Factors related to recidivism for youthful offenders
Christopher A. Mallett*, Miyuki Fukushima, Patricia Stoddard-Dare and
Cleveland State University, 2121 Euclid Avenue, #CB324, Cleveland, OH 44115, USA
Little is known about youth who were previously placed in a detention facility
and what factors predict a subsequent recidivism to placement. This study of a
two-county juvenile offender population (one urban and one rural) investigates
what demographic, educational, mental health, substance dependence, and court-
related variables predict recidivism to detention placement. Findings from logis-
tic regression analysis indicate that seven variables signiﬁcantly predict juvenile
offenders’ recidivism placement, some expected and some unexpected. Predic-
tors that made recidivism more likely include youth with a previous conduct dis-
order diagnosis, a self-reported previous suicide attempt, age, and number of
court offenses. Conversely, predictors that made recidivism less likely include
race (Caucasian), a previous attention-deﬁcit hyperactivity disorder diagnosis,
and a misdemeanor conviction. These ﬁndings indicate that the use of a commu-
nity-based suicide and mental health screening and referral approach may help
to identify and assist these high-risk youth in receiving needed services prior to
juvenile court involvement or during delinquency adjudication.
recidivism; delinquency; youth; mental health; suicide
Over the past two decades, the juvenile justice ﬁeld has been intermittently shifting
between a punitive and rehabilitative approach to dealing with youthful offenders,
resulting from organized reformation activities by key stakeholders and progressive
juvenile courts, and today’s high cost of detainment and incarceration. Recently,
poor juvenile court outcomes for many youthful offenders, particularly serious
offenders, have inﬂuenced an incremental movement away from ‘tough on crime’
policies. Furthermore, the number of youth involved annually in the juvenile justice
system nationwide, while trending downward in recent years, is still somewhat stag-
gering – over 2.1 million arrests of youth under age 18 (Puzzanchera, 2009), 1.7
million delinquency cases (Knoll & Sickmund, 2010), 350,000 youth held in deten-
tion centers (Holman & Ziedenberg, 2006; Sickmund, 2008), and over 100,000
youth held in correctional facilities (Davis, Tsukida, Marchionna, & Krisberg, 2008;
Sickmund, 2009). This paper focuses on more serious youthful offenders who pre-
viously served time in a detention facility and then recidivate to detention center
placement. This repeat detention experience does neither the youth nor the juvenile
court systems much beneﬁt, and is increasingly being recognized as detrimental to
*Corresponding author. Email: firstname.lastname@example.org
Criminal Justice Studies, 2013
Vol. 26, No. 1, 84–98, http://dx.doi.org/10.1080/1478601X.2012.705539
Ó 2013 Taylor & Francis
the youth and the juvenile courts’ public policy goals of youth accountability and
maintaining safe communities. Thus, reducing recidivism is an important juvenile
justice system priority (US Department of Justice, 2010).
There exist many risk factors for juvenile justice system involvement and youth
delinquency. These risks are related to the individual (early aggression, mental
health problems, and substance abuse), family (inconsistent parenting and trauma),
school (academic problems, educational deﬁcits, and special education disabilities),
and neighborhood (high levels of unemployment, residential instability, and family
disruptions) (Bor, McGee, & Fagan, 2004; Bursik & Grasmick, 1993, 1995;
Hawkins et al., 1998; Hay, Fortson, Hollist, Altheirmer, & Schaible, 2006; Lynam,
Mofﬁtt, & Stouthamer-Loeber, 1993; Mallett, 2011; Sampson, Raudenbush, &
Earls, 1997; Tremblay et al., 2004). In addition, a number of youth demographic
factors have been consistently found to predict juvenile court supervision; hence,
researchers usually control for their impact in their analysis. These factors include
juveniles’ age (older youth are more likely), gender (males are more likely, though
females are increasing in numbers), race (minorities are more likely), and socioeco-
nomic status (juveniles living in poor households are more likely, especially for
serious forms of delinquent behaviors) (Hawkins, Catalano, & Miller, 1992; Haw-
kins et al., 2000; Loeber & Farrington, 2000; McLoyd, 1998; Nagin & Tremblay,
2001). Because of the existence of multiple problems and risks, predicting juvenile
delinquency outcomes is difﬁ cult (Ford, Chapman, Hawke, & Albert, 2007; Green,
Gesten, Greenwald, & Salcedo, 2008; Gutman, Sameroff, & Cole, 2003). Predicting
detention placement and recidivism is particularly difﬁcult.
