such as trauma
and affects dysregulation, and behavioral symptoms
such as to conduct and delinquency because these
emotional and behavioral symptoms have been
most commonly studied in other samples of just-
ice-involved youth and tied to health risk behaviors
such as substance use and risky sexual activity
(McReynolds & Wasserman, 2011; Tolou-Shams
et al., 2008; Tolou-Shams et al., 2011; Tossone
et al., 2018). We hypothesized that FTO-CINI
youth would report higher rates of drug and alco-
hol use, sexual (HIV/STI) risk behaviors, and emo-
tional/behavioral symptoms than those published
among general adolescent and community-based
delinquency samples, but below that reported on
detained youth. Among FTO-CINI youth, we
hypothesized that those with co-occurring substance
use and sexual risk behaviors would report higher
rates of recent emotional/behavioral symptoms than
all others. We also hypothesized that FTO-CINI
girls would show heightened risk on all outcomes
relative to FTO-CINI boys consistent with prior lit-
erature demonstrating unique, gender-specific needs
for justice-involved girls (Conrad et al., 2017;
Dembo et al., 2017; Holzer et al., 2018).


Sampling and recruitment procedures

A total of 423 FTO-CINI youth and caregiver
dyads were enrolled. Youth, ages 12–18, and

caregivers were approached for study participa-
tion if the juvenile had an open status and/or
delinquent petition filed through a large Family
Court in the Northeastern region of the United
States. Status petitions were defined as those filed
for an offense that would typically not be consid-
ered illegal if an adult committed the same
offense (e.g., truancy, alcohol use, curfew).
Delinquency petitions were defined as those filed
for offenses that are considered illicit regardless
of age (e.g., breaking and entering, assault). Of
423 dyads, 194 (46%) had a first-time status
offense (FTO-status) and 229 (54%) had a
first-time delinquent offense (FTO-delinquent).
FTO-CINI girls with a delinquent FTO were
oversampled to have sufficient power to conduct
male–female comparisons.

Exclusion criteria
Study exclusion criteria included being a repeat
offender (at time of initial recruitment), outside of
the 12 to 18-year-old age range at the initial court
intake appointment, juvenile or caregiver cognitive
impairment that would preclude the ability to
complete assessment, and/or caregiver unable or
unwilling to participate or had not lived with the
youth for at least the prior 6 months.

Retention and assessment procedures

All caregivers of FTO-CINI youth were sent a
study flyer along with the standard court
appointment date notification letter and then
approached in the court setting for study partici-
pation. Interested youth and families were
screened for eligibility in a private space at the
court and assent and consent was obtained off-
site (home, private community space, or research
lab), when appropriate. To enhance engagement
and retention, we used a variety of strategies
including: obtaining a locator form in which
youth and caregiver provided contact info of up
to five individuals who will always know where
they are and could help us locate them in the
future; scheduling subsequent appointments at
the time of the prior assessment; sending out
weekly reminder emails, texts (and making phone
calls as needed if no response to texts) to remind
youth and caregivers of appointments and make


any relevant changes to locator information;
obtaining releases of information from youth and
caregivers for permission to contact the court to
help us locate them; reminding youth and care-
givers of home or community-based visit options
for assessment; sending birthday and holiday
cards from the project, in order to enhance recall
and familiarity with the project; mail or drop off
to hard-to-reach families’ homes a personalized
letter from study staff, that would include study
contact information and the scheduled follow-up
appointment (if applicable). We also provided
project pens and other items with the project
logo and name to youth, caregivers, and court
stakeholders. These items served as reminders of
participation in the study for both families and
system stakeholders. Lastly, we set up a profile
on the social networks, Facebook, Twitter, and
Instagram only to notify participants about their
appointments. We did not “friend” or “follow”
any of the participants or accept the “friending”
of participants. The page contained the project’s
contact information for participants who needed
to schedule appointments but were not reachable
by text, phone, or in-person. Our page did not
reveal the nature of the study but was recogniz-
able to participants by its logo. The Principal
Investigator’s university and collaborating sites’
Institutional Review Boards approved all recruit-
ment and study procedures.

Youth and caregivers completed separate
assessments (�2 h per assessment) using tablet-
based, audio-assisted computerized assessment
(ACASI) in English and Spanish (parent-only).
ACASI has been shown to improve the reliability
of self-report (Romer et al., 1997), which is easy
to administer and is time and cost-effective. The
majority of assessments were conducted in pri-
vate space the participants’ homes, at the research
offices, the courthouse, and on occasion, at other
community locations (e.g., library or coffee
shop). Caregivers and youth were separated for
the administration when it was logistically pos-
sible—and when not possible (e.g., due to being
in a small home, in a single room coffee shop),
they were positioned at opposite ends of the
room so that neither would be directly distracted
by the other’s presence or able to see any
responses on the tablet.


Self-reported baseline measures assessed basic
demographics, school and treatment history along
with lifetime and recent (past 120 days) substance
use, sexual risk behaviors, and emotional/behav-
ioral symptoms.

Youth and caregiver demographics, youth aca-
demic, and treatment history
Demographics included, but were not limited to,
age, gender, race, ethnicity, and sexual orientation.
The Arrest and Treatment History (ATH)
Questionnaire (developed for this study) queried
mental health and substance use treatment history,
treatment needs, and utilization, a state agency
(e.g., out-of-home placement) and legal involve-
ment. Self-report data were also collected on cur-
rent school status, grades, history of repeated
grades, and receipt of special education services
(e.g., individualized education plan [IEP]).

Youth substance use and sexual (HIV/STI)
risk behaviors
The Adolescent Risk Behavior Assessment (ARBA;
Donenberg et al., 2001) assesses the type of sex-
ual behavior (i.e., oral, vaginal or anal), frequency
of condom use and intercourse (e.g., condom use
at last sexual intercourse), age of sexual debut,
number of sex partners, and substance use by self
and/or partner preceding and/or during sex. This
measure also included self-reported (lifetime and
past 120 days) nicotine, alcohol, marijuana, and
other drug use (e.g., cocaine, prescription drugs)
with respect to quantity, frequency, and other
past use descriptives (e.g., age of onset).

Youth emotional and behavioral symptoms
Emotional symptoms included: (1) the National
Stressful Events Survey PTSD Short Scale
(NSESSS; ) that corresponds with DSM-V diag-
nostic criteria for posttraumatic stress disorder
(PTSD). It is a brief, 9-item measure of posttrau-
matic stress symptoms over the past 7 days for
those youth who endorse a particularly stressful
event/experience. Youth report the extent to
which they have been bothered by problems the
stressful event (1¼ not at all bothered to
5¼ extremely bothered) suggesting a degree of


traumatic stress severity. Average scores range
from 0–4; and (2) the Affect Dysregulation Scale
(ADS), a six-item instrument utilized and vali-
dated in our prior studies of youth in psychiatric
care to assess youth’s frequency of difficulties
with affect regulation (Brown et al., 2012). The
youth responded on a 4-point scale (1¼ not at
all to 4¼ often) and summed scores ranged from
6–24; higher scores indicate greater affect dysre-
gulation (alpha ¼ 0.79). Behavioral symptoms
include: (1) the National Youth Survey Self-
Reported Delinquency (NYS-SRD; Elliott et al.,
1985) scale, a well-validated, 40-item, self-report
measure of delinquent acts (e.g., larceny, fighting,
selling drugs). Scores were used from the General
Delinquency subscale ranging from 0–23 with
higher scores indicating a greater number of
delinquent acts (in the past 120 days) endorsed1

and (2) two yes/no items concerning gang
involvement from the National Youth Risk
Behavior Survey (YRBS; Eaton et al., 2012).

Analysis plan

Descriptive statistics were calculated for all varia-
bles of interest and scales. Given our hypotheses
related to poorer behavioral health outcomes
associated with cumulative and co-occurring risk,
behavioral risk indices were developed for sub-
stance use and sexual risk behaviors as follows:

Substance use risk index
Variables used to create the substance use risk
index included: ever used alcohol ¼ 1; recent
(past 120 days) alcohol use ¼ 1; ever used mari-
juana ¼ 1; recent (past 120 days) marijuana use
¼ 1; ever used other illicit drugs ¼ 1; recent
(past 120 days) other illicit drug use ¼ 1. Scores
ranged from 0–6, with scores of 0 indicating no
lifetime alcohol, marijuana or drug (i.e., sub-
stance) use, a score of 1 indicating less substance
use/risk, and 6 indicating maximum substance

Sexual (HIV/STI) risk behavior index
Variables used to create the sexual risk index
included: ever sexually active ¼ 1; recently (past
120 days) sexually active ¼ 1; no condom use at
last sex ¼ 1; self or partner substance use during

sex ¼ 1. Scores ranged from 0–4, with a score of
0 indicating no lifetime sexual activity, 1 indicat-
ing less sexual behavior risk, and 4 indicating
maximum sexual behavior risk.

Risk indices comparison
Descriptive statistics on each index were exam-
ined and each index was then dichotomized into
0 versus any risk. Risk indices were defined as
“No risk” (neither substance use nor sexual risk
behavior); “single risk” (either substance use or
sexual risk behavior) and “co-occurring risk”
(substance use and sexual risk behavior). A Venn
diagram (Figure 1) presents the extent of overlap
between participants reporting both sexual and
substance use-related risks. The overlapping
group was categorized as having a “co-occurring
risk.” Sociodemographic differences between the
“co-occurring risk” group, the sub-group report-
ing “single risk” and the “no risk” groups were
examined using Chi-square tests. The interrela-
tionship of risk indices and their association with
the third primary study outcome of emotional/
behavioral symptoms (ADS, NSESSS, NYS) was
examined using multivariate analyses of covari-
ance (MANCOVA). All MANCOVA analyses
were adjusted for age, sex, and FTO status (i.e.,
an indicator of FTO severity). For each outcome
of interest, we conducted post-hoc tests (i.e., to
determine statistical significance between groups)
if the omnibus one-way MANCOVA test statistic
was significant at p< 0.05. All statistical analyses
were conducted in SAS version 9.3, and all
p-values are two-sided.


Demographics, education, and treatment/agency

FTO-CINI youth were an average of 14.6 years
(SD ¼ 1.5), and 46% were female (see Table 1).
Racial and ethnic minority CINI youth were dis-
proportionately represented in the system relative
to regional census figures. Youth and caregivers
reported high rates of youth past psychiatric his-
tory (including diagnosis, medications, and hospi-
talization). Caregivers/families of FTO-CINI
youth was predominantly female, birth parents


with an average age of 41 years. Caregivers’ racial
and ethnic self-identification largely mirrored
that of their youth. Caregivers were predomin-
antly single parents, low-income, and receiving
public assistance (Table 1).

Primary outcomes (lifetime and past 120 days)

Substance use
Twenty-one percent of CINI youth reported life-
time cigarette use and early age of onset (13-
years-old; Table 2). Over half of youth lifetime
smokers reported recent, frequent smoking (used
40 out of past 120 days). Alcohol use was
reported by a third of youth (average age of onset
of 14 years) and two-thirds of those youth
reported recent, but on average infrequent, alco-
hol use (used 6 out of past 120 days). Marijuana
use was most prevalent with almost 50% of youth
endorsing lifetime use and the average age of
onset of 13 years. Of those youth, 80% endorsed
recent and frequent use (used 38 out of the past
120 days). Thirteen percent of FTO-CINI youth
reported other lifetime drug use.

Sexual (HIV/STI) risk behaviors
Approximately 40% of FTO-CINI youth reported
lifetime sexual activity (Table 2). Most sexually
active CINI youth reported vaginal (86%) and
oral (81%) sex. Of youth ever sexually active,
74% reported recent sexual activity, 63% reported
condom use at last sex and 49% reported recent

substance use (either themselves and/or their
partner) during sex. Sexually active CINI youth
reported having a median of 2 (IQR: 1–5) life-
time and 1 (IQR: 1–3) recent sexual partner. The
history of pregnancy and STIs was low (1 and
2%, respectively).

Emotional symptoms
Juveniles reported an average ADS score of 12.8
(SD¼ 4.4; range 6–24; Table 2). Over three-quar-
ters (79%) of youth endorsed trauma exposure
with an average traumatic stress severity
(NSESSS) score of 1.2 (SD¼ 1.1; range 0–4).

Behavioral symptoms
On average, the youth in this sample reported a
low score on the NYS delinquency scale (M¼ 2.1;
SD¼ 2.6). Twenty-seven youth (6.4%; predomin-
antly male) reported any history of gang

Bivariate gender analyses

Male and female CINI youth differed on certain
demographics, risk behaviors, and emotional and
behavioral symptoms (Table 2). In terms of pri-
mary outcomes of interest, males reported signifi-
cantly more condom use at last sex than females.
Females reported overall higher rates of nicotine
use and marijuana use than males with the caveat
that males who were recent smokers endorsed
more frequent recent use and differences in

Figure 1. Venn diagram of co-occurring substance use and sexual risk (n¼ 423). 174 (41.1%) participants reported no sexual or
substance use risk and are not shown in the Venn diagram. There were 82 youth reporting only substance use risk and 26 youth
reporting only sexual risk.


marijuana use were at the trend level of signifi-
cance (p¼ 0.06). There were no gender differen-
ces in alcohol use. Lastly, females reported
significantly greater effect dysregulation than
boys; there were no other statistically significant
gender differences on measures of emotional or
behavioral symptoms.

Risk indices

Substance use index
Scores ranged from 0–6; 47% of youth (n¼ 200)
fell in the “no substance use risk” category, 8%

(n¼ 35) received a score of 1, 15% (n¼ 62)
received a score of 2, 9% (n¼ 39) received a
score of 3, 12% (n¼ 51) received a score of 4, 5%
(n¼ 20) received a score of 5 and 4% (n¼ 16)
received a score of 6.

Sexual risk index
Scores ranged from 0–3 with 61% percent of
youth (n¼ 256) with scores of 0 or “no risk” cat-
egory; 22% (n¼ 95) received a score of 1, 12%
(n¼ 51) a score of 2; and 5% (n¼ 21) a score of
3 (no youth had the maximum score of 4).

