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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 logistic regression analysis indicate that seven variables significantly predict juvenile offenders’ recidivism placement, some expected and some unexpected. Predictors that made recidivism more likely include youth with a previous conduct disorder 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 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 juvenile court involvement or during delinquency adjudication.
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Factors related to recidivism for youthful offenders
Christopher A. Mallett*, Miyuki Fukushima, Patricia Stoddard-Dare and
Linda Quinn
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 signicantly 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-decit hyperactivity disorder diagnosis,
and a misdemeanor conviction. These ndings 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 eld 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 todays high cost of detainment and incarceration. Recently,
poor juvenile court outcomes for many youthful offenders, particularly serious
offenders, have inuenced 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 benet, and is increasingly being recognized as detrimental to
*Corresponding author. Email:
Criminal Justice Studies, 2013
Vol. 26, No. 1, 8498,
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).
Juvenile delinquency
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 decits, 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,
Moftt, & 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 difcult (Ford, Chapman, Hawke, & Albert, 2007; Green,
Gesten, Greenwald, & Salcedo, 2008; Gutman, Sameroff, & Cole, 2003). Predicting
detention placement and recidivism is particularly difcult.
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, identied predictive factors of importance
for this study include age at rst 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 dene 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 signicant (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 signicant (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 dif-
culties lead to other risk factors, poor decision-making, or the interaction of various
other risks (Grisso, 2008; Heilbrun, Goldstein, & Redding, 2005; Moftt & 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 signi-
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 specic childhood
mental health difculties 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; ODonnell, 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-decit hyperactivity
disorder (ADHD) have been found linked to later delinquency, evidenced through
physical aggression and stealing behaviors (Goldstein, Olubadewo, Redding, &
Lexcen, 2005; Moftt & 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 dened) have been found to
predict both less and more severe dispositions; whereas substance abuse leads to
more severe sanctions, including connement (Campbell & Schmidt, 2000; Fader,
Harris, Jones, & Poulin, 2001; Lyons, Baerger, Quigley, Erluch, & Grifn, 2001;
McReynolds, Schwalbe, & Wasserman, 2010; Schwalbe, Hatcher, & Maschi, 2009).
Youthful offenders needing secure placement pose a particularly difcult 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 identied mental health disorder (with 20% of these disorders severely
impacting functioning) (Grisso, 2008), over 35% of the youth have an identied
special education disability (Mears & Aron, 2003), and between 30 and 50% of the
youth have a signicant substance abuse disorder (Chassin, 2008).
Suicidal behaviors
This situation becomes more complicated when reviewing youth mental health
problems along with prior suicide attempt. Suicide is a signicant 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 signi-
cantly greater within this conned 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 ghts and
using weapons were found to be high risks for suicide ideation (Evans, Marte,
Betts, & Silliman, 2001). These acting out and antisocial behaviors, and specically
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
nding higher rates of ADHD in these populations (Ruchkin, Schwab-Stone, Kopo-
sov, Vermeiren, & King, 2003; Swensen, Kruesi, Allen, Beusching, & Secnik,
2002), and others not nding 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).
Justication 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 identied,
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 scal resources, and
improving public safety (Caldwell, Vitacco, & Van Rybroek, 2006; Gatti, Tremblay,
& Vitaro, 2009; Loughran et al., 2009; Soler et al., 2009).
Research question
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 rst 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 rst county and a sample size of 90 (over one year 2008)
from the second county would provide the appropriate ve percent margin of error
and 95% condence 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 collection
Data were collected from existing de-identied les provided by each countys
juvenile court. Each le contained ofcial records associated with each youth in the
study sample. Specically, juvenile court histories, probation supervision case les,
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).
Independent variables
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 rst 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 rst 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 deant 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 youths rst delin-
quency adjudication by licensed providers using DSM-IV criteria (yes = 1 indicates
a prior diagnosis). In addition, youths 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 youths age at rst delinquency (in years), the youths 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).
Dependent variable
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).