Research that attempts to predict juvenile recidivism has been limited, with results
generally accounting for approximately 20% of the variance (Katsiyannis, Zhang,
Barrett, & Flaska, 2004). Many of these models measured recidivism as re-adjudica-
tion by the juvenile court, and not recidivism into a detention center or incarcera-
tion facility (Ashford & LeCroy, 1990; Katsiyannis & Archwarmety, 1997). When
measuring recidivism as re-adjudication, identiﬁed predictive factors of importance
for this study include age at ﬁrst offense (Ashford & LeCroy, 1990; Brunner, 1993;
Weaver & Wootton, 1992), severity or seriousness of delinquency (Archwarmety &
Katsiyannia, 2000; Brunner, 1993; Frazier & Cochran, 1986; Wierson & Forehand,
1995), prior arrest (Ashford & LeCroy, 1990; Lattimore, Fisher, & Linster, 1995),
lower academic achievement (Foley, 2001), behavioral problems including impulsiv-
ity (Hagan & King, 1997), and race (Leiber & Fox, 2005; Pope, Lovell, & Hsia,
2002; Webb, 2006).
While measuring recidivism as a re-adjudication is informative, not all youth in
this situation are subsequently placed into a detention center. This detention center
placement is of particular interest in this study, because it often increases
subsequent youth offending and recidivism (Justice Policy Institute, 2009; Soler,
Shoenberg, & Schindler, 2009). In other words, the experience of detention is
unique and this experience makes it more likely that detained youth will continue
Criminal Justice Studies 85
to engage in delinquent behavior, and it may increase the odds of recidivism (Hol-
man & Ziedenberg, 2006). Hence, it is important to identify the risk factors that
impact both detention placement as well as detention placement recidivism.
There are a limited number of studies that deﬁne recidivism as a return place-
ment into a detention or incarceration facility. In a review of factors which predict
recidivism to placement, carrying a weapon, gang membership, and neglect or
abuse by a parent was found signiﬁcant (Benda & Tollett, 1999). Others found that
youth who recidivated to placement were more likely to have both personal- and
school-related problems (Wordes, Bynum, & Corley, 1994), and the likelihood of
being detained was greater for minority youth compared to nonminority youth even
for the same offense (Feld, 1995). Most recently, in a review of what legal and
extra-legal factors predicted detention, race (African-American and Hispanic), prior
arrest, and personal crimes were found signiﬁcant (Webb, 2006).
Youth mental health problems, delinquency, and recidivism
Mental health problems and disorders are linked to youth offending behaviors and
delinquency adjudication; though it is not clear if this link is direct or if these difﬁ-
culties lead to other risk factors, poor decision-making, or the interaction of various
other risks (Grisso, 2008; Heilbrun, Goldstein, & Redding, 2005; Mofﬁtt & Scott,
2008, chap. 35; Shufelt & Cocozza, 2006). Still, reviews have consistently found
that children and youth who are involved with mental health services have a signiﬁ-
cantly higher risk for juvenile court involvement (Rosenblatt, Rosenblatt, & Biggs,
2000; Vander-Stoep, Evans, & Taub, 1997).
A number of pathways have been established which link speciﬁc childhood
mental health difﬁculties to juvenile court involvement. Developmental studies have
found behavioral and emotional problems to be predictive of later delinquency and
substance abuse (Dishion, Capaldi, & Yoerger, 1999; O’Donnell, Hawkins, Cata-
lano, Abbott, & Day, 1995). Similarly, early childhood aggressive behaviors have
been found predictive of later delinquent behaviors and activities (Tremblay &
LeMarquand, 2001). Attention and hyperactivity problems are linked to later high-
risk taking and more violent offending behavior (Elander, Siminoff, Pickles, Holm-
shaw, & Rutter, 2000; Hawkins et al., 1998). Antisocial behaviors and emotional
problems in early childhood are markers for later delinquent activities (Wasserman
et al., 2003). In addition, childhood depression and attention-deﬁcit hyperactivity
disorder (ADHD) have been found linked to later delinquency, evidenced through
physical aggression and stealing behaviors (Goldstein, Olubadewo, Redding, &
Lexcen, 2005; Mofﬁtt & Scott, 2008, chap. 35; Ryan & Redding, 2004).