Table 1. FTO-CINI youth demographics, education, and treatment/agency involvement (n¼ 423).
Total (n¼ 423) Male (n¼ 226) Female (n¼ 193)

Test statisticMean (SD) or n (%)

Age 14.55 (1.53) 14.62 (1.51) 14.49 (1.55) 0.88
Gender (% female) 193 (45.63%)

Caucasian 189 (44.68%) 102 (46.36%) 85 (45.21%) 0.05
Black, African-American, or Haitian 74 (17.49%) 37 (16.82%) 37 (19.68%) 0.56
American Indian 39 (9.22%) 18 (8.18%) 21 (11.17%) 1.05
Asianb 5 (1.18%) 4 (1.82%) 1 (0.53%) 0.38
Native Hawaiian or other Pacific Islander 6 (1.42%) 6 (2.73%) 5.20�
Multi-racial 71 (16.78%) 43 (19.55%) 28 (14.89%) 1.52
Other 79 (18.68%) 34 (15.45%) 44 (23.40%) 1.39

Hispanic or Latinx 181 (43.61%) 100 (45.45%) 79 (41.36%) 0.70
Ethnic origin 1.52
Puerto Rican 94/181 (51.93%) 54 (54.00%) 39 (51.32%)
Dominican 55/181 (30.39%) 33 (33.00%) 22 (28.95%)
Other Latinx 29/181 (16.02%) 13 (13.00%) 15 (19.74%)

Sexual orientation (% non-heterosexual) 81 (19.15%) 13 (5.91%) 65 (34.21%) 53.01���
Caregiver relationship to youth (% female birth parent) 344 (81.32%) 280 (79.65%) 160 (82.90%) 0.72
Number of children <18 years living in the home 2.61 (1.67) 2.63 (1.50) 2.60 (1.85) 0.19
Presence of another parent/caregiver in the home 182 (43.03%) 98 (43.36%) 84 (43.52%) 0.001
Primary caregiver currently employed 220 (52.01%) 119 (52.65%) 98 (50.78%) 0.15
Receive public assistance (current) 274 (64.78%) 143 (63.56%) 129 (66.84%) 0.48
Educationb p¼ 0.0036
Middle school 163 (38.53%) 88 (38.94%) 73 (37.82%)
High school 256 (60.52%) 138 (61.06%) 116 (60.10%)
Not currently in school 4 (0.95%) 4 (2.07%)

Ever repeated a grade in school 138 (32.62%) 87 (38.50%) 50 (25.91%) 7.58���
Ever received special education services 143 (33.81%) 94 (41.78%) 46 (24.08%) 14.49���
Ever had individualized education plan 171 (40.43%) 107 (47.56%) 61 (31.94%) 10.47���
Ever been expelled from school 32 (7.57%) 18 (8.00%) 14 (7.33%) 0.07
Ever been suspended from school 259 (61.67%) 149 (66.22%) 108 (56.54%) 4.10�
Psychiatric and substance use treatment history
Psychiatric diagnosis, lifetime 126 (29.79%) 65 (28.89%) 60 (31.41%) 0.31
Prescribed psychiatric medications, lifetime 130 (30.73%) 66 (29.33%) 64 (33.86%) 0.98
Prescribed psychiatric medications, past 4 months 91/130 (70.00%) 47/66 (71.21%) 44/64 (68.75%) 0.09

Psychiatric inpatient hospitalization, lifetime 71 (16.78%) 34 (15.04%) 36 (18.95%) 1.12
Day hospital or partial hospitalization, lifetime 63 (14.89%) 31 (13.72%) 30 (15.87%) 0.38
Day hospital or partial hospitalization, past 4 months 11/63 (17.46%) 5 (16.13%) 6 (20.00%) 0.15
Visited community outpatient drug or alcohol

clinic, lifetimeb
4 (0.95%) 2 (0.88%) 2 (1.05%) p¼ 1.00

Visited community outpatient drug or alcohol clinic, past
4 monthsb

3/4 (75.00%) 1 (50.00%) 2 (100.00%) p¼ 1.00

Visited a mental health center for psychiatric or mental
health problems, lifetime

97 (22.93%) 47 (20.80%) 49 (25.79%) 1.45

Visited a mental health center for psychiatric or mental
health problems, past 4 months

58/97 (59.79%) 28 (59.57%) 29 (59.18%) 0.002

Note. N’s may vary according to patterns of missing data due to non-response, including 4 participants who did not respond to gender (male/female)
item. aIndividuals were able to select more than one racial category and as such, percentages may not equal 100. bChi-square statistics not reported for
categories containing cells with n< 5, instead we report the Fisher’s Exact Test; �p¼ 0.05; ��p< 0.05���p< 0.01.


Substance use and sexual risk indices were
positively correlated and when identifying co-
occurring risk, three categories emerged (see
Figure 1): those who had “no substance use or
sexual risk” (n¼ 174; 41%); those who had
“either sexual or substance use risk” (n¼ 108;
26%) and those who had “co-occurring sexual
and substance use risk” (n¼ 141; 33%).

Bivariate associations
First-time offense type and age were associated
with risk indices such that youth with a delin-
quent first-time offense and older youth
(15–18 years) were more likely to have

co-occurring risks than first-time status offenders
[v2 (2, n¼ 423) ¼ 5.86, p¼ 0.05] and youth aged
12–14 years, respectively [v2 (2, n¼ 422) ¼ 87.25;
p< 0.0001]. There were no statistically significant
differences in the proportion of males to females
in any of the risk categories [v2 (2, n¼ 419) ¼
2.59, p> 0.05].

Models of risk
MANCOVA results examining the association of
substance use and sexual risk indices with emo-
tional/behavioral symptoms and delinquent
behavior are presented in Table 3. Twenty-two
percent of youth were missing an NSESSS score

Table 3. MANCOVA models of behavioral health risk and emotional/behavioral symptoms.

Co-occurring risk
(n¼ 123), Mean (SE)

Sexual risk or
substance use

behavior (n¼ 78),
Mean (SE)

No risk (n¼ 122),
Mean (SE) Multivariate (F) p-Value

Affect Dysregulation Scale (ADS) 14.10 (0.39)a 13.10 (0.46)a 11.83 (0.39)b 7.34 0.0008
Delinquency (NYS) 3.70 (0.23)a 2.01 (0.27)b 0.68 (0.23)c 37.26 <0.0001
Trauma symptoms (NSESSS) 1.40 (0.10)a 1.11 (0.12)a,b 0.87 (0.10)b 6.13 0.0024

Note: All models controlled for age, sex, and offender status. Different superscripts in the same row indicate a significant difference (p< 0.05) between
the indicated groups. MANCOVA requires listwise deletion and due to reduced sample size on the NSESSS symptom reporting (21% denied any trauma
exposure and were not included for symptom scores), overall sample size was reduced accordingly.

Table 2. FTO-CINI youths’ baseline lifetime and recent substance use, HIV/STI risk and psychiatric symptoms (n¼ 423).
Total (n¼ 423) Male (n¼ 226) Female (n¼ 193) Test statistic

Mean (SD), Median (IQR) or n (%)

HIV/STI risk
Sexually active, lifetimea 167/414b (40.34%) 90 (41.28%) 77 (40.10%) 0.06
Condom use at last sex (% yes) 105/167 (62.87%) 64 (72.73%) 41 (55.41%) 5.29�
Sexually active,a past 4 months 124/167 (74.25%) 64 (71.11%) 60 (77.92%) 1.01
Substance use at last sex (self/partner) 61/124 (49.19%) 27 (42.86%) 34 (58.62%) 3.00

Number of sex partners (oral, vaginal, anal), lifetime (median, IQR) (n¼ 167) 2.00 (1-5) 4.29 (5.49) 3.23 (3.18) 1.48
Number of sex partners, past 4 months (median, IQR) (n¼ 124) 1.00 (1-3) 5.25 (13.85) 2.15 (2.39) 1.69

Getting pregnant or getting someone else pregnant, lifetimec 6 (1.42%) 1 (0.45%) 5 (2.60%) p¼ 0.10
Sexually transmitted infections (STI) diagnosis, lifetimec,d 9 (2.13%) 3 (3.37%) 6 (7.79%) p¼ 0.31
Sexually transmitted infections (STI) diagnosis, past 4 monthsc,d 4/9 (44.44%) 2 (66.67%) 2 (33.33%) p¼ 0.52
Substance use
Cigarette use, lifetime 90 (21.28%) 38 (17.04%) 51 (26.56%) 5.55�
Cigarette use, past 4 months 50/90 (55.56%) 21 (55.26%) 28 (54.90%) 0.001
Number of days smoking, past 4 months (mean, SD) 40.45 (46.41) 56.45 (53.27) 27.13 (35.71) 2.18�
Alcohol use, lifetime 138 (32.62%) 64 (28.83%) 71 (36.79%) 2.98
Alcohol use, past 4 months 93/138 (67.39%) 40 (62.50%) 51 (71.83%) 1.33
Number of days drinking, past 4 months (mean, SD) 5.86 (11.27) 5.63 (6.16) 6.08 (14.19) �0.20
Marijuana use, lifetime 205 (48.46%) 100 (45.25%) 104 (54.74%) 3.68t

Marijuana use, past 4 months 164/205 (80.00%) 76 (76.00%) 87 (83.65%) 1.86
Number of days of marijuana use, past 4 months (mean, SD) 37.99 (44.24) 34.66 (42.44) 39.82 (45.20) �0.70
Other drug use, lifetimee 55 (13.00%) 26 (11.82%) 28 (14.97%) 0.87
Other drug use, past 4 months 22/55 (40.00%)

Emotional and behavioral symptoms
Affect Dysregulation Scale score (mean, SD) 12.87 (4.38) 11.66 (3.89) 14.12 (4.44) �5.88���
Trauma symptoms (National Stressful Events Survey Short Scale; NSESSS) 1.15 (1.07) 1.02 (1.01) 1.23 (1.09) �1.77
NYS Delinquency (General Delinquency subscale) 2.10 (2.68) 2.26 (2.92) 1.88 (2.38) 1.48

Note. aVaginal, oral, or anal; bn¼ 9 persons refused to answer the sexual activity questions; cChi-square statistics not reported for categories containing
cells with n< 5 , instead we report the Fisher’s Exact Test; dGonorrhea, Chlamydia, trichomonas, or syphilis; eOther drug use includes any other drug
use outside of marijuana use (e.g., methamphetamines, opioids), but includes synthetic marijuana use; �p¼ 0.05; ��p< 0.05;���p< 0.01; t(trend)p¼ 0.06.


(due to reporting no lifetime trauma exposure);
thus, due to listwise deletion inherent in
MANCOVA, the sample size was reduced to 323
youth (n¼ 123 with co-occurring risk, n¼ 78
reporting sexual or substance use risk behavior,
and n¼ 122 youth with no risk). Controlling for
variables both empirically and theoretically
expected to be associated with primary outcomes
(i.e., age, sex, and offender status), we observed
statistically significant associations between all
three outcomes and the risk groups (see omnibus
F statistics and corresponding p-values in
Table 3). Thus, post-hoc tests were conducted for
all three measures. Youth with co-occurring risk
had significantly higher mean scores than youth
with single risk on measures of delinquent behav-
iors (p< 0.001) and significantly higher mean
scores than youth with no risk on measures of
emotional/behavioral symptoms and delinquent
behaviors (p< 0.001 and p¼ 0.004, respectively).
Youth with single risk (either sexual or substance
use) risk had significantly higher mean scores
than youth with no risk on measures of affect
dysregulation and delinquency (p¼ 0.036 and
p< 0.001, respectively). Females with co-occur-
ring risk reported more delinquency (F(1, 322) ¼
10.33, p¼ 0.001) and affect dysregulation
(F(1,322) ¼ 23.33, p< 0.001) than all other
groups (i.e., versus male co-occurring risk or
females in the “no risk” or “single risk” group).


Project EPICC is among the first to document
across a uniquely large sample of FTO-CINI
youth that rates of substance use, emotional/
behavioral symptoms, and sexual risk behaviors
are high and co-occur. Rates of risk behaviors
and emotional/behavioral symptoms appear to fit
in squarely between those previously reported in
community-based delinquency prevention studies
[e.g., OJJDP’s Program of Research on the Causes
and Correlates of Delinquency (Office of Juvenile
Justice and Delinquency Prevention, 2016)] and
those with detained youth (e.g., Elkington et al.,
2008; Teplin et al., 2005). Our data suggest that
integrated care is relevant and needed for youth
at this early intercept of justice involvement.
Substance use and sexual activity start as early as

13–14 years of age. For the almost 50% already
using marijuana by the time of first legal contact,
use is recent and frequent, averaging 10 days of
marijuana use per month. Trauma exposure is as
high as that reported for detained youth, who are
presumed to be further entrenched in the system
and more severe in their psychiatric presentation
and needs. Almost one-third of FTO-CINI youth
have a lifetime history of psychiatric diagnosis
and 31% have a history of psychotropic medica-
tion. These findings support our hypothesis that
this is a critical group of youth to target for sec-
ondary prevention of substance use, psychiatric
co-morbidity, and co-occurring sexual
risk behaviors.

Consistent with other literature indicating that
youth with psychiatric symptoms have higher
rates of substance use and sexual risk behaviors
(Brown et al., 2014, 2010; Conrad et al., 2017),
FTO-CINI youth with co-occurring substance use
and sexual risk behaviors appear to endorse
higher rates of emotion dysregulation and trau-
matic stress symptoms. Thus, even for youth
whom the courts and community might perceive
as “low level” offenders and less severe in terms
of behavioral health risk when compared to
detained youth, substantial substance use and
sexual risk behaviors are occurring at an early
stage of legal contact and are highly associated
with emotional/behavioral difficulties. The
Juvenile Justice Translational Research on
Interventions for Adolescents in the Legal System
(JJ-TRIALS) implementation behavioral health
study suggests that more research is urgently
needed to understand how to improve the behav-
ioral health services cascade of care for commu-
nity-supervised justice-involved youth,
particularly as it relates to improving substance
use services (Belenko et al., 2017; Knight et al.,
2016). Our data strongly support the relevance
and need to enhance the juvenile justice
behavioral health cascade of care for this commu-
nity-supervised population and highlight that
psychiatric and sexual health services should be
incorporated with substance use cascade of care
efforts. This integration of care will require a
concentrated partnership between public health
and juvenile justice systems to identify ways in
which they can embed behavioral health


resources into court or community-based diver-
sion settings. Innovative examples might include
partnerships to develop juvenile court clinics
and/or incorporating behavioral health screening
and intervention resources for FTO-CINI youth
served through collaborative court models (e.g.,
juvenile drug court).

Key implications and next steps

There are some key ways in which our data sug-
gest behavioral health services should be tailored
to adeptly meet the needs of FTO-CINI youth.
The first is the need for family engagement.
Caregiver data suggest that the close majority of
families are impoverished, single-parent house-
holds and have a history of child welfare involve-
ment. Behavioral health efforts will require
considerable support resources and specific fam-
ily engagement strategies to improve youth out-
comes. Family motivation and engagement may
be high at this initial stage of justice contact
before the youth may become more system-
entrenched and caregivers have more system
fatigue or frustration or feel “failed” by the sys-
tem. Researching ways that court-involved fami-
lies can be more successfully engaged in linkage
for youth substance use treatment, for example,
is sorely needed. The second is gender-responsive
programming. Our hypothesis that FTO-CINI
females with co-occurring risk would report
more delinquent behaviors and emotional symp-
toms than all other groups was supported.
Training court and diversion staff on gender-
responsive approaches to behavioral health
screening, assessment, and intervention that con-
sider the unique pathways of girls into the system
and their ongoing gender-specific needs is
imperative. Research is needed on the efficacy
and implementation of gender-responsive sub-
stance use and mental health treatment for just-
ice-involved girls, given that these CINI girls
have a higher prevalence of risk factors for recid-
ivism, such as sexual abuse, relative to justice-
involved boys (Conrad, Placella, Tolou-Shams,
Rizzo, & Brown, 2014). The third is the incorpor-
ation of culturally congruent services. Consistent
with justice system statistics at large, racial and
ethnic minority youth in our study were

disproportionately represented at first justice con-
tact; within the jurisdiction that these data were
collected, Latinx and African American youth
representation were double that of existing census
data. Data are clear—across settings (e.g., pediat-
ric, community)—that racial and ethnic minority
youth confront different challenges to engage-
ment in substance use and psychiatric care than
their white counterparts that perpetuate health
and legal disparities (Marrast et al., 2016). Efforts
in addressing the substance use, mental and sex-
ual health needs of FTO-CINI youth must dir-
ectly address cultural differences, needs, and
desires of justice-involved minority families.


