Predicting Recidivism Among Released Juvenile Offenders in Florida

• Published date: 01 January 2018
• Author: Katherine Jackowski,Mark A. Greenwald,Carter Hay,Alex O. Widdowson,Meg Bates,Michael T. Baglivio
• Date: 01 January 2018
• DOI: 10.1177/1541204016660161
• Subject Matter: Articles

Article
Youth Violence and Juvenile Justice
2018, Vol. 16(1) 97-116
Predicting Recidivism Among
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Released Juvenile Offenders
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DOI: 10.1177/1541204016660161
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in Florida: An Evaluation of
the Residential Positive
Achievement Change Tool
Carter Hay1, Alex O. Widdowson1, Meg Bates2, Michael T. Baglivio3,
Katherine Jackowski3, and Mark A. Greenwald2
Abstract
Each year in the United States, as many as 100,000 juvenile offenders are released after completing a
residential placement. A significant task for researchers is to identify the factors that explain var-
iations in recidivism. This study considers this by evaluating the predictive validity of the Residential
Positive Achievement Change Tool (R-PACT), a fourth-generation risk assessment instrument
adopted by Florida for use in all of its juvenile residential facilities. The R-PACT includes a wide
variety of static and dynamic risk and needs scales that are used here to predict reoffending among
4,700 released juvenile offenders in Florida. We devote special attention to (1) whether R-PACT
scales typically predict reoffending and (2) whether the R-PACT’s predictive validity varies across
different subgroups of offenders. In considering these questions, we also consider whether the
predictive risk and protective factors in prior research are predictive in the R-PACT as well. The
analysis revealed relatively strong support for the R-PACT, but there were nuanced exceptions to
that pattern. We discuss the implications these findings have for assessing risk, monitoring progress
among residential youth, and predicting reoffending.
Keywords
juvenile reentry, risk assessment, Residential Positive Achievement Change Tool, juvenile justice
The U.S. approach to juvenile crime includes a heavy reliance on incarceration, with roughly 60,000
juvenile offenders confined in a criminal or juvenile justice facility each day (Hockenberry, 2014). A
key implication of this pattern involves the daunting challenge of youth reentry (Mears & Travis,
2004). Specifically, because most juvenile residential placements involve short stays of 1 year or
1 Florida State University, Tallahassee, FL, USA
2 Florida Department of Juvenile Justice, Tallahassee, FL, USA
3 G4S Youth Services, LLC, Tampa, FL, USA
Corresponding Author:
Carter Hay, Florida State University, 112 S. Copeland, Tallahassee, FL 32306, USA.
Email: chay@fsu.edu