Findings have been mixed regarding the relationship between mental health
treatment needs (including substance abuse) and severity of juvenile court disposi-
tion. Indeed, youth mental health problems (broadly deﬁned) have been found to
predict both less and more severe dispositions; whereas substance abuse leads to
more severe sanctions, including conﬁnement (Campbell & Schmidt, 2000; Fader,
Harris, Jones, & Poulin, 2001; Lyons, Baerger, Quigley, Erluch, & Grifﬁn, 2001;
McReynolds, Schwalbe, & Wasserman, 2010; Schwalbe, Hatcher, & Maschi, 2009).
Youthful offenders needing secure placement pose a particularly difﬁcult chal-
lenge to the juvenile courts, for juvenile detention and incarceration facilities are
disproportionately populated by youth with at least one, if not multiple, mental
health disorders and/or school-related disabilities (Garland et al., 2001; Mallett,
86 C.A. Mallett et al.
2009; Teplin et al., 2006). Within these correctional facilities, 60% of the youth
have an identiﬁed mental health disorder (with 20% of these disorders severely
impacting functioning) (Grisso, 2008), over 35% of the youth have an identiﬁed
special education disability (Mears & Aron, 2003), and between 30 and 50% of the
youth have a signiﬁcant substance abuse disorder (Chassin, 2008).
This situation becomes more complicated when reviewing youth mental health
problems along with prior suicide attempt. Suicide is a signiﬁcant concern for
detained and incarcerated youth, with 110 suicides occurring between 1995 and
1999 (Hayes, 2009), a rate that is multiple times more frequent than what occurs in
the general youth community (Kaczmarek, Hagan, & Kettler, 2006). In addition, in
one study of suicides in Utah, 63% of youth in the community who completed the
suicide had past juvenile justice system contact (Gray et al., 2002). While suicide
completion is tragic, the reports of suicide ideation and attempts look to be signiﬁ-
cantly greater within this conﬁned youthful offender population than in the general
youth population. In one study of 900 incarcerated youth, over 30% reported a past
suicide attempt (Putnins, 2005); while in a most recent nationally representative sur-
vey of over 7000 youthful offenders being held in facilities, over 22% reported a
past suicide attempt (Sedlak & McPherson, 2010).
A number of youth suicide risk factors are relevant here and include numerous
mental health disorders, drug use, antisocial behaviors, and delinquency (Epstein &
Spirito, 2009; Flisher et al., 2000; Maris, Berman, & Silverman, 2000; Thompson,
Ho, & Kingree, 2007). Aggression frequency and violent behaviors increased with
increased suicidality levels (Vannatta, 1996); while getting involved with ﬁghts and
using weapons were found to be high risks for suicide ideation (Evans, Marte,
Betts, & Silliman, 2001). These acting out and antisocial behaviors, and speciﬁcally
conduct disorder, were often found among suicidal youth (Brent et al., 1993). Evi-
dence of the ADHD link to suicide attempts is less clear, with some researchers
ﬁnding higher rates of ADHD in these populations (Ruchkin, Schwab-Stone, Kopo-
sov, Vermeiren, & King, 2003; Swensen, Kruesi, Allen, Beusching, & Secnik,
2002), and others not ﬁnding such a connection (Renaud, Brent, Birmaher, Chiapp-
etta & Bridge, 1999).
However, predicting suicide risk is not easy because risk factors vary in their
impact and intensity for incarcerated and formally adjudicated youth, though this
population is in general at a higher suicidal-behavior risk (Epstein & Spirito, 2009;
Evans, Hawton, & Rodham, 2004). Even when other risk factors – age, ethnicity,
gender, alcohol and drug problems, depression, and impulsivity – were accounted
for, delinquency was still related to suicidal ideation and attempts up to one year
after adjudication and to ideation up to seven years after adjudication (Thompson
et al., 2007).