Data analysis
A bivariate correlation analysis among all variables was rst 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 signicant 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 signicant in bivariate relationships. The
nal 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 identied and the estimates were within the con-
dence intervals of the nal model. The overall model was signicant at predicting
detention placement recidivism X
= 140.63[df = 7] p < .001 and correctly classied
88.5% of cases; Nagelkerke R
In all, seven variables signicantly 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%)
Oppositional deant
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%)
Times adjudicated
Mean = 1.3 (SD = 0.6)
Age at rst 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%)
Personal crime
261 (60.4%) 171 (39.6%)
Drug crime
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
Control Age
1.271 .007
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 deant 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
2.012 .001
Age at rst delinquency
.781 .002
Number of court offenses
1.390 <.001
5.055 <.001
Misdemeanor 1.036 .919
Property crime 2.204 .005
Personal crime
1.052 .850
Drug crime
2.717 <.001
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
Age .237
.122 3.769 1.267
Caucasian .818
.394 4.300 .441
ADHD .862
.446 3.739 .422
Conduct disorder 2.365
.470 25.357 10.646
Suicide attempt 1.096
.448 5.999 2.993
Misdemeanor 1.232
.451 7.453 .292
Constant 6.429
1.956 10.807 .002
p < .05;
p < .01;
p < .001.
Criminal Justice Studies 91
These study ndings are of interest because of the paucity of studies on predicting
juvenile offender recidivism to detention, and because of the conrmatory
(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 connement 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 studys data collection
allowed the identication 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 difculties or court charges for the youth;
while ADHD and its more impulsive or reactive symptoms do not lead to similar
The nding that a youths 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 nding has impli-
cations for these at-risk and detained juvenile offenders. If ongoing research
(including community-based populations) conrms 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 &
Prevention, 2009).
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 identied by pol-
icy-makers as exemplied 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 identied Columbia Universitys 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 ndings from this cur-
rent research, the benets 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 les 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 rst 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 ndings 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
court involvement.
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-specic 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
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... Mowen, 2017; Spruit, van der Put, Gubbels, & Bindels, 2017), and impulsiveness and low levels of anger control (Khachatryan et al., 2018;Navarro-Pérez, Viera, Calero, & Tomás, 2020). Adolescents with behavioral disorders, a history of suicide, and those who have been exposed to more adverse childhood experiences have been reported as being more likely to relapse (Mallett, Fukushima, Stoddard-Dare, & Quinn, 2013;Wolff, Baglivio, & Piquero, 2017). ...
... The variable was coded dichotomously to differentiate between recidivists (1) and non-recidivists (0). This criterion is the most frequently used in the evaluation of this construct (Mallett et al., 2013;Robst, 2017;Van der Put et al., 2014). ...
... values indicated low multicollinearity between the variables (García, García, López, & Salmerón, 2015). Logistic binary regression was selected for prediction, as it is one of the most widely used procedures in the study of recidivism (Cox et al., 2018;Mallett et al., 2013;Nally et al., 2014;Robst, 2017;van der Put & De Ruiter, 2016). In this case, the fit of the regression model was analyzed with the Hosmer-Lemeshow goodness-of-fit test, and a significance greater than .05 ...
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Research with adolescent offenders is concerned with identifying risk and protective factors that influence recidivism and desistance from crime. A quantitative and cross-sectional investigation designed to examine the influence of risk and protective factors on recidivism in Colombian adolescents is presented. In seven regions of Colombia, a convenience sample was obtained, and 646 adolescents aged 14 to 19 years (M = 17.08; SD: 1.23; 15 % girls) belonging to the Sistema de Responsabilidad Penal para Adolescentes (SRPA) participated. The Communities That Care Youth Survey (CTC-YS) was used for the evaluation. It evaluated a broad set of risk and protective factors identified through the community, school, family, peer group, individual conditions, and behavioral outcomes, including drug use, antisocial behavior, and delinquency. Descriptive analyses were conducted, and all CTC-YS factors were correlated with antisocial behavior. The results show varying degrees of relationship between the factors assessed and antisocial behavior. Binary logistic regression was used to determine which risk and protective factors influence recidivism. It was noted that favorable parental attitudes towards drug use and antisocial behavior, early onset of drug use, low school engagement, and interaction with antisocial peers increases the probability of recidivism. Recidivism was identified as being affected by, among other factors, favorable parental attitudes toward drug use and antisocial behavior, early onset of drug use, and low school engagement. It was also observed that beliefs in a moral order, opportunities for prosocial school participation and lower drug use frequency reduce the probability of recidivism. According to the results, the factors that influence criminal recidivism are multiple, and social, family, school, and individual factors need to be addressed. The need to intervene in attitudes favorable to antisocial behavior on the part of parents, strengthen school services, and carry out treatment for drug use to favor the reduction of recidivism in Colombian adolescents is discussed.