Data were collected from families in one region
of the Northeastern US and therefore may limit
representativeness and generalizability. Self-report
data may be associated with under-reporting of
risk and/or sensitive behaviors and in some cases,
having to assess caregivers and youth within the
same location or room might have affected
responding; however, our data suggest that
under-reporting may not have been a concern
given that rates of risk behaviors were high,
including reports of marijuana use. Cross-sec-
tional data limits our ability to understand
causality and direction across variables of inter-
est; however, future longitudinal analysis with
this same cohort will be able to disentangle, for
example, the temporal relationships between
substance use, psychiatric symptoms and
re-offending to further inform the field as to how
and when best to intervene to improve FTO-
CINI youth outcomes.


Our data support a critical need to identify ways
in which we can improve early access to sub-
stance use, sexual and mental health services for
a group of youth who have significant behavioral
health needs but are typically overlooked as being
less risky or “in need.” Increasing access to and
engagement with substance use and mental health
services could have profound implications for
later public health and legal outcomes. Future


Project EPICC analyses will be able to identify
trajectories of youth in each risk index and asso-
ciation with future behavioral health and legal
outcomes to inform more tailored prevention
and intervention efforts for these vulnerable
youth and families.


1. The original subscale includes 24 items. Due to an error
in ACASI development, item 24 of the NYS general
delinquency scale, “Have you had sexual intercourse with
a person who was not your serious partner when
involved in a relationship?” was not administered to
study participants; therefore, subscale scores range from
1-23 but still accurately indicate that greater scores
represent greater number of delinquent acts.


The authors extend their gratitude to the adolescents and
families who participated in this study as well as to the col-
laborating court system, staff and stakeholders who sup-
ported successful study implementation.


This work was supported by National Institute on Drug
Abuse [R01DA034538 (Dr. Tolou-Shams) and
R25DA037190 (Dr. Dauria)] and National Institute of
Mental Health [K23MH111606 (Dr. Kemp)]. The content is
solely the responsibility of the authors and does not neces-
sarily represent the official views of the National Institute
on Drug Abuse, National Institute of Mental Health or
National Institute of Health.


Brandon D. L. Marshall


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  • Abstract
    • Introduction
      • The current study and theoretical framework
      • Hypotheses
    • Methods
      • Sampling and recruitment procedures
        • Participants
        • Exclusion criteria
      • Retention and assessment procedures
      • Measures
        • Youth and caregiver demographics, youth academic, and treatment history
        • Youth substance use and sexual (HIV/STI) risk behaviors
        • Youth emotional and behavioral symptoms
      • Analysis plan
        • Substance use risk index
        • Sexual (HIV/STI) risk behavior index
        • Risk indices comparison
    • Results
      • Demographics, education, and treatment/agency involvement
      • Primary outcomes (lifetime and past 120 days)
        • Substance use
        • Sexual (HIV/STI) risk behaviors
        • Emotional symptoms
        • Behavioral symptoms
      • Bivariate gender analyses
      • Risk indices
        • Substance use index
        • Sexual risk index
        • Bivariate associations
        • Models of risk
    • Discussion
      • Key implications and next steps
      • Limitations
    • Conclusions
    • Acknowledgments
    • References

Crisis intervention team training: when police encounter persons
with mental illness

Michele P. Bratinaa, Kelly M. Carrerob, Bitna Kimc and Alida V. Merloc

adepartment of criminal Justice, West chester University of Pennsylvania, West chester, Pa, Usa; bdepartment of
Psychology, counseling, & special education, texas a & M University–commerce, commerce, tX, Usa; cdepartment
of criminology & criminal Justice, indiana University of Pennsylvania, indiana, Pa, Usa

The Crisis Intervention Team (CIT) model is an established training program
used to improve police response to encounters involving persons with
mental illness (PwMI). Diversion of PwMI from the criminal justice system
to appropriate treatment providers in the community is one of the primary
goals of CIT. The present study examines characteristics and outcomes of
encounters between citizens experiencing mental health-related crises
and CIT-trained patrol officers. Findings of this study indicate encounters
involving PwMI and CIT-trained officers often result in diversion to mental
health services. Implications for policy and future research are discussed.

In conjunction with policing in general, recent police encounters with citizens in crisis have garnered
the attention of criminal justice and mental health practitioners, criminologists, and the media. As a
response to interactions involving persons with mental illness (PwMI) and the criminal justice system
local jurisdictions in the United States and abroad have implemented specialized training programs to
deescalate crisis and divert PwMI to treatment services (Slate, Buffington-Vollum, & Johnson, 2013).
The Crisis Intervention Team (CIT) model is the most popular training program to improve police
response in this context (Hartford, Carey, & Mendonca, 2006).

The fields of psychiatry, behavioral science, psychology, public health, and social work have also
made or are making contributions to research on CIT (e.g., Borum, Deane, Steadman, & Morrissey,
1998; Franz & Borum, 2010; Compton, Esterberg, McGee, Kotwicki, & Oliva, 2006; Compton et al.,
2011; DeMatteo, LaDuke, Locklair, & Heilbrun, 2013; Ogloff et al., 2013). The evidence is cautiously
positive with respect to the success of CIT programs in diversion efforts and in maintaining the
safety of officers and consumers. The Memphis model1 is the most popular in terms of large-scale
implementation; yet empirical research on its effectiveness has been limited (Broussard et al., 2011).

Rigorous research investigating the effectiveness of CIT is scant but developing (Taheri, 2016).
Unfortunately, little is known about specific police contact rates and processes involved in critical
police encounters with PwMI. Franz and Borum’s (2010) study is the exception. Using a sample of CIT
calls in Central Florida from 2001 to 2005, they found that CIT training prevents arrests and decreases
arrest rates of PwMI. Specifically, each year after the implementation of CIT, arrests for mental health
disturbance calls steadily decreased over a five-year period.

The present study explores encounters between officers trained in the Memphis Model and PwMI
in one South Florida region. Self-reported police data from documented crisis calls in a medium-sized

© 2018 informa UK limited, trading as taylor & Francis Group

crisis intervention team;
mental health; policing

received 5 september 2017
accepted 26 May 2018

CONTACT Michele P. Bratina [email protected]

2020, VOL. 21, NO. 3, 279–296

jurisdiction were analyzed using bivariate analysis. The ultimate goal is the dissemination of informa-
tion that potentially supports the continued development, implementation, and expansion of training
to improve police encounters with citizens (many of whom have been identified as PwMI). This study
updates and expands on similar diversion work in central Florida (see Franz & Borum, 2010); and it
is intended to contribute to the policy debate.

Statement of the problem

Current statistics on mental illness prevalence reveal that one in every four adults in any given year,
or about 61.5 million Americans, will experience a diagnosable mental illness at some point in his/her
lifetime (National Alliance on Mental Illness, 2013a). Although it has been reported that about 3–6%
of police encounters involve PwMI (Schwarzfeld, Reuland, & Plotkin, 2008), statistics also indicate
that a disproportionate number of PwMI are involved in critical police encounters resulting in arrest,
and at the most extreme end of the spectrum, police shootings (Franz & Borum, 2010; Ogloff et al.,
2013). For example, in 2016 the Washington Post reported that police killed 963 people, and 241 of
them (i.e., 25%) were linked to mental illness. Similarly, Morabito and Socia (2015) examined all use
of force cases reported by city police departments between 2008 and 2011 in Portland, Oregon, and
found a significant relationship between subjects’ mental health and likelihood of injury to either
subjects or the police officers when force was employed – but only when citizens also had substance
abuse issues present at the time of the encounter.

Despite the increase in police encounters with persons in crisis, it appears that minimal attention in
police training deals with individuals who might be experiencing mental health issues. Most training
academies allot approximately 60 h on how to use a gun, but only 8 h for strategies to address persons
with mental illness (Lowery et al., 2015). It is important to recognize the cross-systems nature of PwMI,
and gaps in available mental health-related training for police and other first responders, are only part
of the problem in responding to and managing this population.

Recently, the intersection between the mental health and criminal justice systems has been empha-
sized as evidenced by media reports of police brutality toward PwMI (e.g., Borrelli, 2015 & McLaughlin,
2015) and mistreatment of inmates with mental illness by correctional staff and contracted mental
health providers in jails and prisons (e.g., Berman, 2015; Fellner, 2015; Miller, 2015). Simultaneously,
the limited literature on effective responses to justice-involved offenders with severe and persistent
mental illness also has been documented. Further research is particularly necessary in policing and
corrections – the components of the system facing challenges in terms of responding effectively in
situations involving PwMI in their care (Cross et al., 2014; DeMatteo et al., 2013; Lucas, 2016).

Police as primary gatekeepers

The gate-keeping function police serve for PwMI in crisis situations has become more pronounced since
deinstitutionalization of state hospitals in the U.S. and abroad (Lamb & Weinberger, 2014), and the sub-
sequent inability of community-based treatment providers to service the growing population of PwMI
(International Association of Chiefs of Police, 2010; Slate et al., 2013). State statutes that prescribe more
rigorous criteria for civil commitment to state hospitals have also raised the visibility of PwMI. Lack
of community support for PwMI and stigma, coupled with inadequate dispatch training and policies
on best practices for navigating police encounters for PwMI, can result in greater use of incarceration
for PwMI for minor offenses (International Association of Chiefs of Police, 2010; Slate et al., 2013).

With increasing interactions between police and PwMI, current programs seek to provide pre-book-
ing diversion options that may address underlying issues and/or needs related to PwMI more effec-
tively. Programs and their variations fall under three categories: (a) mental health-based specialized
mental health responses; (b) police-based specialized mental health responses; and (c) police-based
specialized police responses (Slate et al., 2013). This study is focused on CIT as a specialized police
response in one jurisdiction.


CIT training

The CIT model was designed to improve police response to PwMI. It is the most widely adopted
specialized police-based training program in the United States (Gostomski, 2012; Watson, Ottati,
Draine, & Morabito, 2011). It has been implemented in Australia, Liberia, New Zealand, Canada,
and the United Kingdom (Hartford et al., 2006; Kane, Evans, & Shokraneh, 2017; Kohrt et al., 2015;
NSW Police Force, 2014). The CIT model originally was conceived in the aftermath of a 1987 incident
in Memphis, Tennessee that resulted in Joseph Dewayne Robinson – a man with a history of mental
health and substance abuse issues – being fatally shot during a crisis encounter. When police arrived
at the scene, Robinson was wielding a knife and appeared to be cutting himself. After failing to desist
and release the weapon, Robinson allegedly began to approach police and was subsequently shot eight
times (Heilbrun et al., 2012). In response, a community task force comprised of law enforcement,
community mental health providers, addiction professionals, and consumer advocates was established.
The members collaborated and designed the CIT model (Heilbrun et al., 2012; Watson et al., 2011).
Prior to implementation of CIT, most police departments, including the Memphis Police Department,
were only offering a few hours of academy-based training in crisis intervention (Gostomski, 2012;
Pearson, 2014).

Current estimates suggest that there are approximately 3000 CIT programs in the United States
(Taheri, 2016; University of Memphis, n.d.). CIT implementation globally has also been underway,
and evaluation research has been published (Kohrt et al., 2015; Taheri, 2016). Objectives of the original
Memphis CIT model include: (a) advanced training, (b) immediate crisis response, (c) safety of officer
and consumer, and (d) proper care for persons in crisis (Pennsylvania Mental Health & Justice Center
for Excellence, 2013). Police agencies that have implemented CIT report positive results (DeMatteo
et al., 2013; Pearson, 2014; Steadman, Deane, Borum, & Morrissey, 2000).

Under the Memphis model, patrol officers complete a one-time, 40-h (full week) training curricu-
lum in how to respond to citizens in crisis (Pennsylvania Mental Health & Justice Center for Excellence,
2013). Various training modules are facilitated and delivered by law enforcement personnel, mental
health professionals, family and consumer advocates, and experts in related fields. As presented in
Table 1, training sessions cover topics including: (a) signs and symptoms of mental illness, (b) types
of psychotropic medications, (c) de-escalation techniques, and (d) interaction with PwMI who are
not currently in crisis. Two essential elements of the model are officer training and the development
and maintenance of criminal justice-mental health partnerships (CIT International, 2011). Successful
implementation of training elements should produce changes in officers’ (a) attitudes (i.e., decreased
social distance from PwMI, confidence when responding to calls involving a mental health crisis, and
responsiveness regarding recognizing the appropriate treatment), (b) knowledge (i.e., of the origins
and effects of mental illness and the available resources/services in the jurisdiction), and (c) skills
in de-escalation techniques for crisis situations. The model purports to affect subsequent behavior
of trained officers when encountering PwMI (i.e., reduction of injuries to consumer and officer and
increased diversion) (CIT International, 2011; Cross et al., 2014).

CIT is more than training. One of its most important design and implementation factors is collab-
oration and shared knowledge of community resources (Slate et al., 2013). In addition to the inter-
section of mental health and criminal justice systems, many systems share clients, including public
welfare, veterans’ affairs/services, substance abuse, and foster care or dependency. Developing strong
partnerships between stakeholders allow forensic-based case managers, re-entry coordinators, and
first responders to make referrals to community providers (e.g., mental health and drug and alcohol).
Collaboration among parties involved with a consumer of mental health services can ensure proper
services are being implemented, save resources and funding, and provide on-going evaluations. Due
to limitations in available data, this element of CIT is not examined in the present study.

Consistent with its multi-systemic theme, most CIT law enforcement officials confer with com-
munity stakeholders when planning and delivering training. Involved stakeholders often include: (a)
substance abuse and mental health service providers, (b) advocates in the community, (c) PwMI, and





































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(d) family members of PwMI (Pennsylvania Mental Health & Justice Center for Excellence, 2013).
Attendees are a diverse group of first responders and include (a) corrections officers, (b) school resource
officers, (c) police and deputy sheriffs, (d) public safety dispatchers, (e) 911 operators, and (f) medical
and mental health professionals. Most trainees are self-selected volunteers or recommended by their
departments (Thompson & Borum, 2006). An increasing number of jurisdictions require recruits
and veterans to complete the full 40 h of CIT training. Consequently, more valid observations will be
possible in future analyses of the program (Cross et al., 2014; Pennsylvania Mental Health & Justice
Center for Excellence, 2013).

For CIT based on the Memphis model, the training curriculum may vary depending on the resources
available in the community. For example, in some areas (such as the one under study), trainees have
the opportunity to make a site visit to a mental health or other specialty court (e.g., drug court) as
part of the training. In more rural areas without similar programs, the agenda differs and issues facing
that community, such as substance abuse, might be emphasized.

Effectiveness of CIT

CIT programs are successful in (a) improving understanding of signs and symptoms of mental illness,
(b) reducing stigma and negative attitudes toward PwMI, and (c) increasing the number of positive
police interactions with PwMI overall (e.g., Compton et al., 2006; Wells & Schafer, 2006). For exam-
ple, Compton and colleagues (2006) administered a pre-posttest survey to police officers prior to and
directly following a 40-h CIT training to determine changes in officers’ knowledge, attitudes, and social
distance, particularly related to persons with schizophrenia. In their study, 159 officers completed the
surveys, and findings revealed that officers reported a decline in stigmatizing attitudes toward persons
with schizophrenia, including improved attitudes regarding their levels of aggressive behavior (i.e.,
potential threat). Officers also indicated enhanced knowledge about the disorder, improved attitudes
in support of local treatment programs for persons with schizophrenia, and a decreased desire for
social distance from persons with schizophrenia.

CIT also appears to be effective in diverting PwMI to mental health treatment as opposed to
processing in the justice system. Watson et al. (2011) examined data from 112 officers within four
contextually distinct (i.e., populations and availability of mental health resources were clearly differ-
ent) Chicago police districts where about half the officers were CIT trained with the Memphis model.
On four occasions following their training, officers were asked a series of questions to measure the
effectiveness of CIT training. Findings indicated that CIT trained officers in areas with many mental
health service providers directed a higher number of PwMI to services rather than making an arrest.
In districts where few alternative resources existed, there was little difference between CIT and non-
CIT trained officers.