Justiﬁcation for the study
As discussed, only a small number of studies have focused on offenders who recidi-
vate to placement; therefore, additional research is needed. Continuing these inqui-
ries and identifying what legal and extra-legal factors predict recidivism to
detention placement is important in understanding and directing preventative
Criminal Justice Studies 87
programs. If certain youth risks, behaviors, and/or disabilities are clearly identiﬁed,
then early intervention efforts may decrease or help youth desist delinquent offend-
ing and subsequent detention or incarceration. Decreasing youth recidivism (offend-
ing and incarceration) is effective public policy, saving limited ﬁscal resources, and
improving public safety (Caldwell, Vitacco, & Van Rybroek, 2006; Gatti, Tremblay,
& Vitaro, 2009; Loughran et al., 2009; Soler et al., 2009).
What demographic, educational, mental health, substance dependence, and court-
related variables predict secure detention placement recidivism in a population of
court involved youth?
The sampling frame for this study consisted of court involved youth from two
counties over a distinct period of time. An a priori analysis was conducted to calcu-
late an appropriate sample size. Given the annual population size of 2300 delin-
quent probation-supervised youth from the ﬁrst county (urban) and an annual
population size of 300 delinquent probation-supervised youth from the second
county (rural), it was determined that a sample size of 343 (over three years, from
2006 to 2008) from the ﬁrst county and a sample size of 90 (over one year – 2008)
from the second county would provide the appropriate ﬁve percent margin of error
and 95% conﬁdence interval, assuming a population proportion of 50% (Royse,
Thyer, Padgett, & Logan, 2006). A simple random sample was drawn for each pop-
ulation year of the counties’ juvenile delinquent probation-supervised population;
youth who had been adjudicated delinquent during that calendar year and chosen
for the study did not include youth transferred to criminal (adult) court (though this
number of transferred youth represented less than half of one percent of the total).
A total of 433 (not duplicated) youth were included in this study sample: urban
county 2006 = 100; 2007 = 137; 2008 = 105; rural county 2008 = 91.
Data were collected from existing de-identiﬁed ﬁles provided by each county’s
juvenile court. Each ﬁle contained ofﬁcial records associated with each youth in the
study sample. Speciﬁcally, juvenile court histories, probation supervision case ﬁles,
school records, and mental health assessments were provided. Data from the case
records were coded and entered into a statistical software package. Each case
entered was evaluated for proper coding and correct data entry. Inter-coder reliabil-
ity was high (.96).
Theoretically important demographic, educational, mental health, and juvenile
court-related variables were measured. Demographic variables include age (in
years), gender (male = 1), race (Caucasian = 1, all other = 0), and county of residence
88 C.A. Mallett et al.
when ﬁrst adjudicated delinquent (rural = 1, urban = 0). Three separate education dis-
ability variables were measured – severely behaviorally handicapped (SBH), devel-
opmentally handicap (DH), and severely emotionally disturbed (SED). For each
variable, diagnosis was made prior to ﬁrst delinquency adjudication, by a licensed
provider using DSM-IV criteria (yes = 1 indicates an existing diagnosis). Mental
health-related variables that were measured include ADHD, conduct disorder, oppo-
sitional deﬁant disorder, bipolar disorder, depression, adjustment disorder, post-trau-
matic stress disorder (PTSD), and anxiety disorder. Additionally, alcohol
dependence and substance use disorders were also measured. Individuals were
counted as alcohol dependent if they had ever been diagnosed with alcohol depen-
dence, and individuals were counted as substance dependent if they had ever been
diagnosed by a licensed provider using DSM-IV criteria with dependence to any
drugs other than alcohol. All diagnoses were made prior to the youth’s ﬁrst delin-
quency adjudication by licensed providers using DSM-IV criteria (yes = 1 indicates
a prior diagnosis). In addition, youth’s self-report of a prior suicide attempt was also
measured (yes = 1 indicates a prior suicide attempt). Juvenile court-related variables
included the total number of times each youth was adjudicated delinquent (in num-
ber of times), the youth’s age at ﬁrst delinquency (in years), the youth’s total num-
ber of court offenses which includes multiple offenses over time (in number of
court offenses), if the youth had ever been convicted of a felony (yes = 1), if youth
had ever been convicted of a misdemeanor (yes = 1), and if the youth had ever been
convicted of a property crime, personal crime, drug crime, status offense, or viola-
tion of court order (VCO) (all coded 1 for yes).
One dependent variable, recidivism, was measured. Youth were coded (yes = 1) if
they were sentenced to detention, were released from custody, and then subse-
quently placed back into detention.
A small number of missing variables were imputed with either the mean (for
continuous variables) or the mode (for categorical variables), except for juvenile
court-related variables where missing cases (only one to two per variable) were
eliminated from the analysis. There were no missing cases for the dependent vari-
able (see Table 1).
A bivariate correlation analysis among all variables was ﬁrst conducted. Second,
bivariate logistic regression was used to assess the bivariate relationship between
each of the independent and control variables and the dependent variable, recidi-
vism to secure detention placement. Variable pairs that were signiﬁcant at a p-value
greater than .1 in the bivariate mode were then entered into a forward stepwise mul-
tivariable model (see Table 2).
Additionally, a second multivariable forward stepwise logistic model with all the
potential independent variables was developed. This was done so that two theoreti-
cally relevant variables which have demonstrated impact on detention placement
would be included in the multivariate model. The models differed only in the inclu-
sion of ADHD and race, which were not signiﬁcant in bivariate relationships. The
ﬁnal model included age, race, ADHD, conduct disorder, suicide attempt, number
Criminal Justice Studies 89
of court offenses, and previous conviction on a misdemeanor offense. This model
was tested for stability using repeated 80% validation samples from the data. In
each case, the same variables were identiﬁed and the estimates were within the con-
ﬁdence intervals of the ﬁnal model. The overall model was signiﬁcant at predicting
detention placement recidivism X
= 140.63[df = 7] p < .001 and correctly classiﬁed
88.5% of cases; Nagelkerke R
In all, seven variables signiﬁcantly predict juvenile offenders’ secure detention
placement recidivism. Predictors that made this recidivism more likely include a
previous diagnosis of conduct disorder (10 times more likely), a self-reported previ-
ous suicide attempt (almost three times more likely), age (for each additional year,
1.3 times more likely), and number of court offenses (for each additional offense,
Table 1. Descriptive statistics of variables.
Variable Yes (coded 1) No (coded 0)
Predictor Recidivitated to placement 71 (16.4%) 362 (83.6%)
Control Age Mean = 15.2
(SD = 1.6)
Gender Male 303 (70.0%) Female 130 (30.0%)
Race Caucasian 155
All other 278
Country Urban 343 (79.2%) Rural 90 (20.8%)
Education SBH 29 (6.7%) 404 (93.3%)
DH 5 (1.2%) 428 (98.8%)
SED 30 (6.9%) 403 (93.1%)
ADHD 103 (23.8%) 330 (76.2%)
Conduct disorder 40 (9.2%) 393 (90.8%)
33 (7.9%) 400 (92.4%)
Bipolar disorder 34 (7.9%) 399 (92.1%)
Depression 52 (12.0%) 381 (88.0%)
Adjustment disorder 10 (2.3%) 423 (97.7%)
PTSD 10 (2.3%) 423 (97.7%)
Anxiety disorder 11 (2.5%) 422 (97.5%)
Alcohol dependence 17 (3.9%) 416 (96.1%)
Substance use disorders 64 (14.8%) 369 (85.2%)
Suicide attempt 53 (12.2%) 380 (87.8%)
Mean = 1.3 (SD = 0.6)
Age at ﬁrst delinquency Mean = 14.6
(SD = 1.6)
Number of Court Offenses Mean = 4.4 (SD = 3.8)
237 (54.9%) 195 (45.1%)
Misdemeanor 358 (82.7%) 75 (17.3%)
Property crime 238 (55.0%) 195 (45.0%)
261 (60.4%) 171 (39.6%)
85 (64.9%) 346 (80.3%)
149 (34.5%) 283 (65.5%)
Note: Frequencies and percentages in parenthesis (n = 433).
1 missing cases.
2 missing cases.
90 C.A. Mallett et al.
1.5 times more likely). Conversely, predictors that made this recidivism less likely
to occur include race (Caucasians were more than twice as less likely), a previous
diagnosis of ADHD (more than twice less likely), and a conviction for a misde-
meanor (3.4 times as less likely) (see Table 3).