... Despite the close relationship between mental disorders and crime, what is known about the effects of mental disorders on recidivism and re-incarceration is limited and has controversial results. While few studies reported that ADHD was not related to offending or recidivism (Grieger & Hosser, 2012;Mallett, Fukushima, Stoddard-Dare, & Quinn, 2013;Mordre, Groholt, Kjelsberg, Sandstad, & Myhre, 2011), most of the studies agreed that ADHD had an important effect on youth criminality (Bessler, Stiefel, Barra, Plattner, & Aebi, 2019;Mannuzza, Klein, & Moulton, 2008;Philipp-Wiegmann et al., 2018;Wibbelink et al., 2017). A follow-up study reported that the diagnosis of ADHD predicted recidivism among a sample of young incarcerated men (Philipp-Wiegmann et al., 2018). ...
... The predictive value of dimensions of ODD for CD is also less clear. Early-onset CD displays a stronger relation with recidivism (Boduszek et al., 2014;Mallett et al., 2013;Tarolla, Wagner, Rabinowitz, & Tubman, 2002;Wibbelink et al., 2017). In our study, ODD was statistically more common in the re-incarceration group although it was not a predictor for re-incarceration in regression analysis. ...
Background Adolescents involved in the legal system are known to be under elevated risk for repeat offending. There may be many reasons for recidivism. Specifically, we aim to investigate the clinical, socio-demographic, and familial factors and psychopathology among adolescents in a penal institution and to determine risk factors for re-incarceration. Methods This single-center cross-sectional survey was conducted at Tarsus Closed Penal Institution for Children and Youth. This institution is for males only, and all male adolescents detained at the center within the study period were evaluated with semi-structured interviews (K-SADS-PL). The adolescents completed Meaning and Purpose of Life Scale, The EPOCH measure of Adolescent Well-being, Family Sense of Belonging Scale, Children's Alexithymia Scale, Beck Depression Inventory, and State-Trait Anxiety Inventory for themselves. Descriptive and inferential analyses were used. P was set at 0.05. Results Ninety adolescent offenders with a mean age of 16.6 years (S·D = 0.7) were enrolled. Mean age at first offense was 14.6 years (S·D = 2.1). The most common reason for offenses was reported as as being with peers who were offenders, too (57.8%). Most common diagnoses were substance use (36.7%), attention deficit/hyperactivity disorder (33.3%), and conduct disorder (26.7%). Rates of offending and conviction in first-degree relatives were 62.2% and 60.0%, respectively, and most of the adolescents had at least one peer with a criminal record (n = 71, 78.9%). Re-incarcerated adolescents had lower education, committed more violent crimes, and reported elevated use of substances, suicide attempts, and psychopathology. However, in regression analysis, age of onset was the sole predictor of re-incarceration. Conclusion Turkish male adolescents in forensic settings may be screened for externalizing disorders and referred for treatment. Re-incarcerated Turkish youth may be more susceptible to peer influence, substance use and externalizing disorders. It may be prudent to systematically screen offending youth for psychiatric disorders regardless of the individual's request for treatment and refer identified cases to treatment. Integration of child and adolescent psychiatrists with penal institutions serving youth may help in this regard.
... Likewise, [40] found out that psychopaths continued to recidivate at a higher rate than nonpsychopaths even beyond age 40. On the other hand, [41] emphasized that recidivism is more likely to include youth with a previous conduct disorder diagnosis, a self-reported previous suicide attempt, age, and the number of court offenses. Conversely, predictors that made recidivism less likely include race a previous attention-deficit hyperactivity disorder diagnosis, and a misdemeanor conviction. ...
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Recidivism is an offense committed by a person who at the time of his trial for one crime has been previously convicted by a final judgment of another crime. From this perspective, the researchers are interested to explore the lived experiences and untold stories of repeat offenders. The study focuses on three parts; the informants’ experiences in the pillars of the criminal justice system; the impacts of incarceration on the lives of the offender; and the reasons for reoffending. A qualitative research design using a phenomenological approach was used in the conduct of the study through an in-depth interview with the informants. The sample informants which comprises of ten recidivists and inmates of selected city jails Negros Occidental, Philippines participated in the interview through purposive sampling using the inclusion criteria set by the researchers. The data was collected using audiotaping of interviews. Audiotapes were then transcribed where data from transcriptions were analyzed to describe the richness of the informants’ experiences. Out of the transcribed and analyzed data, six major themes emerged. The Cry of the Suspect, Light within the Darkness, You Reap what you Sow, Blessing in Disguise, Many are Bad Associates But Few are Good Mentors, and Corruption of the Mind. Key findings from the study suggest coordination and cooperation among the pillars of the criminal justice system come up with a very comprehensive and sustainable rehabilitation program with proper and effective implementation, monitoring, and evaluation. Thereby, recommendations for change are provided in the emerging themes to address the phenomenon.
... What is however, surprising, is the lack of standard assessment protocols in the juvenile justice system to systematically assess for trauma in order to treat trauma as a primary clinical need. Given the dire long-term life outcomes related to justice-involved adolescents (Mallett 2013;Seigle et al. 2014;Willison et al. 2013), the timing to begin addressing such a critical treatment need may prove crucial. Unfortunately, this issue is not exclusive to juvenile justice, as similar calls have been made to integrate the assessment of trauma into child welfare programs to ensure such symptoms can be effectively treated (e.g., Milne and Collin-Vezina 2015;Nader 2008). ...
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Objectives This investigation evaluated trauma-focused cognitive behavioral therapy (T-F CBT) with detained male adolescents with serious offending behaviors. Methods One hundred and seventeen youth with trauma symptoms participated in an average of twelve T-F CBT individual sessions. Trauma symptoms were assessed prior to and following treatment using the Trauma Symptom Checklist for Children (TSCC) and a paired sample t-test was used to analyze changes from pre- to posttest. Results Trauma symptoms in five of the six scales (i.e., anger, depression, anxiety, posttraumatic stress, dissociation) were significantly reduced following treatment. Conclusions T-F CBT may be effective in reducing general trauma symptoms among detained adolescents; assessing and treating trauma symptoms are recommended as standard protocol in the juvenile justice system; and T-F CBT may be effective without caregiver participation.
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Background Delinquent behaviours among youth harms health and social trajectories, and public health broadly. Despite evidence that engaging in and being victimized by delinquent behaviours often cluster, most studies have examined the clustering of delinquent behaviours or victimization experiences independently. Information on patterns of co-occurrence is crucial to design appropriate interventions.Objectives The primary purpose was to identify latent classes of delinquency and victimization among youth from the general population. The secondary purpose of this study was to examine associations of individual, household, and classroom covariates on latent class membership.Method The sample consisted of 1948 youth aged 4–14 from the 2014 Ontario Child Health Study. Latent class analysis was performed to identify patterns of delinquent behaviours and experiences of victimization, while multinomial regression was conducted to examine how covariates were associated with likelihood of class membership.ResultsThe analysis identified four classes of youth in the OCHS sample: (1) low delinquency and low victimization (75.4%), (2) moderate victimization and moderate school delinquency (7.8%), (3) high victimization and moderate home delinquency (11.8%), and high victimization and high home and school delinquency (5.0%). Youth sex, household income, ethnicity, parental education, and parental depression were associated with differences in class membership.Conclusions Approximately one quarter of youth engaged in delinquent behaviours, with patterns of co-occurrence suggesting these youth engage in delinquent behaviours and are victimized by delinquent behaviours across environments. Interventions should approach youth delinquency and victimization as a spectrum of clustered behaviours and experiences in these environments.
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In recent years, the incidence of juvenile delinquency has been increasing, posing a threat to the well-being of families and society. This urges the understanding of the involvement in delinquent behaviours among young male juvenile delinquents. The main objective of this study was to explore juvenile delinquent involvement among former young male juvenile delinquents. A phenomenology qualitative research design was utilised to explore the participants’ lived experiences and their involvement in juvenile delinquency. Eight (8) former young male juvenile delinquents aged 26 years old and below participated in this research. The non-probability sampling technique of snowball sampling was utilised to select the participants. An intake form was administered to obtain the participants’ socio-demographic information. A semi-structured interview questions were used to explore the participants’ involvement in juvenile delinquency. The analysis of the data collected from former young male juvenile delinquents using Interpretative Phenomenological Analysis (IPA) revealed several themes that led to such delinquent behaviours; namely, lucrative business, recidivism, and peer pressure. The results indicate that the participants’ initial involvement was influenced by their social circle or peer group, the challenges of relapse, and the advantages they gained from their unlawful conduct. The analysis of the results helps us determine the need for early intervention in order to prevent the increasing percentage of juvenile delinquency among adolescents in Malaysia.
This study investigated item- and test-level functioning of the Structured Assessment of Violence Risk in Youth (SAVRY) and differential item functioning (DIF) across gender and race/ethnicity in justice-involved youth (JIY) using item response theory analysis. Participants were 868 JIY (23.7% female; 26.9% White, 50.9% Black, and 22.2% Hispanic) in pre-trial detention centers in Connecticut. Results obtained from the application of the graded response model showed that the SAVRY items were not equally discriminating JIY with varying levels of the latent trait, with “Poor compliance” as the most discriminating item and “History of self-harm or suicide attempts” as the least discriminating item. At the test level, the SAVRY provided precise (reliable) information about the latent trait for the majority of JIY whose latent trait between two standard deviations below and above the mean. Results of DIF revealed that six items operated inconsistently between White, Black, and Hispanic JIY, among which two items also functioned differentially across gender.
A plenitude of research on juvenile recidivism exists within the criminological literature, and some scholars have suggested using the Latino paradox and racial invariance thesis to make sense of racial and other disparities in recidivism. However, there is an extremely limited body of literature that tests one or both of these theories on juvenile recidivism, and the research which does exist is limited in its generalizability. To address this gap, we use statewide data from Virginia’s Department of Juvenile Justice over 5 years to test the Latino paradox and racial invariance thesis. Furthermore, given the nested nature of the data (juveniles within counties/independent cities), we merged county-level data from the Virginia State Police and American Community Survey into the data and used two-level hierarchical generalized linear models to analyze the data. Our findings largely supported the Latino paradox and offered some support for the second interpretation of the racial invariance thesis. Implications for theory and recommendations for public policy are discussed.
Just as delinquency case rates skyrocketed more for girls than boys in the 80s and 90s, recent declines show, yet again, that advances in youth intervention favor boys over girls. Given that boys continue to outnumber girls involved with the system, it is perhaps not surprising that juvenile courts tend to rely on gender-neutral programming; in effect treating girls like boys. Drawing a sample of youths (N = 3,901) processed by a Midwestern juvenile court filed between 2012 and 2016, the present study shows that gender neutral diversion programs yield lower recidivism rates for boys, but not for girls.
