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

• Author: Juan R. Sandoval,Courtney S. Harding,Jesse Brey,Joel Miller,Krissinda Palmer,Carrie Maloney
• Published date: 01 March 2021
• DOI: 10.1177/0032885521991109
• Date: 01 March 2021
• Subject Matter: Articles

https://doi.org/10.1177/0032885521991109

The Prison Journal

2021, Vol. 101(2) 210 –233

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DOI: 10.1177/0032885521991109

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Article

Assessing Youth

Level of Service/Case

Management Inventory

Implementation

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.

Abstract

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.

Email: joel.miller@rutgers.edu

991109TPJXXX10.1177/0032885521991109The Prison JournalMiller et al.

research-article2021

Miller et al. 211

officers’ support for reforms, and the broader stakeholder environment.

Results are largely consistent with implementation science principles.

Keywords

risk assessment, risk-need-responsivity, juvenile justice, probation,

implementation

Introduction

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).