Table 2. Univariate logistic regression with recidivism (n = 433).
Variable Univariate odds ratio p-value
Male 1.442 .223
Caucasian .694 .053
Urban 3.273 .008
Education SBH 6.658 <.001
DH 1.000 .493
SED .346 .153
Mental health ADHD .920 .786
Conduct disorder 6.706 <.001
Oppositional deﬁant disorder 1.413 .439
Bipolar disorder 0.870 .782
Depression 2.094 .032
Adjustment disorder 2.237 .252
PTSD .560 .586
Anxiety disorder 1.952 .332
Alcohol dependence 1.097 .887
Substance use disorders 3.752 <.001
Suicide attempt 2.851 .001
Juvenile court Times adjudicated delinquent
Age at ﬁrst delinquency
Number of court offenses
Misdemeanor 1.036 .919
Property crime 2.204 .005
VCO 1.596 .077
Odds ratio is per unit increase.
1 missing case.
2 missing cases.
Table 3. Multivariable logistic regression predicting recidivism.
Independent variables B S.E. Wald Odds Ratio
Numbers of court offenses .379
.052 54.118 1.461
.122 3.769 1.267
.394 4.300 .441
.446 3.739 .422
Conduct disorder 2.365
.470 25.357 10.646
Suicide attempt 1.096
.448 5.999 2.993
.451 7.453 .292
1.956 10.807 .002
p < .05;
p < .01;
p < .001.
Criminal Justice Studies 91
These study ﬁndings are of interest because of the paucity of studies on predicting
juvenile offender recidivism to detention, and because of the conﬁrmatory
(expected) and unexpected predictors. There is nothing unexpected for juvenile
offenders who are older or have increasing number of court offenses being at a
higher risk for recidivism (Ashford & LeCroy, 1990; Brunner, 1993; Weaver &
Wootton, 1992; Webb, 2006). Likewise, convictions for misdemeanors are not
expected to be highly predictive of secure detention placement, or here for place-
ment recidivism (Archwarmety & Katsiyannia, 2000; Brunner, 1993; Frazier &
Cochran, 1986; Wierson & Forehand, 1995). Caucasian youth were also expected
to be less likely to be placed into secure detention, and in this case, recidivate to
placement. This disproportionate minority conﬁnement problem is well documented
across many jurisdictions (Feld, 1995; Leiber & Fox, 2005; Pope et al., 2002;
Webb, 2006), and was also found here.
Less clear though is the impact different mental health problems or disorders
have on delinquency and subsequent detention. Conduct disorder was the strongest
predictor of recidivism into detention placement, which has been found to varying
degrees by others (Hagan & King, 1997; McReynolds et al., 2010). However, other
researchers have often failed to differentiate by disorder and many times had
grouped their mental health problems into one measured variable (Campbell &
Schmidt, 2000; Fader et al., 2001; Lyons et al., 2001). This study’s data collection
allowed the identiﬁcation and differentiation of individual mental health diagnoses.
With the second disorder, ADHD was found to make recidivism less likely for the
juvenile offender. This has been measured before as impulsivity, though not in
detention prediction (Hagan & King, 1997). While both conduct disorder and
ADHD have externalizing features, in other words, acting out behaviors, impulsive
reactions, and over-reaction symptoms, they may be handled differently by juvenile
court personnel. It may be that the more severe aggression symptoms associated
with conduct disorder leads to additional difﬁculties or court charges for the youth;
while ADHD and its more impulsive or reactive symptoms do not lead to similar
The ﬁnding that a youth’s past attempted suicide made recidivism more likely
is unique. The interplay among youth suicidality, mental health disorders (particu-
larly aggressive symptoms), drug use, and delinquency is complex (Epstein &
Spirito, 2009; Maris et al., 2000; Thompson et al., 2007). This ﬁnding has impli-
cations for these at-risk and detained juvenile offenders. If ongoing research
(including community-based populations) conﬁrms this suicide risk, then numer-
ous youth-serving systems have an opportunity to intervene. The suicide attempts
were reported to have occurred while the youth was in the community, prior to
detention placement. These at-risk youth may have been in contact with the men-
tal health system, and undoubtedly most were enrolled in a school system, pre-
senting possible intervention and coordination points. In addition, standardized
suicide risk assessments by experienced practitioners could be utilized within the
detention centers; or better yet, at earlier juvenile justice system processing points