Research from 2008 to 2018 related to risk and protective factors for juvenile delinquency was reviewed to provide an update on the relevance of various factors. Based on the review, some important factors that are consistently related to increased delinquent behaviors include substance use, trauma, delinquent peers, and neighborhood characteristics. The reviewed research also identified many moderators and mediators for risk factors (e.g., genes), indicating that delinquency is unlikely to be predicted with variables from one domain. Within the searched timeframe, research on variables that decrease the likelihood of delinquency (i.e., protective factors) was limited when compared to the research examining risk factors. Nonetheless, familial and neighborhood factors were observed to have protective effects. Some of the methodological characteristics that could clarify conflicting findings between studies were explored, however all reviewed studies could not be contrasted with one another. Therefore, the original references may be consulted to supplement the conclusions drawn here. Lastly, we attempted to incorporate all the relevant research from the searched timeframe into this chapter, but it is possible that some references were overlooked or excluded.
The term conduct disorder refers to a persistent pattern of antisocial behaviour in which the individual repeatedly breaks social rules and carries out aggressive acts which upset other people. It is the commonest psychiatric disorder of childhood across the world, and the commonest reason for referral to child and adolescent mental health services in Western countries. Antisocial behaviour has the highest continuity into adulthood of all measured human traits except intelligence. A high proportion of children and adolescents with conduct disorder grow up to be antisocial adults with impoverished and destructive lifestyles; a significant minority will develop antisocial personality disorder (psychopathy). The disorder in adolescence is becoming more frequent in Western countries and places a large personal and economic burden on individuals and society.
The relative contribution of court-ordered mental health reports and legal factors in determining young offenderdispositions was examined. Poor quality of home conditions and severity of substance abuse, as coded from mental health reports, significantly increased the odds of receiving custody over a term of probation once legal factors were controlled. Legal factors significantly predicted probation length, whereas mental health factors only made a small contribution through externalizing behavior problems. The overall concordance between clinicians' mental health recommendations and court dispositions was 67.5%. Although these results suggest mental health reports influence disposition decision making, this influence is more limited than expected given that the purpose of these reports is to assist such decision making. The implications and limitations of these findings are discussed.
This book is intended for neurologists, orthopedic surgeons, pediatricians, physiatrists, physical therapists, and others who want to acquire a better understanding of normal and abnormal gait patterns in children and adolescents. In the first chapter, the author describes in detail the instrumentation protocol and the data analysis system used in the Motion Analysis Laboratory, Children's Hospital and Health Center, San Diego. In the second chapter, normal gait and the changes that occur with age are described. The data base for agerelated parameters are derived from gait studies of 464 normal children between the ages of 1 and 7; and 15 adults between 19 and 40 years of age. The next ten chapters are devoted to case reports of orthopedic and neurologic problems with gait studies before and after intervention providing objective data regarding the efficacy of procedures such as physical therapy, orthopedic surgery, orthotic devices, or a combination of these.
visit Easy Access to Juvenile Court Statistics for the most up to date statistics or the Juveniles in Court section of the Statistical Briefing Book Counts and trends The number of delinquency cases handled in U.S. juvenile courts remained virtually unchanged from 2000 through 2007. An esti-mated 1.7 million delinquency cases were handled in juvenile courts nationwide in 2007. During the two decades since 1985, however, the juvenile court caseload has been anything but static. From 1985 through 1997, the number of delinquency cases han-dled climbed steadily (61%) and from 1997 through 2007, the delinquency caseload dropped 11%. Juvenile courts handled 44% more cases in 2007 than in 1985. This overall pattern of increase followed by decline and then lev-eling is the result of the trends of various offense categories com-bined. Public order offense cases increased steadily from 1985 through 2007 (142%). Person offense cases increased through 1997 (124%) and then leveled off. Drug law violation cases were relatively flat from 1985 through 1993 (increasing 17%), rose sharply (up 109% from 1993 through 1997), and then leveled off through 2005 (up just 1% from 1997 through 2007). Although these patterns were different, each showed generally increasing trends. In contrast, property offenses showed quite a different trend. Between 1985 and 1992, the number of property offense cases increased 26%. After 1992, the number of property offense cases declined steadily (down 38% from 1992 through 2007). Thus, property offenses were the one general offense category that showed an overall decline from 1985 through 2007 (down 23%).