Prediction of Recidivism With the Youth Level of Service/Case Management Inventory (Reduced Version) in a Sample of Young Spanish Offenders

• Published date: 01 August 2018
• Author: Keren Cuervo,Lidón Villanueva
• DOI: 10.1177/0306624X17741250
• Date: 01 August 2018
• Subject Matter: Articles

https://doi.org/10.1177/0306624X17741250

International Journal of

Offender Therapy and

Comparative Criminology

2018, Vol. 62(11) 3562 –3580

© The Author(s) 2017

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DOI: 10.1177/0306624X17741250

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Article

Prediction of Recidivism

With the Youth Level of

Service/Case Management

Inventory (Reduced Version)

in a Sample of Young Spanish

Offenders

Keren Cuervo1 and Lidón Villanueva1

Abstract

Intervention in youth recidivism is critical in helping prevent young people from

continuing their criminal career into adulthood, on a life-course-persistent trajectory.

Andrews and Bonta attempt to provide an explanation of risk and protective factors

using a conversion of the Youth Level of Service/Case Management Inventory (YLS/

CMI), which predicts recidivism. In this study, scores have been obtained from 382

adolescents (M age = 16.33 years) from the juvenile court, to check the ability of a

reduced version of the YLS/CMI, to predict recidivism. The outcome variables for

recidivism were examined in the 2-year follow-up period, after their first assessment

in the court. The risk factors showed good levels of recidivism prediction. Recidivists

obtained significant higher mean total risk scores than nonrecidivists in the reduced

(M = 6.54, SD = 2.44; M = 3.66, SD = 2.85), with areas under the curve (AUCs)

ranging from .601 to .857. The factors that emerged as the most discriminative were

education/employment, criminal friends, and personality. All the protective factors

differentiated between recidivists and nonrecidivists. The results, therefore, showed

that this reduced version would be capable of predicting youth recidivism in a reliable

way.

Keywords

recidivism, Spanish youth offenders, YLS/CMI, reduced version

1Jaume I University, Castellón de la Plana, Spain

Corresponding Author:

Keren Cuervo, Department of Developmental, Educational and Social Psychology and Methodology,

Jaume I University, Av. de Vicent Sos Baynat, s/n, 12071 Castellón de la Plana, Spain.

Email: cuervo@uji.es

741250IJOXXX10.1177/0306624X17741250International Journal of Offender Therapy and Comparative CriminologyCuervo and Villanueva

research-article2017

Cuervo and Villanueva 3563

Predicting criminal behavior, and in particular juvenile crime, is an issue of major

concern in today’s society. Although the general level of youth offending does not

appear to have increased, there has been a rise in some violent crimes in recent years

(Benavente, 2009; Capdevila, Ferrer, & Luque, 2005; Pérez, 2010; Puzzanchera &

Adams, 2011; Smit & Bijleveld, 2015). Similarly, a large percentage of crimes are

committed by a small percentage of juvenile offenders (Cottle, Lee, & Heilbrun,

2001). Furthermore, in the Spanish juvenile system, rates of recidivism for general

youth offenders, during follow-up periods of 2 to 4 years, stand at between 26% and

40% (Acosta, Muñoz de Bustillo, Martín, Aragón, & Betancort, 2012; Bravo, Sierra,

& Del Valle, 2009; Cuervo, Villanueva, & Prado-Gascó, 2017; García-España, García

Pérez, Benítez Jiménez, & Pérez Jiménez, 2011; San Juan & Ocáriz, 2009). In this

context, intervention in youth recidivism is critical to help prevent young offenders

from continuing their criminal career into adulthood, on a life-course-persistent trajec-

tory (Moffitt, 2006).

Detecting risk and protective factors has become crucial in preventing and reducing

crime. A risk factor for offending is a variable that predicts a high probability of later

offending (Farrington, Loeber, & Ttofi, 2012; Ribeaud & Eisner, 2010). Meanwhile,

protective factors can be considered variables that predict a low probability of offend-

ing among persons exposed to risk factors. They have been related to desistance in

different prospective longitudinal studies (Farrington et al., 2012; Hartman, Turner,

Daigle, Exum, & Cullen, 2009; Ttofi, Farrington, Piquero, & DeLisi, 2016). Social

learning theories (Catalano & Hawkins, 1996) try to structure the wide range of risk

and protective factors in accordance with their theoretical assumptions. These theories

are mainly based on the fact that behavior is interiorized through interaction with the

environment, so criminal conduct will be more likely in youth who perceive more

rewards for performing an antisocial activity than a prosocial one. One perspective of

social learning theories attempts to provide an in-depth explanation of the theoretical

frame of risk and protective factors through Andrews and Bonta’s (2010) general per-

sonality and social psychological model of criminal conduct. This model considers the

individual as an agent that interacts with his or her environment, and who cannot be

explained without this interactive, dynamic context. At the same time, in a wider con-

text, the youth is also influenced by sex, age, or race (Shepherd, 2015; Zhang, 2016).

Besides these contextual variables, the model includes some individual factors,

which are considered the best predictors of recidivism. These variables or factors were

antisocial attitudes, antisocial friendships, an antisocial personality pattern, a history

of previous offences (considered “the Big Four”), plus deficient family circumstances,

education and employment, substance abuse, and free time for leisure and recreation.

Taken together, these factors are referred as “the Central Eight” and are the same as

those put forward by Hoge and Andrews (2006) in the Youth Level of Service/Case

Management Inventory (YLS/CMI). This inventory enables young people to be clas-

sified as being at low, moderate, high, or very high risk of recidivism. Several studies

show the validity of the inventory (Anderson et al., 2016; Catchpole & Gretton, 2003;

Flores, Travis, & Latessa, 2004; Rennie & Dolan, 2010). Results from different meta-

analyses support the predictive accuracy of the YLS/CMI even with female and