to identify those at risk for suicide. This may be very important because deten-
tion and incarceration are also risk factors for suicide, making the placement
experience itself potentially harmful for youth (Centers for Disease Control &
92 C.A. Mallett et al.
Indeed, what is known is that youth suicide and attempts continue to be an
alarming problem: seven percent of the general youth population has attempted sui-
cide in the past year (Centers for Disease Control & Prevention, 2009). Addition-
ally, up to 80% of youth with a serious mental health disorder do not receive
needed services (Kataoka, Zhang, & Wells, 2002). Even following a suicide
attempt, only 40% of youth needing mental health services receive help (Centers
for Disease Control & Prevention, 2009). This problem has been identiﬁed by pol-
icy-makers as exempliﬁed by the Garrett Lee Smith Memorial Act of 2004, which
mandates suicide screening and interventions for youth. The National Registry of
Evidenced-Based Programs and Practices identiﬁed Columbia University’s Teen-
Screen Program: Mental Health Check-ups for Youth (2003) as an ideal model. This
program focuses on screenings for mental health, suicidal ideation, and substance
use disorders in community settings such as schools and clinics (Horowitz, 2009).
Youth who screen positive are then referred to a variety of community agencies for
further assessment and or treatment. Community-based screenings for mental health
disorders, substance use, and suicide risk can be completed easily and effectively
using the Diagnostic Predictive Scales-8. Although their use has expanded in recent
years, evidenced-based screening and intervention programs are currently underuti-
lized in community-based settings (Payne, 2009). Given the ﬁndings from this cur-
rent research, the beneﬁts of this type of screening and subsequent referrals could
be even more effective, perhaps impacting juvenile delinquency and detention
This study has some important limitations worth noting. First, data were gathered
from existing case records. Hence, any errors within the case ﬁles are unknown.
Second, there may be some underdiagnosis or under-reporting of some of the inde-
pendent variables. For example, some youth could have struggled with the ADHD
symptoms prior to their ﬁrst arrest, but may have never been properly diagnosed.
Similarly, the suicide measurement relies on self-report by the youth. There is a
possibility that this variable was under-reported. Finally, although a random sam-
pling method was used, the ﬁndings are not widely generalizeable due to the nar-
row sampling frame.
A number of juvenile offenders experience multiple placements into secure deten-
tion. This detention is an oftentimes ineffective method for improving outcomes
for delinquent youth. This study evaluated a two county population of delinquent
youth to investigate the legal and extra-legal variables that predict recidivism. This
study found that youth with a previous diagnosis of conduct disorder, a self-
reported previous suicide attempt, those who were older, and those who had an
increased number of court offenses are more likely to recidivate. If the goal is to
reduce recidivism that leads to subsequent detention placement, it is imperative for
all stakeholders to understand these factors that may serve as early detection points
for concern and intervention. Utilizing a community-based suicide and mental
health screening and referral approach may help to identify and assist these high-
risk youth in receiving needed services prior to delinquency and formal juvenile
Criminal Justice Studies 93
Notes on contributors
Christopher A. Mallett is an Associate Professor of Social Work with research efforts that
focus on preventing children and adolescents with disabilities from being incarcerated.
Miyuki Fukushima is an Assistant Professor of Criminology with research efforts focused on
gender-speciﬁc differences and crime commission, and also cross-cultural studies.
Patricia Stoddard-Dare is an Associate Professor of Social Work with research focused on
the impact that drug and alcohol problems have on youth and families.
Linda Quinn is a Senior Associate Lecturer in the Department of Mathematics, specializing
in delinquency prevention statistical modeling and analysis.
Archwamety, T., & Katsiyannia, A. (2000). Academic remediation, parole violations, and
recidivism rates among delinquent youths. Remedial and Special Education, 21, 161–
Ashford, J.B., & LeCroy, C.W. (1990). Juvenile recidivism: A comparison of three predic-
tion instruments. Adolescence, 25, 441–450.
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