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Youth Violence and Juvenile Justice 16(1)
less (Hockenberry, 2014), nearly every residential confinement produces a reentry case not long
thereafter. Thus, as many as 100,000 juveniles are returned to their communities each year after
completing a residential confinement (Snyder, 2004).
Much research has examined how these offenders fare upon release. Although recidivism esti-
mates vary across states and different studies, the general pattern for released youth is that within 2
years, roughly 70% are rearrested for a new offense, 50% receive an adjudication or conviction, and
20% return to a correctional institution (Annie E. Casey Foundation, 2011; Krisberg, 2011; Trulson,
Haerle, DeLisi, & Marquart, 2011). Such high recidivism contributes to the perception that there is a
revolving door of juvenile justice in which young offenders often reenter the system not long after
exiting it (Benda & Tollett, 1999). However, these recidivism rates also reveal genuine variation—
although many released juvenile offenders persist with crime and reenter the justice system, many do
not. In short, real instances of desistance occur among juvenile offenders and that pattern is con-
firmed in ethnographic and self-report research that goes beyond official measures of recidivism
(Basto-Pereira, Comec¸anha, Ribeiro, & Maia, 2015; Panuccio, Christian, Martinez, & Sullivan,
2012; Steinberg, Cauffman, & Monahan, 2015).
A task for researchers is to explain these variations. Many studies reveal that static background
characteristics—especially prior offending history and membership in high-risk demographic
subgroups—consistently predict recidivism (Lattimore, Macdonald, Piquero, Linster, & Visher,
2004; Trulson, DeLisi, & Marquart, 2011). Recent research and risk assessment tools also prior-
itize dynamic attributes—values, social commitments, and interpersonal skills that are shaped by
new experiences and that may change during the residential stay (Labrecque, Smith, Lovins, &
Latessa, 2014; McGrath & Thompson, 2012). Considering dynamic attributes builds on the idea
that juvenile offenders are at a point in life when biological, psychological, and social changes are
common, perhaps especially for those with justice system involvement. With that in mind, state-
of-the-art risk assessment tools now track static and dynamic risk factors over time to inform
service plans and treatment delivery. Prominent such tools include the Youth Level of Service/
Case Management Inventory (YLS/CMI), the Structured Assessment of Violence Risk in Youth
(SAVRY), and the Psychopathy Checklist (PCL), all of which have received relatively strong
support in validation research (Fass, Heilbrun, Dematteo, & Fretz, 2008; Olver, Stockdale, &
Wormith, 2009).
It bears emphasizing, however, that other risk assessment tools are prominently used and also
require rigorous validation. This includes the Residential Positive Achievement Change Tool
(R-PACT), which is the focus of the present study. The R-PACT is a substantively important
tool—the state of Florida now uses it in all juvenile residential facilities, which house roughly
3,700 juvenile offenders on a given day (Hockenberry, 2014). The R-PACT is administered multiple
times during the residential stay, and data are collected not just on the static risk factors prioritized in
prior research but also on dynamic risks involving such things as social relationships, academic and
work performance, attitudes, and social skills. Its statewide adoption is a recent development,
however, and research is needed to evaluate its predictive validity.
This study provides a needed validation of the R-PACT. We examine its ability to predict
reoffending in a sample of 4,700 released juvenile offenders in Florida. The analysis focuses on
two questions. First, do R-PACT domain scales significantly predict reoffending? In considering this
issue, we especially attend to whether significant effects of dynamic domains are maintained even
when accounting for key static background factors. Second, is the R-PACT’s predictive validity
similar across different subgroups of offenders, including those that vary in terms of age, sex, race
and ethnicity, and prior offending? In considering these questions, this study can reveal whether the
R-PACT is accomplishing the risk assessment goals envisioned when it was adopted system-wide by
such a large state juvenile justice system. Such research is relevant not just to Florida but also to any
jurisdiction that will be adopting similar assessment tools, and it can inform the broader study of

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Hay et al.
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juvenile reentry by providing evidence on which risk factors are most consequential. As Taxman and
Caudy (2015) have argued, and as we describe below, there still is uncertainty on this issue.
Prior to examining these issues, we first describe prior efforts to predict and understand juvenile
recidivism, highlighting the distinguishing factors of recent approaches. We then describe the
unique history of Florida, emphasizing its evolving approach to juvenile justice and risk assessment.
That evolution has produced in recent years a systematic statewide commitment to assessing the
risks and needs of juvenile offenders and better understanding how these things affect recidivism.
Predicting Reoffending
Efforts to predict juvenile recidivism have been undertaken for decades, but the methods used have
changed over time. Andrews, Bonta, and Wormith (2006) described this in identifying four genera-
tions of approaches, the first of which relied on unstructured professional judgments about a youth’s
odds of reoffending. This ‘‘gut instinct’’ approach ultimately gave way to actuarial second-
generation approaches that became quite common in the 1980s and 1990s. Second-generation tools
rely on empirically based inventories that especially emphasize static risk factors like prior offend-
ing history, demographic affiliations, and indicators of a troubled social history. As Andrews and his
colleagues (2006) note, this shift was critical—the ability of second-generation tools to predict
reoffending is substantially greater than that observed with first-generation tools.
In more recent years, third- and fourth-generation assessment tools have become prominent.
Their most distinctive qualities are (1) attention to a wide array of criminogenic risks and needs
and (2) a dynamic emphasis that more fully considers offender change. Thus, whereas second-
generation tools were mostly concerned with static aspects of an offender’s prior history, third- and
fourth-generation tools more fully consider an ‘‘offender’s current and ever-changing situation’’
(Bonta & Andrews, 2007, p. 4), often in reference to the ‘‘central eight’’ areas of risk and need
thought to best predict reoffending (Andrews, Bonta, & Wormith, 2006). This list includes risks in
the areas of prior offending, antisocial personality, antisocial beliefs, and antisocial peers, along with
needs in the areas of family, leisure and recreation, substance use, and employment/education.
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