Studies on CIT programs outside the U.S. reveal similar findings For example, in one pre/post-test
evaluation of a CIT curriculum developed specifically to improve and strengthen collaboration between
law enforcement and mental health service providers in Liberia, Kohrt and colleagues (2015) found
a significant increase in knowledge of and positive attitudes toward PwMI, and a significant decrease
in social distance reported by officers who completed an adapted 3-day version of CIT. Similar to
Watson et al. (2011), Liberian police officers were more likely to divert people to treatment instead
of jail after the training.

Other research suggests that CIT has (a) reduced arrests (e.g., Steadman et al., 2000); (b) increased
the number of mental health related calls identified (e.g., Teller, Munetz, Gil, & Ritter, 2006); and, (c)
facilitated diversion to mental health treatment services (Compton, Bahora, Watson, & Oliva, 2008;
Heilbrun et al., 2012; Lattimore, Broner, Sherman, Frisman, & Shafer, 2003). Findings are incon-
clusive regarding improvements in overall public safety, which refers to reductions in use of force
by and against police and resulting injuries to consumers and/or responding officer(s) (Morabito &
Socia, 2015; Taheri, 2016). Cowell, Broner, and Dupont (2004) reported that PwMI diverted through
CIT indicate a greater reduction in symptoms related to their mental illness, although there was no


significant reduction in the likelihood of reoffending. They found that PwMI diverted from the system
through a pre-booking diversion program (e.g., CIT) utilized mental health services in the 12 months
following the diversion.

Much of what has been measured regarding CIT effectiveness has been attitudinal, and overwhelm-
ingly, there is little data pertaining to changes in police behavior. The Franz and Borum (2010) study
filled this gap. Using CIT-encounter tracking forms from nine police agencies in a large, primarily
urban, county in central Florida for five years (2001–2005), they examined dispositional data from
1,539 encounters between CIT-trained officers and PwMI. Specifically, they compared the number
of arrests vs. ‘prevented’ arrests (diverted cases that officers reported would have likely resulted in an
arrest prior to their CIT training). Findings indicated that CIT prevented a substantial number of
PwMI from being arrested while in crisis (i.e., only 3% of CIT calls resulted in an arrest, and 19% of
CIT calls examined would have resulted in arrest prior to the officer being trained in CIT – all other
calls resulted in diversion to mental health referrals and/or services).

Purpose of this study

This study’s primary aim is to update the current literature by describing specific elements of encoun-
ters between CIT-trained police and citizens experiencing mental health crises in one jurisdiction in
Florida. Particularly, the present study extends the Franz and Borum (2010) research by examining
the characteristics of cases/consumers, the outcomes/dispositions, and the characteristics of diversion
cases in south Florida in which officers reported that they would have taken the consumer to jail if
they did not receive CIT (i.e., ‘prevented’ arrests). Furthermore, this study attempts to help address a
gap with research that collects and examines data specific to law enforcement encounters with PwMI
(Morabito & Socia, 2015). The research questions guiding this study are:

(1) What are the outcomes/case dispositions of CIT encounters in this jurisdiction? How do
the outcomes compare with those from previous research conducted in Florida (i.e., Franz
& Borum, 2010)?

(2) What are the characteristics of the jurisdiction’s cases/consumers being diverted?
(3) How do police perceive they would have responded to the situation prior to CIT; what are

the factors influencing their perception?

Based upon these questions and published research, we anticipated a high-level of diversion from
the system.


The research approach was a descriptive case-study design of one jurisdiction comprised of a four-
county area in south Florida. The decision to deploy CIT-responders is typically made by the dispatch-
ers, although others may request officers specifically trained in crisis intervention or de-escalation
with special populations. These data include a sample of police encounters in which the dispatcher
radioed for CIT-trained police to respond to a situation that may have involved a PwMI in crisis. The
dispositions of encounters between CIT officers and PwMI were examined as a means of estimating
the number of cases diverted to mental health evaluation and treatment vs. arrest. Although unable
to obtain access to additional cases from the jurisdiction to demonstrate officers’ pre-CIT arrest dis-
positional outcomes (or cases in which a specialized response was not required) to examine differ-
ences in dispositions, the results can inform the field by showing how some programs are operating

The data-set included CIT calls from four different law enforcement agencies in the jurisdiction
from 2007 to 2011.2 Official self-reported behavioral health data for 2008–2010 at the jurisdiction-level
indicate that an estimated 4.8% of adults (18 and older) had been diagnosed with serious mental illness3


during the year prior to the survey (SAMHSA, 2014). With respect to suicide ideation, estimates show
that approximately 4% of adults had contemplated suicide seriously during the same period. For this
study, all data are analyzed and reported in aggregate form.

CIT in selected jurisdiction

The jurisdiction is part of the Florida CIT Coalition that was established in 2004. The Coalition con-
sists of stakeholders from diverse counties whose goal is to develop consensus on CIT for the State of
Florida to ensure that the training can achieve maximum effectiveness. According to the Florida CIT
Program Model,4 essential elements based on the original Memphis Model must be included in the
curriculum to maintain fidelity to achieve effective training outcomes. Original Coalition members
included representatives from mental health providers, law enforcement agencies, and advocates from
13 counties, including Broward and Dade. As of February 2015, the Coalition members represented
23 counties – including the regional site for this study (Florida CIT, 2015).

Key stakeholders
The University of Memphis (n.d.), CIT Center, recommends developing a steering committee with
members from various agencies who advocate across systems of care in the community (Dupont,
Cochran, & Pillsbury, 2007). The group’s primary responsibility is to assist with the planning stages of
the program, and to sustain the core of the training from its inception. The CIT Steering Committee is
comprised of mental health and criminal justice representatives from the jurisdiction, including law
enforcement (county, city, and regional police), corrections officials (jail director of the largest county,
and Chief Executive Officer and supervisory staff from secure forensic hospitals/detention centers),
state-level mental health and substance abuse personnel from the local Department of Children and
Families, and the National Alliance on Mental Illness (NAMI) or other advocates. The multiagency
characteristic reflects one of the three core elements of a successful CIT program: The development
of ongoing partnerships (Dupont et al., 2007). One Steering Committee member is designated as the
CIT-Coordinator whose primary task is to coordinate presenters, location, and curriculum updates
for each 40-h (full week) program. The Coordinator also is the lead facilitator for the training; and
typically, the other members of the Committee are involved only in planning (though some facilitate
training modules periodically and/or attend graduation on the last day of training).

Participants and data collection

In this jurisdiction, training is offered three times per year, and each training cohort group has approx-
imately 30–35 attendees. During the years for this study (2007–2011), approximately 650 police officers
were trained in CIT using the Memphis Model. As noted and consistent with the Florida CIT Program
Core Elements, trainees (mostly patrol officers and corrections staff) are selected from candidates who
have either volunteered or are recommended (some mandated) by their departments.

The departments requested that CIT-trained police officers who respond to mental health-related
crisis calls complete documentation forms immediately following any encounter. The dispatcher, adher-
ing to the jurisdiction’s protocol, determines whether a call is dispatched as involving a mental health
or psychiatric crisis. However, the data collection instrument utilized did not contain this information.
There were 438 documented encounters reported by four departments with approximately 113 CIT
trained officers (21 out of 438 documented encounters did not include legible officer names or badge
numbers) from 2007 to 2011. Of these 438, 33 encounters were excluded from the analysis due to
missing variables; a total of 405 encounters comprise this study (N = 405).



Officers trained in CIT complete the target jurisdiction’s Crisis Intervention Tracking Form (CIF;
Appendix 1) following encounters. Department administrators distribute forms to CIT-officers only,
and they are requested to complete one for each encounter. However, there is no way to determine
officer compliance. Therefore, it is more accurately described as ‘voluntary’. The CIF contains 11
sections which officers complete. The first section asks the officer for basic demographic information
about the consumer and encounter details. The second section includes 10 checkboxes for officers to
indicate the phrases describing the nature of the incident. In the third section, the officer enters infor-
mation about the consumer’s use or possession of a weapon. Sections four and five solicit information
about officer’s prior contact with the consumer and evidence of drug and/or alcohol intoxication. In
sections six and seven, the officer provides information specific to elements of the consumer’s mental
health status (i.e., mental health referral and medication compliance). For section eight, the officer
indicates all outcomes of the encounter. The ninth section requests information about use of force
(i.e., whether force was employed, and, if yes, whether any resulting injuries to the officer and/or the
subject occurred). The tenth and eleventh sections are specific to the CIT-officers – questions aimed
at assessing the officers’ perceptions and asking them to reflect on whether they would have taken the
consumer to jail prior to CIT and soliciting identifying information, such as their name and badge
number. Notably, officers complete the forms post-encounter, and aside from factual data presented by
dispatch and at the scene of an incident (e.g., consumer name, date of birth, diagnosis – if reported),
responses reflect officers’ perceptions and discretion.

Study respondents indicated whether force was used during an encounter by checking a box labeled
‘yes’ or ‘no’ For this data-set, ‘use of force’ is defined as any type of force along the continuum (from
officer presence to lethal force) (National Institute of Justice, 2009). Subsequent injury because of use
of force was also indicated using a binary variable (i.e., officers were asked to indicate injuries to self
or subjects via a checkbox option of ‘yes’ or ‘no’ for each), thereby limiting information on injuries
incurred during encounters.


Characteristics of cases/outcomes

Of 405 documented CIT encounters, 85% (n = 346) of cases were diverted from the system. Descriptive
analysis and bivariate patterns reveal (see Table 2) that among the diversion cases, about 12% (n = 47)
of cases were ‘prevented arrests’ (i.e., officers reported they would have taken the consumer to jail if
they did not receive CIT), and only 1% (n = 5) of encounters resulted in an arrest. The small number
of cases that resulted in arrest or ‘no action’ precluded statistical inferences to be drawn Of the 47
‘prevented arrests’ cases, officers indicated which charges they might have utilized. Responses varied,
but more frequently involved battery charges (n = 17), disruption to the public order (public intoxica-
tion, disorderly conduct, misuse of 911) (n = 14), and resisting arrest (n = 4). The most serious charges
considered for imposition were aggravated assault (n = 1), burglary attempt (n = 1), and grand theft
(n = 1). These data suggest that CIT may facilitate officers making more referrals to agencies rather
than formally processing individuals through the system.

Table 2 indicates that all subjects who encountered CIT-trained officers were relatively evenly
distributed by gender, with male clients representing 55.9%, and female clients representing 44.1% of
the sample. Officers identified the largest percentage as White (83.1%); 14.6% as Black, and 2.3% as
Hispanic. It is worth noting that a trend in reporting race data on surveys demonstrates that the race
of Hispanics or Latinos is frequently reported as White (Cohn, 2014; Ennis, Rios-Vargas, & Albert,
2011; Rios, Romero, & Ramirez, 2014). The sample ranged from age 12 and under to 65 and over. The
two largest categories were between the ages of 25–29 and 40–44, and each group represented 12.3%
of the sample Together, these two groups comprised 25% of the citizens involved in CIT encounters.


The mean age was 35.8 years of age. Overall, youth (12 and under) and older individuals (60 and older)
were less likely to have an encounter with CIT-trained police.

Either the subject or someone who knew him/her and was on the scene reported the diagnosis.
In three quarters of overall encounters (75%), the officer noted the ‘diagnosis’ was unknown; and in
the remaining 25% of cases where the diagnosis was known, it was not always categorized. Of the
diagnoses reported, bipolar disorder had the highest frequency at 7.6%. Other categories included
Depression, Schizophrenia, PTSD, and ‘Other’.

In terms of incidence time, a slightly larger percentage of cases occurred between the hours of 12
and 6 pm (38.5%), followed by 32.8% of cases occurring between 6 pm and midnight. By contrast,
for these cases, mental health crisis calls were least likely to be reported in the study jurisdiction after

Table 2. Bivariate patterns for four-category case disposition variable by demographic characteristics and incidence characteristics.a

notes: Bold numbers = values that are above the overall mean of total for a particular variable.
aall numbers are percentages except for mean age.
bdiversion cases with which officers reported that they would have taken the consumer to jail prior to cit.
csome cases reported more than one category for ‘nature of incident’.


Case disposition

Total (N = 405) No action (N = 7) Arrest (N = 5)
Prevented arrestsb

(N = 47)
(N = 346)

Demographic characteristics
White 83.1 57.1 60.0 79.2 84.4
Black 14.6 42.9 40.0 16.7 13.5
hispanic 2.3 0.0 0.0 4.2 2.1
Male 55.9 42.9 60.0 66.7 54.8
Female 44.1 57.1 40.0 33.3 45.2
Age 35.8 46.9 30.0 33.9 35.9
Unknown 75.1 42.9 80.0 62.5 77.2
Bipolar disorder 7.6 0.0 20.0 16.7 6.4
depression 6.6 0.0 0.0 8.3 6.6
schizophrenia 3.7 42.9 0.0 0.0 3.4
Ptsd 0.9 14.3 0.0 2.1 0.5
other 6.2 0.0 0.0 10.4 5.8

Incident characteristics
Incident occurrence time
after 12am–6am 10.4 0.0 20.0 10.6 10.4
after 6am–12pm 18.3 42.9 20.0 10.6 18.8
after 12pm–6pm 38.5 14.3 60.0 40.4 38.4
after 6pm–12am 32.8 42.9 0.0 38.3 32.4
Nature of incident
disorderly/disruptive behavior 4.2 14.3 33.3 2.1 4.0
nuisance 0.2 14.3 0.0 0.0 0.0
threats or Violence to others 3.0 0.0 0.0 6.4 2.7
theft/Property crime 0.5 0.0 0.0 2.1 0.3
neglect of self-care 3.9 0.0 0.0 2.1 4.3
Public intoxication 2.3 0.0 0.0 4.3 2.1
drug-related offense 0.5 0.0 0.0 0.0 0.5
suicide threat/attempt 45.8 0.0 0.0 4.3 52.3
other 7.2 57.1 0.0 4.3 6.7
Multiple offensesc 32.4 14.3 66.7 74.5 27.2
Yes 13.5 0.0 20.0 16.7 13.2
no 84.0 100.0 80.0 77.1 84.7
Unknown 2.5 0.0 0.0 6.3 2.1
 Yes 38.6 0.0 20.0 43.8 38.9
no 61.4 100.0 80.0 56.2 61.1


12:00 am up until 12:00 pm (After 12–6am = 10.4%; Between 6am-12 pm = 18.3%). With respect to
nature of the incident, 45.8% of documented encounters were a result of a reported suicide threat/
attempt. The second most frequently documented reason was some combination of incident charac-
teristics – coded as ‘multiple offenses’ – making up 32.4% of encounters. Conversely, cases involving
theft/property crime represented less than 1% of encounters. In the majority of incidents (84%), no
weapon was involved, and officers reported drug or alcohol involvement in 38.6% of the cases. Again,
the nature of the incident reported for each encounter is determined by the responding CIT officer.

