Assessing Youth Level of Service/Case Management Inventory Implementation Outcomes: Lessons from Five Diverse Pennsylvania Counties

AuthorJuan R. Sandoval,Courtney S. Harding,Jesse Brey,Joel Miller,Krissinda Palmer,Carrie Maloney
Published date01 March 2021
Date01 March 2021
Subject MatterArticles
The Prison Journal
2021, Vol. 101(2) 210 –233
© 2021 SAGE Publications
Article reuse guidelines:
DOI: 10.1177/0032885521991109
Assessing Youth
Level of Service/Case
Management Inventory
Outcomes: Lessons
from Five Diverse
Pennsylvania Counties
Joel Miller1, Carrie Maloney2, Courtney S. Harding3,
Krissinda Palmer1, Jesse Brey4, and Juan R. Sandoval5
Funding: This project was supported by Award No. 2015-R2-CX-0015, awarded by the
National Institute of Justice, Office of Justice Programs, US. Department of Justice. The
opinions, findings, and conclusions or recommendations expressed in this publication are
those of the authors and do not necessarily reflect those of the Department of Justice.
We examined implementation outcomes several years after rollout of
the Youth Level of Service/Case Management Inventory (YLS/CMI) risk/
need assessment (RNA) tool in five diverse Pennsylvania county juvenile
probation offices. Offices had policies to direct the use of the YLS/CMI,
and officers tended to view the tool favorably, complete it, and apply
it in their work. However, there were also variations in the extent of
implementation. These seemed related to differences in office leadership
and climate, implementation and quality assurance strategies, probation
1Rutgers University, Newark, NJ, USA
2East Stroudsburg University of Pennsylvania, East Stroudsburg, PA, USA
3PA Office of Attorney General, Philadelphia, PA, USA
4Temple University, Philadelphia, PA, USA
5University of California-Irvine, Irvine, CA, USA
Corresponding Author:
Joel Miller, School of Criminal Justice Center for Law and Justice, Rutgers University,
123 Washington St., Suite 549, Newark, NJ 07102, USA.
991109TPJXXX10.1177/0032885521991109The Prison JournalMiller et al.
Miller et al. 211
officers’ support for reforms, and the broader stakeholder environment.
Results are largely consistent with implementation science principles.
risk assessment, risk-need-responsivity, juvenile justice, probation,
Structured risk/needs assessment tools (RNAs) are the cornerstone of deci-
sion-making within the “Risk-Needs-Responsivity” (RNR) model (Andrews
et al., 2011; Bonta & Andrews, 2010). However, while a substantial body of
literature speaks to the predictive validity of RNAs (Gendreau et al., 1996;
Latessa et al., 2009; Vose et al., 2008), and research shows they contribute
to offender success when properly used (Luong & Wormith, 2011; Vieira
et al., 2009), the extent to which practitioners actually use RNAs in line
with RNR principles is variable in practice. While some short-term follow-
up evaluations of well-resourced pilot RNA implementations show
improvements in decision-making (Vincent, Paiva-Salisbury, et al., 2012;
Vincent et al., 2016; Young et al., 2006), research in more routine commu-
nity corrections contexts tends to indicate only partial adherence to RNA
policies (Miller & Maloney, 2013; Viglione et al., 2015). It is, therefore,
important to understand how these tools can be effectively implemented
and sustained in local settings. This article helps address this question by
documenting the patterns of use of the Youth Level of Service/Case
Management Inventory (YLS/CMI) RNA in local Pennsylvania counties
several years after its initial state-wide introduction, and examining the
processes that have shaped outcomes.
Background Literature
Risk/Need Assessment (RNA) Tools
Contemporary RNAs score individual characteristics to produce empirically
validated “risk” scores (Gottfredson & Moriarty, 2006), allowing clients to be
classified into groups reflecting their likelihood of recidivism. These tools
also score “needs”—namely dynamic risk factors that are susceptible to
change through intervention. “Fourth generation” RNAs support ongoing
assessment and case planning, and often measure “responsivity” factors that
affect client responses to treatment (Bonta, 1996).

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT