Resistance to Antisocial Peers in Adolescents Found Not Criminally Responsible on Account of Mental Disorder: Predictive and Incremental Validity With the VRAG-R

• DOI: 10.1177/00938548221077949
• Author: Nicol Patricny,Jacqueline R. Pei,Andrew M. Haag
• Published date: 01 May 2022
• Date: 01 May 2022
• Subject Matter: Articles

CRIMINAL JUSTICE AND BEHAVIOR, 2022, Vol. 49, No. 5, May 2022, 681 –699.

DOI: https://doi.org/10.1177/00938548221077949

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© 2022 International Association for Correctional and Forensic Psychology

681

RESISTANCE TO ANTISOCIAL PEERS IN

ADOLESCENTS FOUND NOT CRIMINALLY

RESPONSIBLE ON ACCOUNT OF MENTAL

DISORDER

Predictive and Incremental Validity With the

VRAG-R

NICOL PATRICNY

ANDREW M. HAAG

JACQUELINE R. PEI

University of Alberta

There has been a recent theoretical shift toward the inclusion of protective factors within risk assessment. However, there is

a lack of empirical evidence surrounding this practice in unique forensic populations. Using a long-term retrospective design,

we examined the predictive and incremental validity of the protective factor resistance to antisocial peers and the Violence

Risk Appraisal Guide—Revised in 119 individuals who were found Not Criminally Responsible on Account of Mental

Disorder (NCRMD) as adolescents. The results indicated that resistance to antisocial peers significantly predicted general

nonrecidivism (area under the curve [AUC] = .647) and violent nonrecidivism (AUC = .654) in the long term (maximum

35-year follow-up). Incorporation of resistance to antisocial peers into the Violence Risk Appraisal Guide—Revised did not

significantly increase the incremental validity for general or violent recidivism. Using logistic regression, adolescents’ age at

their NCRMD start date had no significant relationship with recidivism and was unrelated to the protective effect of resistance

to antisocial peers.

Keywords: violence risk assessment; protective factors; juvenile offenders; adolescence; desistance; predictive validity;

recidivism; risk assessment

INTRODUCTION

RISK ASSESSMENT

Within the field of forensic psychology and forensic psychiatry, evaluating individuals

who have previously offended for their likelihood of possible recidivism is referred to as

risk assessment (Kocsis, 2011). The field of risk assessment has made notable progress in

AUTHORS’ NOTE: This WCHRI graduate studentship has been funded through the Stollery Children’s

Hospital Foundation through the Women and Children’s Health Research Institute. AUTHORS’ NOTE:

Correspondence concerning this article should be addressed to Nicol Patricny, University of Alberta, 6-102

Education North, 11210 - 87 Ave, Edmonton, Alberta, Canada T6G 2G5; e-mail: patricny@ualberta.ca.

1077949CJBXXX10.1177/00938548221077949Criminal Justice and BehaviorPatricny et al. / Resistance to Antisocial Peers

research-article2022

682 CRIMINAL JUSTICE AND BEHAVIOR

terms of clinicians’ ability to accurately predict individuals’ risk of violence and offending.

First-generation risk assessment approaches, which involved unstructured professional

judgment (Andrews et al., 2006) and reliance on subjective decision-making, had poor pre-

dictive accuracy (Bonta & Andrews, 2017). Second-generation actuarial prediction, which

is based on static risk factors empirically related to risk (Andrews et al., 2006), outperforms

clinical judgment (Hanson & Morton-Bourgon, 2009; Harris et al., 2015). Third-generation

assessments measure offender criminogenic needs that can be targeted through treatment

(Bonta & Andrews, 2017). Structured professional judgment tools, while not originally

considered third generation (see Bonta, 1996), have also been defined as such by recent

scholars, given their inclusion of both static and dynamic risk factors and their ability to

inform management decisions (Abidin et al., 2013; De Bortoli et al., 2017). Through the

evolution of risk assessment practice, researchers have helped to establish risk assessment

approaches and tools with empirical validity and clinical utility.

Research pursuits of alternative strategies and theories to performing risk assessment

must always occur in an ethically informed rational and empirical manner (Andrews et al.,

2006). Although there is much evidence supporting the accuracy of actuarial prediction

over unstructured clinical judgment (Ægisdóttir et al., 2006; Grove et al., 2000; Harris

et al., 2015), the quality of this evidence has some limitations due to research biases (Viljoen

et al., 2021). There is also ongoing debate around the advantages and limitations of second

versus third-generation approaches (Coid et al., 2009; Nicholls et al., 2016). Metanalytic

studies have found that actuarial measures and structured professional judgment tools per-

form similarly (Yang et al., 2010). More recently, fourth-generation risk assessment has

emerged. This approach emphasizes the link between assessment and case management

across time from intake through closure (Andrews et al., 2006). It also acknowledges the

role of the assessed individual’s personal strengths and considers factors that play a role in

maximizing an individual’s response to treatment (Bonta & Andrews, 2017). In line with

fourth-generation approaches, the incorporation of strength-based factors that may proac-

tively reduce risk allows for a fairer, balanced, and comprehensive evaluation of individuals

(Rogers, 2000). The need remains for researchers to critically examine and evaluate ele-

ments of fourth-generation approaches, such as the inclusion of protective factors within

risk assessment. In particular, knowledge on protective factors that are theoretically dynamic

in nature may have clinical utility for both assessment and treatment.

Protective Factors

Strength-based variables that buffer individuals from problems in the face of risk and

promote positive outcomes are called protective factors (Brumley & Jaffee, 2016). While

direct protective factors (also referred to as promotive factors) are characteristics of indi-

viduals or their environments that are associated with a decreased likelihood of problem

behaviors, buffering protective factors function to moderate the impact or influence of risk

factors (Durrant, 2017). For example, in a study that used the LS/CMI (Olver et al., 2014)

to examine the interface between risk and protection in the prediction of recidivism

among convicted males, some protective factors (e.g., relationships with prosocial peo-

ple) had direct promotive effects—that is, a direct inverse effect on the likelihood of

recidivism, irrespective of risk factors—whereas others (e.g., negative attitudes toward

crime) had buffering effects—that is, an indirect effect on the likelihood of recidivism by