Case dispositions of diverted CIT encounters

As presented in Table 2, we disaggregated the cases into four categories: arrest, no action, diversion,
and diversion but jail prior to CIT. Using the data sheets, officers were asked to reflect retrospectively
and indicate whether they would have taken the consumer to jail prior to receiving CIT by selecting
‘yes’ or ‘no’. Of the 393 diverted cases, in 346 cases, officers indicated that they would have diverted
even prior to CIT, while in the remaining 47 cases officers reported they diverted because of CIT. As
indicated in Table 2, of 346 cases diverted according to the reports (where arrest prior to CIT was
not considered), 84.4% of consumers were White and 13.5% were Black. For gender, the percentage
of male and female cases diverted were relatively evenly distributed, though males were diverted at a
slightly higher percentage (54.8% vs. 45.2%, respectively); however, the number was above the mean
for female cases in the overall sample. The average age of individuals diverted was 35.9.

For the variable, diagnosis, ‘unknown’ was the most frequently occurring value among cases with a
diversion outcome (77.2%). The value was slightly above the mean for cases representing an unknown
diagnosis in the sample. With respect to time of encounter, two periods, 12:00 pm–6:00 pm, and
6:00 pm–12:00 am (38.4% and 32.4%, respectively), occurred the most frequently under the diversion
outcome. The number of cases between 6:00 am and 12:00 pm was above the mean as reported for
the overall sample, but only slightly.

In relation to the association between nature of the incident and a diversion decision by the officer,
the most frequent type of incident was threat or attempted suicide (52.3% of cases), followed by mul-
tiple offending behaviors (27.2%). The value for suicide cases with a diversion outcome was above the
mean for cases representing suicidal ideation and behavior, indicating (as expected) that officers were
more likely to divert consumers when the call was related to a threat or attempted suicide.

When no weapon was present, the officer, in 84.7% of cases, diverted individuals. When alcohol
or drugs were involved, officers diverted the individual in 38.9% of the cases. The value of diversion
cases not involving weapons was slightly above the overall mean for the weapons variable; and for
drugs and alcohol, the percentage of cases in which there was a diversion outcome was only slightly
above the sample mean

Diversion but Jail Prior to CIT
Of 47 cases in which police indicated that they would have made an arrest prior to the training pro-
gram, respondents reported that they would have arrested and placed in jail 16.7% of Black and 4.2%
of Hispanic individuals. Both values are above the overall mean for the race variable. For gender, the
percentage of males in which an arrest would have been made prior to CIT is higher than females
(66.7% vs. 33.3%, respectively), and it is also above the mean for cases representing males in the sample.
The average age among individuals with a jail outcome was 33.9.

Finally, of 47 cases in which officers indicated that they would have made an arrest prior to CIT,
a weapon was present in 16.7% of cases, and alcohol or drugs were reported in 43.8% of these cases.
The value of cases involving weapons was above the overall mean for the weapons variable in this
sample. Furthermore, the value of the ‘unknown’ weapon category (6.3%) was also above the mean for
the weapons variable. For the drugs and alcohol variable, in which police indicated an arrest would
have been made when drug or alcohol involvement was reported, 43.8% was above the mean in the
overall sample.


For the diagnosis variable, the most frequent category under arrest prior to CIT is ‘unknown’
(62.5%); and the least occurring value is schizophrenia (0.0%). Values for categories of bipolar (16.7%),
depression (8.3%), PTSD (2.1%), and ‘Other’ (10.4) were above the mean for cases representing these
diagnoses in the sample. This finding is corroborated with logistic regression analysis. As demonstrated
in Table 3, none of the demographic characteristics is statistically significant, and considering both
the statistical significance as well as the effect size, there is no race effect. Although some variables
are statistically not significant, their effect sizes are noteworthy and may warrant future examination
[(i.e., depression (Exp(b) = 2.005), PTSD (16.081), and theft/property crime (2.196)]. Moreover, officers
in the current study were almost 16 times more likely to divert cases involving PTSD after the CIT
training, and approximately twice as likely for cases involving depression or theft/property crimes.

The time zone variable is the most robust in differentiating this disposition outcome. And the nature
of the incident also shows a negative effect in the model. Specifically, even without CIT, officers are
more likely to divert cases occurring between 6:00 am and 12:00 pm, and suicide threat/attempt cases.
Thus, CIT has little impact on these cases.


Consistent with Franz and Borum (2010), the majority of encounters documented by CIT-trained
officers resulted in diversion rather than arrest. Whether this is the direct result of CIT is unclear, but it
suggests the outcome one would expect after the training. At the very least, it appears that CIT officers
have knowledge of diversion options and they often make referrals instead of arrests. There were other
study findings, however, that may add to the literature on CIT, and encourage future studies. First,

Table 3. logistic model by case disposition (N = 393).

notes: Model chi-square = 73.532***; cox-snell R2 = 0.183; nagelkerke R2 = 356.
Male, White, unknown diagnosis, after 6 pm–12am, and multiple offenses are the reference category for each variable.
adiversion cases with which officers reported that they would have taken the consumer to jail prior to cit.
*p < 0.05; **p < 0.001.


Jail prior to CITa vs. Diversion

b SE EXP (b)
Gender −0.335 0.391 0.715
Black −0.124 0.536 0.884
hispanic −0.125 0.331 0.883
age −0.007 0.012 0.993
Bipolar disorder 0.36 0.591 1.434
depression 0.696 0.701 2.005
schizophrenia −19.124 7.011 0.000
Ptsd 2.774 1.563 16.018
other 0.221 0.636 1.247
incident occurrence time
after 12am–6am −0.497 0.659 0.608
after 6am–12pm −1.568* 0.674 0.208
after 12pm–6pm −0.314 0.43 0.73
Nature of incident
disorderly/disruptive Behavior −1.622 1.102 0.197
threats or Violence to others −0.413 0.872 0.662
theft/Property crime 0.787 1.523 2.196
neglect of self-care −1.725 1.11 0.178
Public intoxication −0.501 0.847 0.606
drug-related offense −19.847 4.982 0.000
suicide threat/attempt −3.790*** 0.772 0.023
other −1.335 0.799 0.263
Weapon −0.495 0.525 0.610
drug/alcohol 0.185 0.421 1.203
constant 0.131 0.718 1.140


similar to Franz and Borum (2010) this study found a substantial proportion of ‘prevented arrests’
when officers were CIT-trained. While acknowledging this as an improvement, it is important to note
that in 88% of the cases, officers reported that they would have taken the same actions even if they
had not completed CIT training.

CIT has been promoted as a way to address stigmatization of mental illness and reduce the num-
ber of criminal justice-related encounters that result in arrest or injury to all parties involved (CIT
International, 2011). The desired outcome of these encounters should be successful diversion to quali-
fied mental health services. Training does not guarantee that first responders will become advocates and
have an understanding of mental health issues, but the emerging research indicates that CIT trained
individuals may be less likely to have stigmatizing attitudes about persons with mental health issues,
more likely to understand mental health needs, and more likely to divert individuals from further
immersion in the criminal justice system than those who are not trained (Cross et al., 2014). For the
47 cases, these data suggest that further immersion (use of jail) did not occur.

Additional evaluation of the CIT program that may translate into further development with CIT
and/or other training opportunities or in collaboration with other regions across the state is needed.
The goals of CIT include officer safety and diversion efforts to keep people with serious mental illness,
who do not need to be in the criminal justice system, in the community. The results suggest that CIT
should continue to be implemented and more robust research should be conducted as to its effective-
ness – particularly in the context of these overall program objectives.

Formal studies on the effectiveness of CIT suffer from methodological issues (Taheri, 2016). Specific
and pervasive difficulties include: (a) small sample sizes, (b) lack of a comparison group, and (c)
selection effects which render conclusive findings and differences observed between groups prob-
lematic (Cross et al., 2014; Engel, 2015). As noted, this study has limitations. Specifically, there was
no comparison group and no data available prior to the implementation of CIT. Further, because
participation in CIT for most officers is voluntary, there is a potential for selection bias, which could
influence outcomes. Variation on these and other factors would have facilitated more sophisticated
analyses and possible application to a wider audience.

Given recent attention on mental health issues in the criminal justice system, research involving
police-citizen encounters has important policy implications. First, national data indicating that jails
and prisons incarcerate disproportionate numbers of individuals with severe and persistent mental
illness who are without proper treatment or care (Fellner, 2015; International Association of Chiefs
of Police, 2010; National Alliance on Mental Illness, 2013a; Subramanian, Delaney, Roberts, Fishman,
& McGarry, 2015; Torrey, Kennard, Eslinger, Lamb, & Pavle, 2010) is discussed with greater urgency.
Thus, the finding that the large majority of CIT encounters in this particular jurisdiction resulted in
diversion rather than arrest, even if temporarily, is encouraging. It suggests that training is occurring
in the region and it is effective in terms of increasing awareness and knowledge pertaining to mental
health and available resources.

Previous research demonstrated support for the idea that police perceive CIT training to be effective
when it is accompanied by a supportive community with accessible resources and positive relationships
between law enforcement and community human service providers (Cross et al., 2014; Watson et al.,
2011). To gauge the impact of such factors in the future, an assessment of police-provider relationships,
from the perspectives of providers, law enforcement officials, and consumers would be beneficial. In
studying CIT, researchers have designed questions that measure factors pre and post-training after
having encountered consumers: Officers’ personal familiarity with mental illness; perceptions of the
mental health services available in the area; their own skills for responding to persons with mental
illnesses; their perception of the CIT program; and district organizational support of the CIT program
(Compton et al., 2006). Finally, it is recommended that data evaluating consumer perspectives be
incorporated in evaluating the overall effect of CIT training.

As noted, participants were not evaluated regarding their attitudes and behaviors prior to training.
Furthermore, the study included only four of nine police localities in this region. The five localities
that did not submit data might have had different outcomes. Nonetheless, the study is relevant; and


it is useful to consider and discuss policy and practice regarding more successful police encounters
with PwMI across systems.

Future directions

There are several recommendations for future research. First, once appropriate measurement tools
are constructed, researchers should survey a diverse sample of departments to further explore the
relationship between CIT and positive outcomes in terms of diversion. Similarly, because it is not clear
whether individual officers differ with respect to the method by which they received the training (i.e.,
volunteered to participate in the training or assigned), their arrest (i.e., encounter) histories, or other
characteristics that might influence attitudes and behavior (e.g., race, age, years of service, level of
education, available community resources), the relationship should be further explored by comparing
outcomes between officers and other participants.

Second, future researchers might develop a tool that more directly measures the effectiveness of
CIT elevating the training from a best practice to an evidence-based practice. As noted, it is possible
that CIT training alone does not predict successful outcomes in terms of diverting consumers from
the criminal justice system. Earlier research indicates that other constructs, such as organizational
factors, mental health resources, and community characteristics, when considered with officer char-
acteristics, may provide a more comprehensive picture as to the way a community responds to PwMI
(Cross et al., 2014). However, measuring additional outcomes other than those that the jurisdiction is
already utilizing to obtain more information as to the influence of CIT training on individual officer
decision-making would be helpful.

Third, mental health issues affect people in every culture. CIT may have international applicability to
promote effective collaborations in a global context. In particular, the adaptive nature of the Memphis
model enables it to be applied to locations with varying needs and resources. Notwithstanding this
advantage, minimal attention has been focused on implementation of CIT internationally. Furthermore,
existing research – mostly conducted in the U.S. – fails to compare outcomes between communities
within geographic regions (e.g., rural vs. urban communities). Perhaps future research could include
comparative analyses of outcomes between the U.S.-based CIT program and other versions that have
been implemented successfully in other countries (e.g., Woods, Leidl, Butler, Stonechild, & Luimes,
2017 [Police and Crisis Team]). More evidence to understand which approaches communities can adopt
to address barriers to successful implementation is essential – both internationally and domestically.

1. The ‘Memphis Model’ of CIT training, while the original format and the most replicated, is not the only model

used across the United States (Cross et al., 2014).
2. Nine police departments in the jurisdiction participate in the area’s Crisis Intervention Team (Florida CIT,

2015); however, for reasons unknown to the researchers, five departments did not submit completed data
sheets for analysis.

3. The Substance Abuse and Mental Health Services Administration (SAMHSA) defines serious mental illness:
‘Serious mental illness is defined as having a diagnosable mental, behavioral, or emotional disorder, other than
a developmental or substance use disorder, that met the criteria found in the 4th edition of the Diagnostic and
Statistical Manual of Mental Disorders (DSM-IV) and resulted in serious functional impairment. (SAMHSA,
Center for Behavioral Health Statistics and Quality, National Survey on Drug Use and Health, 2008, 2009, and
2010 (Revised March 2012).

4. The Florida CIT Coalition has published a document that outlines the ‘Florida CIT Program Model’ (http:// Core elements of the program
(The Model) are aligned with those set forth in the original Memphis Model, and include policies and procedures
related to using a generalist/specialist model; selection of CIT officers after training; a recognizable CIT pin
worn by trained officers; size of CIT force; selecting a CIT Coordinator; selecting a mental health/substance
abuse Coordinator; representation of mental health advocacy organizations; mental health and substance abuse
systems; roles and responsibilities of law enforcement and service providers in the system of care; frequency of
training and selection of trainers/presenters; refresher courses and abbreviated versions of training for other


community stakeholders; methods for collecting data on program outcomes; ongoing meetings and support,
feedback; and, recognition of officers (Florida CIT, 2015).

Disclosure statement
No potential conflict of interest was reported by the authors.

Notes on contributors
Michele P. Bratina, PhD, is an Assistant Professor in the Criminal Justice Department at West Chester University in
West Chester, Pennsylvania.  Previously, she was the Forensic and Children’s Mental Health Coordinator for the Florida
Department of Children and Families in the 19th Judicial Circuit. Dr Bratina is the Immediate Past President of the
Northeastern Association of Criminal Justice Sciences (NEACJS). She is also a three-time recipient of the ACJS/Sage
Junior Faculty Professional Development Teaching Award.  Her research interests and publications include human
exploitation, criminological theory, race, social structure, ethnicity and crime, and forensic mental health. Dr. Bratina
has authored two books, Latino attitudes toward violence: The effect of Americanization (LFB Scholarly Publications,
2013), and Forensic mental health: Framing integrated solutions (Routledge-Taylor & Francis, 2017). She also has pub-
lications in the Journal of Criminal Justice Education, the Journal of Ethnicity in Criminal Justice, and the International
Journal of Police Science and Management.

Kelly M. Carrero, PhD, BCBA is an assistant professor in the Department of Psychology & Special Education at Texas A
& M University – Commerce. She earned her doctorate in special education with an emphasis on behavioral disorders at
the University of North Texas. Prior to entering academia, she served children from culturally and linguistically diverse
backgrounds identified with exceptionalities and behavioral health concerns in a variety of settings. Her research projects
serve as a vehicle for positive social change and advocacy for children identified with exceptionalities and challenging
behaviors (including Autism Spectrum Disorders). Specifically, she is interested in identifying (a) demographic dis-
parities in the special education evidence-base and provision of quality service delivery, (b) interventions that increase
access to social capital for children and families from diverse backgrounds who are affected by communicative and
behavioral health disorders, and (c) culturally responsive practices in research and service delivery. She serves her
profession as a reviewer for several journals and an active member of the Council for Exceptional Children (CEC) and
its respective divisions.

Bitna Kim is a professor in the Department of Criminology and Criminal Justice at Indiana University of Pennsylvania
(IUP). She received her PhD in the college of criminal justice at Sam Houston State University, Texas. Her specific areas
of interest include a systemic review of the interventions with Meta Analysis, police-community corrections partner-
ships, and international/comparative criminal justice. She has published widely, including recent articles in Crime and
Delinquency, Journal of Criminal Justice, Trauma, Violence & Abuse, Police Quarterly, Policing, Policing and Society,
Federal Probation, Prison Journal, Criminal Justice and Behavior, Deviant behavior, Asian Journal of Criminology, and
Journal of Criminal Justice Education.

Alida V. Merlo is a professor of Criminology and Criminal Justice at Indiana University of Pennsylvania. She received
her PhD in Sociology from Fordham University. Her research interests are juvenile justice, criminal justice policy, and
women and the law. She is the co-author with Peter Benekos of Reaffirming Juvenile Justice: From Gault to Montgomery
(2018), and The Juvenile Justice System: Delinquency, Processing, and the Law, 9th Edition (2019). Her recent research
has been published in the Criminal Justice Policy Review, the Journal of Criminal Justice Education, and the Asian Journal
of Criminology.

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Appendix 1. Crisis Intervention Tracking Form (CIF) from one judicial circuit in south
Florida, 2014.

Crisis Intervention Form

Agency Case # ___________________________

Subjects Name: Date of Birth: Race: Sex:

Address: Arrival Time:/Completed

City: State: Zip: Phone:

Enrolled in Medical Security Program? Yes No Unknown

Call Dispatched Self-Initiated Referred By: ____________________ Other: ___________________

(if known) ___________________________________________________________________________

Nature of Incident (check all
that apply)
Disorderly/disruptive behavior

Neglect of self-care
Public Intoxication
Nuisance (loitering,
panhandling, Trespassing
Theft/other property crime
Drug-related offenses
Suicide threat or attempt
Threats or violence to others
No Information
Other / specify:

Did subject use/brandish a weapon?
Yes No Unknown
If yes –
Type of weapon (check all that
Knife Gun Other/specify:


Did subject threaten violence toward
another person?
Yes No Unknown
If “yes”, to whom? (relative, law
enforcement, stranger, Etc)

Did subject injure or attempt to
injure self?
Yes No

If Yes, how

Prior Contacts (check all that
Known person (from prior LEO
Yes No Unknown
Repeat call (within 24 hours
Yes No Unknown


Drug/Alcohol Involvement
Evidence of drug/alcohol
Yes No Unknown
If Yes –
Other Drug / specify:

Medication Compliance
Yes No Unknown
Specify if known:


Disposition (check all that apply)
No action taken/resolved on scent
On-scene crisis intervention
LEO notified case manager or

mental health center
Outpatient/case management

Transported to treatment facility
Baker Act Marchman Act
Arrested (if yes what charges)

Mental health referral Yes No

Use of Force
Did incident result in a use of force? Yes No
If yes, was there injury to the officer? Yes No
Was there injury to the subject? Yes No
Prior to CIT would you have taken this individual to jail Yes No

What would the charges have been?

Signature of Officer:

Print Officer Name:

Badge/ID #




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individual use.

  • Abstract
  • Statement of the problem
    • Police as primary gatekeepers
    • CIT training
    • Effectiveness of CIT
  • Purpose of this study
  • Method
    • CIT in selected jurisdiction
      • Key stakeholders
    • Participants and data collection
    • Instrumentation
  • Results
    • Characteristics of cases/outcomes
    • Case dispositions of diverted CIT encounters
      • Diversion but Jail Prior to CIT
  • Discussion
    • Future directions
  • Notes
  • Disclosure statement
  • Notes on contributors
  • References
  • Appendix 1. Crisis Intervention Tracking Form (CIF) from one judicial circuit in south Florida, 2014.

Poly-Victimization across Time in Juvenile Justice-Involved
Youth Receiving Behavioral Health Treatment
Fredrick Butcher , Krystel Tossone, Maureen Kishna, Jeff M. Kretschmar,
and Daniel J. Flannery

Jack, Joseph and Morton Mandel School of Applied Social Sciences, Case Western Reserve University,
Cleveland, Ohio, USA

Diversion and community-based treatment for juveniles involved in
the justice system is commonly used across jurisdictions as an alter-
native to incarceration. While evidence-based approaches to assess-
ment have allowed courts and treatment providers to more
accurately identify areas of treatment needs, studies have rarely
examined how needs change during treatment. One specific area of
treatment needs common to juvenile justice-involved youth is expo-
sure to violence (ETV). This study extended a previous cross-sectional
study that examined the importance of contextual location on con-
ceptualizing ETV by examining how ETV changes during treatment
for youth involved in the justice system. Using Latent Transition
Analyses, the data revealed three stable groups: (a) low ETV, (b)
Home and School ETV, and (c) Poly-Location ETV. While there was
little interclass movement, family history of mental health problems
predicted movement from the low ETV to another class. Results
confirm the importance of examining the contextual location of the
exposure, the relative stability of exposure during treatment, and the
importance of providing family-based behavioral health treatment for
JJI youth. These findings suggest that juvenile justice-involved youth
receiving community-based treatment are likely to continue to
experience ETV and that treatment approaches should address this
area of need.

Adolescent victimization;
juvenile justice; treatment;
diversion programs

The juvenile justice system has undergone some major changes over the past several
decades in an attempt to reduce the incarcerated population. These efforts have focused on
promoting diversion and community-based treatment, as well as identifying assessment
practices that match youth with services that best address the risks and needs of the
individual (Andrews & Bonta, 2010; Harvell et al., 2016). There has been much interest
around implementing the Risk Needs Responsivity (RNR) model in the juvenile justice
system, which is designed to identify criminogenic risk in youth and match the intensity of
treatment to specific needs to reduce risk for recidivism (Bonta & Andrews, 2007 Lipsey,
Howell, Kelly, Chapman, & Carver, 2010; Schwalbe, 2007). While much of the literature
has focused on how criminogenic risk changes during treatment and how to tailor
treatment to these changes, little attention is paid to changes in factors that affect
responsivity to treatment (Holloway, Cruise, Morin, Kaufman, & Steele, 2018; Taxman,

CONTACT Fredrick Butcher [email protected] Begun Center for Violence Prevention Research & Education, Jack,
Joseph and Morton Mandel School of Applied Social Sciences, Case Western Reserve University, 11402 Bellflower Road,
Cleveland, OH 44106-7167, USA.

2020, VOL. 15, NO. 1, 22–42

© 2019 Taylor & Francis Group, LLC

2014). One specific area of need that affects responsivity to treatment for many juvenile
justice-involved youth is exposure to violence (ETV). ETV is an issue that affects a large
proportion of youth in the juvenile justice system and may place youth at risk for future
justice system involvement (Wolff & Baglivio, 2016). Increasingly, juvenile justice systems
have moved toward trauma-informed treatment to reduce incarceration and prevent
recidivism (Griffin, Germain, & Wilkerson, 2012; Ko et al., 2008). However, to adequately
implement treatment in juvenile justice-involved youth, we must first understand how
youth experience violence exposure and how best to assess and identify issues
around ETV.

The current study builds on a previous study (see Butcher, Holmes, Kretschmar, &
Flannery, 2016), which used latent class analysis (LCA) to examine typologies of juvenile
justice-involved youth exposed to violence. Data reported in the 2016 study indicated that
the contextual location in which the violence exposure occurred is an important point of
assessment for juvenile justice-involved youth. Building off of this finding, the current
study uses latent transition analysis (LTA) to examine whether ETV based on the
typologies defined in the previous study changes during community-based treatment for
youth diverted from the juvenile justice system. Latent class modeling approaches are
often used to find identifiable subgroups with similar characteristics within populations
that are seemingly heterogeneous (McCutcheon, 1987). Subgroups refer to smaller clusters
of individuals with similarities on a given variable such as ETV. Identifying these clusters
can help tailor treatment approaches to target groups of youth who can most benefit from
a given type of treatment. While the focus of the first study was to better understand the
conceptualization of ETV, specifically around the importance of understanding where
exposure to violence occurred, the current study looks to extend this knowledge by
examining how ETV changes during treatment for youth diverted from the juvenile
justice system. In the previous study, we found subgroups of youth who generally had
low probabilities of violence exposure, those who had higher probabilities of violence
exposure in homes and school, and those who had high probabilities of ETV in the home,
school, and neighborhood (Butcher et al., 2016). Given these findings, the previous study
had clear implications around including questions that are location specific when screen-
ing and assessing juvenile justice-involved youth for exposure to violence. Based on these
conceptualizations from the previous study, the current study will examine whether youth
move from subgroup to subgroup (e.g. from elevated probabilities of ETV in all three
locations (home, school, and neighborhood) to low probabilities of violence exposure)
during treatment and the variables that may predict this movement.

Literature review

Across the United States, youth report ETV across multiple social contexts including the
home, school, and neighborhood (Butcher et al., 2016; Finkelhor, Turner, Shattuck, &
Hamby, 2013; Stein, Jaycox, Kataoka, Rhodes, & Vestal, 2003; Turner, Shattuck,
Finkelhor, & Hamby, 2016). Youth exposed to violence are at an increased risk of
internalizing and externalizing problems (Ford, Elhai, Connor, & Frueh, 2010; Singer,
Anglin, Song, & Lunghofer, 1995; Zarling et al., 2013). Particularly at risk are poly-
victims who are exposed to multiple types of violent victimization and experience
trauma symptomatology at a higher rate (Finkelhor, Ormrod, & Turner, 2007).


Conceptually, research on poly-victimization distinguishes itself from earlier conceptua-
lizations of ETV by focusing on the cumulative effects of multiple types of violence
exposure rather than the frequency of single types of exposure (Finkelhor et al., 2007).
Further adding to the complexity of conceptualizing violence exposure, poly-victims
experience violence both directly as victims and indirectly as witnesses and in locations
such as the home, schools, and neighborhoods (Finkelhor et al., 2013). While males and
females are at a similar level of risk for experiencing violence in neighborhoods and
schools (Singer et al., 1995), females report higher levels of victimization in the home
(Mitchell & Finkelhor, 2001). Factors such as family history of mental health issues also
raise the risks of experiencing ETV in the home (Finkelhor, Ormrod, Turner, & Holt,
2009; Tossone et al., 2015). Poly-victimization is associated with trauma symptomatol-
ogy which often negatively affects the ways in which youth can build and maintain
positive social relationships, ultimately changing a youth’s responsivity to treatment and
increasing their risk for recidivism (Butcher, Galanek, Kretschmar, & Flannery, 2015;
Holloway et al., 2018).

Widom’s (1989) work on the Cycle of Violence three decades ago has informed
research around the effects of children’s maltreatment and ETV. Children who are
exposed to violence are at a greater risk for perpetrating violence themselves (Maxfield
& Widom, 1996; Widom, 1989) mediated by factors that can help to protect youth from
the effects of ETV (Wright, Turanovic, O’Neal, Morse, & Booth, 2019). One theoretical
approach to explaining the process in which ETV in youth increases the risk for violence
perpetration is the social information-processing theory (Dodge, Bates, & Pettit, 1990;
Widom & Wilson, 2015). Youth who experience violence directly and indirectly are more
likely to misinterpret social cues and to respond aggressively (Dodge & Crick, 1990).
Social interactions are also nested in the context in which they occur and interpretation
and reaction to social cues takes into account the settings in which these interactions take
place (Lösel, Bliesener, & Bender, 2007).

While there is generally an extensive literature around the impact of ETV, there is still
considerable debate around conceptualizing ETV particularly as new and innovative meth-
odologies are developed. Recently, researchers have utilized mixture modeling, a person-
oriented analytical technique to classify youth based on their ETV and poly-victimization
(Aebi, Giger, Plattner, Metzke, & Steinhausen, 2014; Bender, Ferguson, Thompson, &
Langenderfer, 2014; Ford et al., 2010; Ford, Grasso, Hawke, & Chapman, 2013;
Kretschmar, Tossone, Butcher, & Flannery, 2017; Obsuth, Mueller-Johnson, Murray,
Ribeaud, & Eisner, 2017; Reid & Sullivan, 2009; Tossone et al., 2015). Mixture modeling
techniques including LCA are fairly familiar in violence research and can help researchers to
identify subgroups within a study population (Nurius & Macy, 2008; Swartout & Swartout,
2012). Identifying subgroups can help researchers and practitioners to make sense of data by
classifying youth into different groups based on the probability of experiencing a given
phenomenon. For example, one study found six subgroups of adolescents exposed to
violence, with four of these groups with a high likelihood of different types of polyvictimiza-
tion (e.g. polyvictimization in the community; Ford et al., 2010). Youth who were identified
as experiencing multiple types of abuse and assault are at a higher risk for delinquent
behaviors (Ford et al., 2010). Studies have consistently found between 3 (e.g. Aebi et al.,
2014; Charak et al., 2016) and 8 (Adams et al., 2016) distinguishable subgroups depending
on how ETV was measured with poly-victimization consistently found to be associated with


behavioral health problems (Adams et al., 2016; Burns, Lagdon, Boyda, & Armour, 2016;
Turner et al., 2016).

While previous studies incorporating LCA on ETV have consistently and accurately
identified subgroups of youth exposed to violence, few studies have examined the impor-
tance of contextual location in the study of poly-victimization. Often, ETV occurs in
multiple settings, which may be cumulative and particularly damaging as the threat of
victimization can become pervasive in these youth’s lives (Mrug & Windle, 2010). Several
studies have found subgroups of youth exposed to violence based on the contextual
location in which the incidents occur (Butcher et al., 2016; Turner et al., 2016).
Findings suggest that youth who experience ETV in multiple locations including the
home, school, and neighborhood, termed poly-location victims, have higher levels of
externalizing problems (Butcher et al., 2016). Conceptualizing ETV by contextual location
may be particularly important as there may be a differential impact of ETV on childhood
development based on the ecological context in which the violence occurs.

Bronfenbrenner’s (1974, 1994) work on the ecological models of human development
argues that the effect of interpersonal interactions depends on factors nested in the
environment. Specific to child maltreatment and violence exposure, Cicchetti and Lynch
(1993) proposed the ecological-transactional model which examines the complex hierar-
chy of risk and protective factors that affects a youth’s susceptibility to violence in the
home and community and its effects on child development. They argue that ETV at one
level (e.g. community) does not necessarily affect the risk of violence in the home.
However, the interaction of the risk and effects of violence in the community with the
risk and effects of violence exposure in the home is important in developmental outcomes
(Cicchetti & Lynch, 1993; Spano, Rivera, Vazsonyi, & Bolland, 2008).

Accurate assessment for ETV at intake and during treatment is of particular impor-
tance in juvenile justice-involved youth receiving community-based treatment. Studies
have found that in comparison with community samples, juvenile justice samples con-
sistently report higher prevalence rates of ETV (Ford, Hartman, Hawke, & Chapman,
2008; Wasserman & McReynolds, 2011; Wilson et al., 2013) with one study reporting that
as many as 90% experience violent victimization either directly or indirectly (Abram et al.,
2004). Females involved in the juvenile justice system are particularly at risk for poly-
victimization (Ford et al., 2013). As part of decarceration efforts in the juvenile justice
system, a number of states across the US have invested in strategies to divert youth from
the justice system by providing evidence-based treatment while keeping youth away from
costly and ineffective out of home placements (Harvell et al., 2016). Treating juvenile
justice-involved youth in the community can potentially mean that youth who are exposed
to violence in multiple settings continue to remain at risk for violence exposure during
treatment. While treatment approaches should address issues around ETV (Cohen,
Berliner, & Mannarino, 2000), treating juvenile justice-involved youth in nonresidential
settings presents a unique challenge. Given that these youth may be at continued risk of
experiencing violence during treatment, it is important that the research examines the
ways in which ETV changes for youth during treatment and the factors that predict this
change. Understanding ETV during treatment has strong implications for assessment, case
planning, and treatment for juvenile justice-involved youth.

While studies that have identified subgroups of youth exposed to violence have
important implications for understanding poly-victimization at a single time point, few


studies have examined how these subgroups change over time. This is particularly salient
for juvenile justice-involved youth who are undergoing treatment as ETV is associated
with factors that greatly affect an individual’s responsivity to treatment (Ford, Hawke,
Alessi, Ledgerwood, & Petry, 2007). Latent Transition Analyses (LTA) which is an exten-
sion of LCA for longitudinal data have generally found that youth in community samples
experience consistent levels of ETV over time. In a study of 543 African–American middle
school students, Lambert, Nylund-Gibson, Copeland-Linder, and Ialongo (2010) found
fairly little movement in youth exposed to violence. Students who reported either high or
low levels of violence in 6th grade reported similar levels of ETV throughout middle
school. Similarly, Choi and Temple (2016) found that among youth exposed to teen dating
violence, there was little movement between classes over time. While these studies have
examined subgroups of youth exposed to violence in community samples, little is known
about how these models work in juvenile justice-involved youth undergoing treatment in
the community. Understanding changes in ETV for juvenile justice-involved youth is
particularly important as decarceration efforts have focused on treating youth in the
community and ongoing ETV may affect approaches to treatment and recidivism.
Therefore, research into the changes in ETV during treatment and the variables that
predict these changes are essential to assessment and treatment planning.

Current study

In sum, juvenile justice-involved youth experience ETV and poly-victimization at a high rate.
As the juvenile justice system has moved toward providing community-based treatment, it is
important that we understand how youth experience ETV during treatment and how to
identify those at risk for ETV during treatment. While recent studies have utilized person-
oriented approaches such as latent class analysis to identify typologies of experiencing ETV,
few studies have examined how these typologies change over time and during the course of
treatment. The current study builds upon a previous study that conceptualized exposure to
violence and polyvictimization as dependent on social context by examining the data over
time to see whether subgroups of juvenile justice-involved youth based on ETV change
during treatment. Further, we examine the variables that predict whether youth will transi-
tion from one subgroup to another. Understanding how to identify youth who are at risk for
continued violence exposure during treatment is important to treatment planning and care.
The current study examines the following research questions:

1. How do subgroups of youth expose to violence change over time?
2. What is the probability that an individual will stay in the same subgroup or move to
different subgroups across time?

3. Do family mental health history, gender, and age affect the probability of transition-
ing to different subgroups?


Study population and design

The study population consists of 1,275 juvenile justice-involved youth who successfully
completed participation in the Behavioral Health Juvenile Justice Initiative (BHJJ),


a community-based diversion program in multiple sites across a Midwestern state.
Counties contracted with treatment providers to provide BHJJ youth with community-
based behavioral health treatment that best serves their population. To be eligible for BHJJ
participation, youth were required to have a history of juvenile justice involvement, be
between 10 and 18 years old, and present with at least one Diagnostic and Statistical
Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 2000)
diagnosis. Success in treatment often depended upon individual goals and treatment plans,
and while there is no general definition, youth were required to attend treatment sessions
and meetings with caseworkers, progress in therapy and comply with the terms of the
treatment plan, in addition to other county and program-specific criteria. Progress in
therapy depends upon individual needs and can include, for example, improving relation-
ships with family members. County-specific criteria may include the terms of probation
for counties that served youth on probation while program-specific criteria include
programmatic goals such as increasing prosocial activities. Each juvenile court, with the
help of their service providers, was asked to identify whether a youth had completed
treatment successfully. Youth who did not successfully complete the program often failed
to return to treatment providers to complete termination paperwork and therefore were
removed from the total sample (n = 704). Table 1 presents the characteristics of the
sample, with the majority of participants being male, white, and about 15 years old. Data
for the present study were collected at the county level by a trained caseworker during
intake and termination interviews with the participant. The average treatment time for
participants was 7.32 months (Table 1). After appropriate assent and consent by partici-

Table 1. Population characteristicsa.

Variable in Model Time 1 Time 2

Latent Class Indicator

Victim of a Threat- Home 301 (25.5%) 170 (18.1%)
Witness a Threat- Home 211 (18%) 119 (9.3%)

Victim Slap, Hit or Punch- Home 438 (37.2%) 221 (23.6%)
Witness Slap, Hit or Punch- Home 360 (30.6%) 197 (21.2%)

Victim of Beating- Home 109 (9.3%) 50 (5.4%)
Witness Beating- Home 135 (11.5%) 67 (7.2%)
Victim of a Threat- School 398 (33.8%) 208 (22.2%)

Witness a Threat- School 582 (45.6%) 316 (24.8%)
Victim Slap, Hit or Punch- School 316 (26.9%) 166 (17.7%)

Witness Slap, Hit or Punch- School 685 (58.4%) 368 (39.4%)
Victim of Beating- School 85 (7.2%) 28 (3%)

Witness Beating- School 642 (54.6%) 354 (37.9%)
Victim of a Threat- Neighborhood 257 (21.8%) 165 (17.6%)

Witness a Threat- Neighborhood 391 (33.4%) 236 (25.3%)
Victim Slap, Hit, Punch- Neighborhood 225 (19.1%) 137 (14.7%)
Witness Slap, Hit, Punch- Neighborhood 482 (40.9%) 272 (29.1%)

Victim of Beating- Neighborhood 91 (7.7%) 46 (4.9%)
Witness Beating- Neighborhood 438 (37.4%) 270 (28.9%)


Family Mental Health History 782 (66.5%)

Male 723 (56.7%)
Treatment Time 7.32 Months (4.5 Months)
Age at Beginning of Treatment 15.1 Years (1.5 Years)

White 723 (56.8%)
Neighborhood Disorganization, Treatment Time and Age at Beginning of Treatment employ mean (standard deviation) –
All others use frequency (rounded valid percent)


pants and their caregivers, the counties sent de-identified data to researchers. The parti-
cipating institution’s IRB approved all study protocols.


Exposure to violence
The Recent Exposure to Violence Scale (REVS) contains 26 items that use self-report to
measure ETV in the past 12 months as a witness or victim in three locations (school,
home, and neighborhood; Singer et al., 1999). Previous research indicates acceptable
internal consistency for this scale (Butcher, Kretschmar, Lin, Flannery, & Singer, 2014;
Singer et al., 1995). Because we were interested in only the location items (18 location-
specific items), we only included those in the analysis, employing the same items as
a previous latent class analysis on the REVS in the BHJJ population (Butcher et al.,
2016). These items examine several forms of ETV, including experiencing or witnessing
threats, being slapped, hit or punched, and beatings for each location. Responses ranged
between 0 (Never) and 3 (Almost Every Day). Due to cell scarcity for the 1, 2 and 3
responses, we combined these responses to make each item a dichotomous variable – 0
being an absence of that item or 1 being a presence of that item. While the original scale
included two items around witnessing and being a victim of sexual abuse, the frequency of
youth who responded yes to these questions was low at intake (victim 6.0%, n = 134;
witness 6.2%, n = 138) and at termination (victim 3.4%, n = 37; witness 3.3%, n = 36).
Preliminary analyses were conducted with the sexual abuse items in the proposed models
but convergence was not achieved indicating extreme values in those variables and were
removed from the models presented here. Internal consistency testing employing
Cronbach’s Alpha indicated that a dichotomized version (Time 1 α = .84, Time 2 α =
.87) performs similarly to the original scale (Time 1 α = .86, Time 2 α = .88).

As described in a previous study (Butcher et al., 2016), ETV at intake was conceptua-
lized as a latent construct composed of three latent classes or sub-populations in the study
population. These three sub-populations were dependent upon the location of the expo-
sure – home, school and/or neighborhood. The Low ETV class or sub-population con-
sisted of participants who have a low probability of indicating yes to any of the ETV items.
The Home and School ETV class consisted of participants who have a high probability of
indicating yes to any of the home items and school items, particularly being a victim to or
witnessing threats or slapping, hitting or punching. The Poly-Location ETV class con-
sisted of participants who have a high probability of indicating yes to the neighborhood
and school items, and a somewhat higher probability of indicating yes to the home items.

Family mental health history
Family mental health history was a combination of two items measured as part of the
Caregiver Information Questionnaire administered during intake. The first item indicated
the presence of depression in the participant’s biological family and the second items
indicated the presence of any other mental health illness in the participant’s biological
family. The Caregiver Information Questionnaire is administered to the participant’s
caregiver. The family mental health history item was a dichotomous variable, 0 meaning
no history of mental health issues and 1 being family mental health history (yes to clinical
depression or yes to other mental health illness).


Other study covariates
Gender was coded as Female (1) or Male (0) and was a self-report item. Due to cell
scarcity in some of the race categories, race was coded as a dichotomous variable, White
(1) and Nonwhite (0). Nonwhite represented African-American, Hispanic, Asian, or
Other. Age at the beginning of treatment was a continuous variable representing age in
years at the start of treatment. Treatment time was a continuous variable representing
time in months from the beginning date of treatment to the termination date of treatment.


This analysis seeks to build upon a previous study that examined the subclasses of juvenile
justice-involved youth exposed to violence at one time point to examine changes in
subclasses during treatment. For the purposes of this paper and the analyses, subclasses
refer to smaller clusters of individuals who share characteristics on a given variable, in this
case, exposure to violence (McCutcheon, 1987). In the previous paper, we identified three
underlying subclasses with a portion of the same population studied here (Butcher et al.,
2016). The following sections will describe the results of a Latent Transition Analysis
(LTA), a longitudinal mixture modeling technique that examines how subclasses change
over time (Collins & Lanza, 2010; Graham, Collins, Wugalter, Chung, & Hansen, 1991).

LTA is the longitudinal extension of LCA, a cross-sectional, individual-based modeling
approach that identifies a latent variable composed of observed categorical items within
that latent variable that is represented by heterogeneous sub-populations (McCutcheon,
1987). In contrast with other types of latent variable modeling techniques that focus on
variables that can be scaled to create a construct (i.e. factor analysis), the focus of the
research is on identifying groups of individuals who experience a phenomenon similarly.
Furthermore, the current analysis is intended to examine how these groups change over
the course of community-based treatment. As Lambert et al. (2010) note, LTA modeling is
useful for measuring the model change in ETV because it allows for the examination of
violence exposure over time in addition to explaining the subclasses of youth experien-
cing ETV.

In the current study, the analyses sought to accomplish four objectives. First, we
examined how well the model established in the previous paper fits the sample in this
study at intake. The model established in the previous paper consisted of three classes of
youth; low probability of ETV, higher probabilities of Home and School ETV, and higher
probabilities of ETV in the Home, School, and Neighborhood. Second, the analysis sought
to establish the model at termination by examining how well the intake model fits the data
at termination. Third, we examined how subgroups of youth change from class to class
from intake to termination. For example, youth may stay in the same ETV class from
intake to termination while others move from a low probability of ETV to a high
probability of ETV during treatment. Finally, the fourth objective of the analysis is to
examine the factors that predict whether a youth moves or stays in a subgroup.

To accomplish these four objectives, we fit an LTA model using the three-step approach
recommended by Nylund (2010) and Collins and Lanza (2010). We assessed how well the
model fit by first including only the REVS variables, and then we added in the important
covariates that were identified in the previous study. As with LCA, LTA relies on multiple
indicators for assessing model fit, including relative information criteria, the Bayesian


Information Criterion (BIC), Akaike’s Information Criterion (AIC), and the adjusted
Bayesian Information Criterion (aBIC), entropy, to compare models against each other
while favoring the most parsimonious and interpretable class solution (Geiser, 2012).
These fit criteria, in addition to the Bootstrap LR Difference Test, were also employed
to compare between classes when fitting the time 2 LCA model before fitting the LTA
model. All LTA models employed random start values to ensure that results are global
rather than only local solutions. These random starts allow researchers to see whether
estimating the model multiple times results in the same or a different solution.

We fit several LTA models. First, we fit one that did not include covariates in order to
obtain the baseline transitional probabilities and classification of individuals into possible
classifications. As with an LCA, we examined fit statistics to determine which model fits
the data best. Another important aspect of fitting an LTA model is the examination of
measurement invariance from Intake to Termination. LTA measurement invariance is
defined as the models, including the number and nature of classes, being expressed
similarly over a period of time (Collins & Lanza, 2010). While measurement invariance
is desirable, it is not necessary as we can estimate using partial measurement invariance.
To estimate measurement invariance, we compare the selected LTA model using all freely
estimated parameters with the same model except one where all of the parameters are set
to equal each other over time. Then, we can calculate the amount that these two agree by
conducting an LRT test (Perra, 2012). If the p-value is < .05, then we cannot assume
measurement invariance and will fit a model that is partially non-invariant by estimating
which parameters should be fixed based on theory.

Following proper LTA model fit, the second LTA model included all covariates related
to Time 1 membership: gender, family mental health history, age at beginning of treat-
ment, race, and treatment time in months. We hypothesized that family mental health
history and treatment time in months would be related to the transition probabilities from
Time 1 to Time 2; therefore, the third model included statistically significant covariates
affecting Time 1 membership (gender and family mental health history) and family mental
health history and treatment time in months affecting Time 1 to Time 2 membership. The
tables presented reflect the multinomial logistic regression analyses from the second model
and third model. Only statistically significant results are presented from the third model.
Analyses were conducted in MPlus version 7.2 (Muthén & Muthén, 1998/2014) and the
SAS PROC LCA and LTA Macro Version 1.3.2 (Lanza, Dziak, Huang, Wagner, & Collins,


Table 1 displays the population characteristics according to the 18 Recent Exposure to
Violence Scale (REVS) class indicators employed in the LCAs and LTA, and the covariates
employed in the LTA model. A total of 1,094 participants had all the covariates of interest
in this study. Generally, the prevalence of each REVS variable decreased from Time 1 to
Time 2. Nearly two-thirds (66.5%) of successfully completed youth had a family history of
mental health issues as indicated by their caregivers. Most of the successfully completed
population is male (56.7%) and white (56.8%). The average age at the beginning of
treatment was 15.1 years. The average treatment time was 7.3 months. We examined
differences in all ETV variables in the study for successful and unsuccessful completers at


intake and found no statistically significant differences. These population characteristics
were also similar to the cross-sectional model presented previously (Butcher et al., 2016).

The original Time 1 LCA presented in a previous article was conducted on all youth
enrolled in BHJJ (N = 2,124; see Butcher et al., 2016). Prevalence rates for all violence
exposure items are nearly identical (see Butcher et al., 2016) suggesting that the two
samples are similar to the variables of interest. Considering the population characteristics
including the endorsement of the REVS latent indicators were very similar for the current
analyses, we used the same LCA three-class model and found nearly identical fit statistics
and conditional probability plot (Figure 1). The three classes are as such: Low ETV (low
probability of endorsement on all items), Home and School ETV (high probability of
endorsement on home and school items), and Poly-Location ETV (high probability of
endorsement on home, school and neighborhood items). For more information on the
measurement portion of the Time 1 LCA model, please see Butcher et al. (2016).

While the fit criteria for the Time 2 LCA indicated a superior fit (lower AIC, BIC, aBIC;
Higher entropy) for a four-class model as opposed to a three class model, the interpreta-
tion of the fourth class was unclear and endorsement of the indicators were in the .4 to .5
range. This class may indicate a group of individuals that are guessing their answers,
decreasing the interpretability of an optimal class solution (Geiser, 2012). Due to the poor
interpretation of the fourth class, we opted to remain with the three-class solution
determined in the Time 1 LCA. Figure 1 presents the Time 1 LCA (top figure) and
Time 2 LCA (bottom figure) conditional probability plots for the study population. For
the Low ETV class, endorsement of REVS indicators was lower at Time 2 than at Time 1.
For Time 2, there appeared to be a clearer divide between the Poly-Location ETV class
and the Home and School ETV class, particularly for the Home indicators.

We suspected that due to the shift in the home items and the neighborhood items for
the Home and School ETV class that there might be partial measurement invariance from
Time 1 to Time 2. To test for this, we compared the LTA with full measurement
invariance and the LTA with freely estimated parameters and found that there was
a statistically significant difference between the two models (p = .0003). Then, we
compared the LTA with full measurement invariance and the LTA with freely estimated
parameters for the home and neighborhood items (partial measurement invariance) and
found that there was no statistically significant difference between the two models (χ2 =
32.012, df = 36, p = .30). Therefore, we ran the model with constrained parameters on the
School items, and freely estimated parameters on the home and neighborhood items.

According to the results of the LTA (no covariates), the prevalence of latent classes
from Time 1 to Time 2 shifts (Table 2). In Time 1, the prevalence of the latent classes is
about equal, with a larger prevalence in the Poly-Location ETV class (38.1%) than in
the Low ETV (33.8%) or Home and School ETV class (28.1%). This changes by Time 2,
where the majority of the prevalence is in the Low ETV class (53.9%), and the other
two classes each experience a prevalence decrease of about 10%. This shift in prevalence
is supported by the transition probabilities demonstrated in Figure 2 (Table 3). The
highest probabilities are in the cells that represent no movement from one class to
a different class (i.e. Low ETV to Low ETV). Following that, the highest probabilities
are from the Home and School ETV and Poly-Location ETV to Low ETV (.39).
Therefore, in this population, someone has a higher likelihood of remaining in the
same class from Time 1 to Time 2 than of moving to another class, particularly if that


person is in the Low ETV class (.85). If someone is in the Home and School ETV or
Poly-Location ETV class, that person has about a 40% chance of moving to the Low
ETV class.























Time 1, N = 1,184

Poly Location, 37.1% Home and School, 25.6% Low ETV, 37.3%























Time 2, N = 942

Poly Location, 16.9% Home and School, 37.6% Low ETV, 45.4%

Figure 1. Conditional item probability plots for 3-Class LCA models for Time 1 and Time 2.


Based on this model, there are nine statuses that can represent the spectrum of transition-
ing (or staying) from one class to another between Time 1 and Time 2 (Table 4). Staying in the
Low ETV class represents 28.6% of the study population (n = 365). While staying in the other
two classes represents 32.6% of the study population, moving fromHome and School ETV or

Figure 2. Transition probabilities Time 1 to Time 2.

Table 2. Prevalence of latent classes according to time.
Low ETV Home and School ETV Poly- Location ETV

Time 1 371 (33.8%) 308 (28.1%) 418 (38.1%)
Time 2 592 (53.9%) 197 (18%) 308 (28.1%)


Poly-Location ETV to Low ETV represents about 26.1% of the study population. Additionally,
few move from a lower ETV class to a higher ETV class (9.3%, n = 119).

After examining the baseline LTA model, we included covariates into the model using
multinomial logistic regression analysis. Table 5 presents the results of the regression
analysis of the impact of covariates on Time 1 class membership. Family mental health
history compared to no family mental health history (p = .0039) and gender (female
compared to male; p = .0188) significantly impacted Time 1 class membership. Of
particular interest is family mental health history, where those who have family mental
health history have higher odds of being in the Home and School ETV class (OR = 1.93)
or Poly-Location ETV class (OR = 1.45) than being in the Low ETV class. Females had
lower odds of being in the Poly-Location ETV class versus the Low ETV class (OR = 0.72);
however, they had slightly higher odds of being in the Home and School ETV class versus
the Low ETV class (OR = 1.15) compared to males.

While we examined the impact of both statistically significant variables on the transi-
tion from Time 1 to Time 2, we focused on the Odds Ratios for only family mental health
history due to the interest of the study and because the data revealed generally null ORs
(about 1.00) for each class comparison. Table 6 presents the Odds Ratios for family mental
health history (compared to no family mental health history). Similar to the Time 1 LCA

Table 5. Multinomial logistic regression modeling Time 1 covariate impact.
Change in

Type III

Home and School ETV
Odds Ratioa

Poly- Location ETV
Odds Ratioa

Family Mental Health Historyb 11.09 .0039 1.93 1.45
Genderc 7.94 .0188 1.15 0.72
Treatment Time 0.17 .9174 1.00 1.00
Age at Beginning of Treatment 2.96 .2272 0.90 0.90
Raced 0.25 .8813 1.05 0.95

aClass 1 (Low ETV) is the Reference Group
bNo Family Mental Health History is the Reference Group
cMale is the Reference Group
dNonwhite is the Reference Group

Table 3. Transition probabilities of latent class membership.
Time 2

Time 1 Low ETV Home and School ETV Poly-Location ETV

Low ETV 0.85 0.03 0.11
Home and School ETV 0.39 0.46 0.15
Poly-Location ETV 0.39 0.09 0.51

Table 4. Classification of individuals based on the estimated model (N = 1,275).
Time 1 Class Time 2 Class Number Rounded Percent

Low ETV Low ETV 365 28.6%
Low ETV Home & School ETV 17 1.3%
Low ETV Poly ETV 45 3.5%
Home & School ETV Low ETV 146 11.5%
Home & School ETV Home & School ETV 166 13%
Home & School ETV Poly ETV 57 4.5%
Poly ETV Low ETV 186 14.6%
Poly ETV Home & School ETV 42 3.3%
Poly ETV Poly ETV 250 19.6%


covariate analysis, we find that those who go from the Low ETV class to a higher ETV
class (Home and School ETV or Poly-Location ETV) have a higher odds of family mental
health history (OR = 1.26 and 1.22, respectively) than those who stay in the Low ETV
class. When examining the impact of family mental health history on transition prob-
ability and status membership (Table 7), we find that the transition probabilities are
similar regardless of family mental health history. However, those with a family mental
health history have a slightly higher probability of transitioning from Low ETV to Home
and School ETV, Low ETV to Poly-Location ETV, Home and School ETV to Low ETV,
Poly-Location ETV to Home and School ETV than those without a family mental health


The current study extended a previous cross-sectional study by examining how subgroups of
juvenile justice-involved youth exposed to violence change over time. As with the previous
study (see Butcher et al., 2016), we found that location was an important distinguishing factor
for youth experiencing ETV. Youth exposed to violence can be classified into three subgroups
according to the contextual location in which the violence occurred: Low ETV, Home and
School ETV, and Poly-Location ETV. Recent literature has shown that in addition to under-
standing the cumulative nature of violence exposure, conceptualizing ETV in youth should
focus on the location in which the incidents occurred and the effects of victimization across

Table 7. Prevalence of latent class membership and transition probabilities according to family mental
health history.

Time Prevalence Transition Probability

Time 1 Class Time 2 Class
Family Mental
Health Hx

No Family Mental
Health Hx

Family Mental
Health Hx

No Family Mental
Health Hx

Low ETV Low ETV 187 (25.6%) 128 (34.8%) .84 .88
Low ETV Home & School

13 (1.7%) 4 (1.1%) .06 .03

Low ETV Poly ETV 25 (3.4%) 14 (3.8%) .11 .09
Home & School

Low ETV 90 (12.3%) 27 (7.4%) .40 .33

Home & School

Home & School

106 (14.5%) 37 (10.1%) .47 .46

Home & School

Poly ETV 30 (4.1%) 17 (4.6%) .13 .21

Poly ETV Low ETV 96 (13.1%) 63 (17.2%) .34 .45
Poly ETV Home & School

30 (4.2%) 8 (2.2%) .11 .06

Poly ETV Poly ETV 153 (20.9%) 69 (18.8%) .55 .49

Table 6. Odds ratios predicting the effect of family mental health history on transitions from Time 1 to
Time 2.a

Time 2

Time 1 Low ETV Home and School ETV Poly-Location ETV

Low ETV Reference 1.26 1.22
Home and School ETV 0.96 Reference 0.86
Poly-Location ETV 0.87 1.08 Reference

aFamily Mental Health History compared to No Family Mental Health History


multiple social contexts (Butcher et al., 2016; Turner et al., 2016). Data presented here also
demonstrated the stability of this conceptualization of ETV across time. The current study was
also restricted to using only youth who successfully completed programming, and therefore,
the stability in the conceptual model further confirms the importance of contextual location as
a factor in conceptualizing ETV in juvenile justice-involved youth.

Consistent with findings reported by Lambert et al. (2010) and Choi and Temple
(2016), data presented here suggest that subgroups of youth who experience ETV change
with fairly low probability during treatment. Particularly, the Low ETV class was the least
likely to change from Time 1 to Time 2. It is encouraging that subgroups of youth with
a low probability of ETV at intake into treatment are likely to remain at low risk for ETV
throughout treatment. Similar to the finding for youth in the Low ETV class, there was
a substantial probability that youth in Poly-Location ETV class at intake would continue
to experience ETV in all three contextual locations throughout treatment. These youth
present a challenge for treatment providers as ETV is likely to be ongoing.

While interclass movement was fairly low for all three subgroups of youth exposed to
violence, family history of mental health issues predicted movement from the Low ETV
class into either the Home and School ETV class or the Poly-Location ETV class. Previous
studies have found that family history of mental health issues is a predictor of ETV class
membership (Butcher et al., 2016; Tossone et al., 2015). The current study builds upon
these previous studies by demonstrating the importance of family mental health problems
in the dynamic nature of a youth’s experience with ETV.

Strengths and limitations

Several strengths of the current study are of note. Data from the current study represent
a relatively large sample of youth involved with the juvenile justice system. Behavioral
health problems in juvenile justice-involved youth are still a relatively understudied issue
that can have a significant impact across child-serving systems. While there are a number
of cross-sectional studies on ETV in this population, data at intake and termination are
fairly unique. Youth in the juvenile justice system are more likely to be from areas of high
structural disadvantage and are at an increased risk for ETV (Butcher et al., 2015).
Therefore, the current study design allowed for an examination of youth who report
high prevalence rates of ETV across social contexts and during treatment. High prevalence
rates for each of these items allowed for a clear separation of latent classes. While the
sample is restricted to youth in a Midwestern state, these youth represent 11 geographi-
cally diverse counties across the state.

While the current study has several strengths, findings reported here have several
limitations. Youth are involved with the juvenile justice system so findings may not be
generalizable to community samples. Further, ETV is a sensitive topic that participants
may feel uncomfortable discussing. Self-report surveys on sensitive topics have the implicit
limitation of underreporting or malingering (Butcher et al., 2014; Tourangeau & Yan,
2007). Due to data scarcity in cells, ETV items were dichotomized to reflect whether the
youth had or had not experienced each type of incident in the past year. The items
included in the REVS have been dichotomized in previous studies (e.g. Butcher et al.,
2016). Data for this study were derived entirely of youth who successfully completed
treatment. As youth often did not return to the juvenile court or treatment agency


following their unsuccessful termination, we did not have the opportunity to collect data
at the second time point. However, the LCA model at intake reported in this study fit the
data similarly when compared to the Butcher et al. (2016) study which included all youth
participating in the BHJJ program including those who would go on to complete the
program unsuccessfully. Further, it is important to note that exposure to violence is
experienced by the majority of juvenile justice-involved youth. Regardless of their pro-
gram completion status, the prevalence of ETV at intake was similar. The current study
also does not include questions around sexual violence. While we did collect these data,
there were not enough youth who answered yes for the models to converge. Finally, while
the current study is an examination of the effects of change in ETV during treatment, the
average length of time between intake and termination was 7 months. Longer time periods
between measurement may uncover a more meaningful change in latent profiles.
However, several studies using latent transition analysis examining ETV class membership
across time show that ETV class membership across time is fairly stable (Choi & Temple,
2016; Lambert et al., 2010).


Exposure to violence is an important challenge for juvenile justice-involved youth and
treatment providers as more juvenile justice systems expand diversion programming.
Research on juvenile justice-involved youth have consistently recommended screening
for ETV and trauma as part of the intake and screening procedures (Branson, Baetz,
Horwitz, & Hoagwood, 2017; Ford, Cruise, Grasso, & Holloway, 2018; Fox, Perez, Cass,
Baglivio, & Epps, 2015; Ko et al., 2008). Screening procedures can help to identify
treatment needs for youth and can help systems to reduce recidivism. The current study
reinforces the importance of capturing the contextual location in which the violence
exposure occurs by examining ETV at two time points. This finding supports previous
research that examined location-based ETV using person-oriented approaches (Butcher
et al., 2016; Turner et al., 2016).

While the purpose of the current study was to examine the conceptualization of ETV
during treatment and to understand how subgroups of youth experience ETV, data
presented here also have significant implications for family-based treatment in juvenile
justice settings. The findings here suggest that family history of mental health was
associated with ETV in multiple settings throughout treatment. This may support
treatment models that involve both the youth and their caregivers to build and sustain
supportive relationships. Evidence supported treatment models appropriate for juvenile
justice-involved youth that have been successfully implemented in the community while
maintaining fidelity include Multisystemic Therapy (MST), Functional Family Therapy
(FFT), and Treatment Foster Care-Oregon (TFC-O, formerly Multidimensional
Treatment Foster Care, MTFC; Henggeler & Sheidow, 2012). While these treatments
do not target PTSD specifically, they do address associated and common behavioral and
functional issues (Mahoney, Ford, Ko, & Siegfried, 2004). A major benefit of these types
of treatments is that they coach caregivers to intervene in the multiple domains of their
child’s life (school, peers, and community) to varying degrees. This makes these
programs extremely well-positioned to address the youth’s complex need for safety,
stability, and structure, in order for caregivers to best guide the child in navigating the


often fraught terrain of adolescence into adulthood. These treatment approaches often
address issues beyond the reasons for referral, and target other co-occurring issues in
order to build up protective factors that are needed to help the youth to achieve and
sustain positive changes, including targeting the youth and caregivers’ substance abuse
and mental health needs (Henggeler & Sheidow, 2012).

Validated and evidence-based treatments that have shown that they can reduce symp-
toms of mental health issues for caregivers are particularly worth consideration for use
with juvenile justice-involved youth with violence exposure and a history of family mental
health issues. Employing a treatment approach that addresses caregiver mental health
needs can be powerful in activating protective parenting approaches that aid in a youth’s
recovery and resiliency. Data here indicated that these youth are particularly vulnerable to
repeated victimization and trauma and point to the importance of providing juvenile
justice-involved youth with family-based treatment approaches.

While the current study has significant implications for the assessment of ETV during
community-based treatment and the factors associated with ETV, future studies should
examine the impact of ETV on treatment engagement and outcomes. Juvenile justice-
involved youth are likely to experience ETV across multiple social and this can have an
impact on treatment outcomes. Further, the data showed a fairly high likelihood that
youth who experience ETV in multiple settings will continue to experience ETV through-
out community treatment. Future studies should examine how well-specific treatment
modalities can help youth to build resilience and how resilience affects both treatment
outcomes and future justice involvement.

Disclosure statement

No potential conflict of interest was reported by the authors.


This research was supported in part by grants from the Ohio Department of Youth Services and the
Ohio Department of Mental Health & Addiction Services [4AS3190].


Fredrick Butcher


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  • Abstract
    • Literature review
    • Current study
  • Method
    • Study population and design
    • Measures
      • Exposure to violence
      • Family mental health history
      • Other study covariates
    • Analysis
  • Results
    • Discussion
    • Strengths and limitations
    • Conclusions
  • Disclosure statement
  • Funding
  • References

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