Strengths Matter: Evidence From Five Separate Cohorts of Justice-Involved Youth and Adults Across North America

• Author: Kayla A. Wanamaker,Megan Wagstaff,David Robinson,Shelley L. Brown
• Published date: 01 November 2020
• Date: 01 November 2020
• DOI: http://doi.org/10.1177/0093854820931549
• Subject Matter: Articles

CRIMINAL JUSTICE AND BEHAVIOR, 2020, Vol. 47, No. 11, November 2020, 1428 –1447.

DOI: https://doi.org/10.1177/0093854820931549

Article reuse guidelines: sagepub.com/journals-permissions

© 2020 International Association for Correctional and Forensic Psychology

1428

STRENGTHS MATTER

Evidence From Five Separate Cohorts of Justice-

Involved Youth and Adults Across North America

SHELLEY L. BROWN

Carleton University

DAVID ROBINSON

Orbis Partners

KAYLA A. WANAMAKER

MEGAN WAGSTAFF

Carleton University

This study examined the predictive validity of criminogenic needs and strengths as measured by the Youth Assessment and

Screening Instrument (YASI) and the Service Planning Instrument (SPIn) within five different samples from Canada and the

United States spanning 6,445 justice-involved youths and 46,127 adults (combined N = 52,572). Dynamic strengths consis-

tently increased predictive accuracy beyond dynamic criminogenic needs. Furthermore, in a Canadian community adult

sample (N = 20,537), strengths attenuated recidivism rates in lower- and moderate-need-level groups but had no impact

among higher-need-level groups. Relatedly, in an American, federal re-entry adult residential sample (N = 23,615), strengths

attenuated program completion success rates across all need levels, albeit the effect was slightly more pronounced in the

lower- and moderate-need groups. Thus, dynamic strengths are more than merely the absence of dynamic criminogenic needs

and should be actively considered during case management.

Keywords: criminogenic needs; dynamic risk; forensic assessment; recidivism; protective factors

Risk assessment has evolved considerably since the pioneering work of Ernest Burgess

(1928) who created the first actuarial measure to predict parole failure. Since Burgess,

the number of justice-oriented risk assessment tools has swelled to 400 worldwide with

most characterized by structured professional judgment (SPJ) or actuarial mechanical

approaches (Singh et al., 2014). The corresponding evidence base is equally impressive. To

AUTHORS’ NOTE: David Robinson is director of Assessment, Orbis Partners, a company that provides the

Youth Assessment and Screening Instrument (YASI) and the Service Planning Instrument (SPIn) as case man-

agement tools to criminal justice clients. Correspondence concerning this article should be addressed to

Shelley L. Brown, Department of Psychology, Carleton University, 1125 Colonel By Drive, Ottawa, ON,

Canada K1S 5B6; e-mail: shelley.brown@carleton.ca.

931549CJBXXX10.1177/0093854820931549Criminal Justice and BehaviorBrown et al. / STRENGTHS MATTER

research-article2020

Brown et al. / STRENGTHS MATTER 1429

date, SPJ and actuarial tools used across a range of justice populations generate area under

the curve (AUC) predictive accuracy values in the range of .63 to .78 (Campbell et al.,

2009; Hanson & Morton-Bourgon, 2009; Olver et al., 2014; Singh et al., 2011).

However, assessment has been decidedly deficit-based with little attention afforded to the

construct of strengths. Furthermore, the correctional literature has been mired in definitional

debates regarding what constitutes a “strength” coupled with the introduction of countless

strength-based terms that are often used interchangeably (e.g., strengths, assets, promotive

factors, protective factors [interactive or risk-based], buffering factors, booster factors).

There is evidence that composite strength measures are negatively correlated with criminal

justice outcomes. However, less is known about the role of individual strength domains,

whether strengths add incrementally to risk, and whether or not strengths can actually exert

a genuine buffering or protective effect among higher risk individuals (Wanamaker et al.,

2018). Consequently, this study examines the predictive utility of strengths alongside risks in

five different data sets comprised of either adult or youthful justice-involved samples from

Canada and the United States using the Youth Assessment and Screening Instrument (YASI;

Orbis Partners, 2000) and the Service Planning Instrument (SPIn, Orbis Partners, 2003).

Most contemporary risk assessment tools capture both static and dynamic risk factors/crimi-

nogenic needs. Although static and dynamic risk factors predict future criminal justice involve-

ment, static factors are immutable, whereas dynamic factors/criminogenic needs can change in

response to intervention. Notably, throughout this article, the term risk factor includes both static

and dynamic risk factors; as well, the terms dynamic risk and criminogenic need are used inter-

changeably. Correctional assessment tools have traditionally focused on measuring risk factors

(e.g., has many criminal friends, has no employment skills) that increase the probability of crim-

inal conduct rather than strength factors (e.g., has prosocial friends, demonstrates high treatment

motivation) that may potentially reduce the probability of criminal conduct. Furthermore, some

scholars have argued that strengths are merely the flip side of risks and are already adequately

captured during the risk assessment process (Baird, 2009; Harris & Rice, 2015).

However, a paradigm shift has emerged. In 2002, Ward introduced the Good Lives Model

(GLM) as a strength-based rehabilitative framework. The GLM was originally developed for

individuals convicted of sex offenses, but has since been applied to other populations, includ-

ing justice-involved individuals with mental illness (Barnao et al., 2016) as well as adoles-

cent detained females (Van Damme et al., 2017). In short, the GLM posits that criminal

behavior results when individuals lack internal and external resources to meet their needs

using prosocial means. Consequently, rehabilitation efforts should empower justice-involved

individuals with the skills, knowledge, and resources required to meet their life goals and

values without harming others. Therapeutically speaking, the GLM leads to approach set-

ting/strength-oriented goals (e.g., seek out prosocial activities) as well as risk avoidance

goals (e.g., don’t go to the bar with your friends; Ward, 2002). Similarly, the latest version of

the Risk-Need-Responsivity (RNR) model of assessment and treatment (Bonta & Andrews,

2017) has actively incorporated the role of strengths; notably, the concept of strength is

explicitly embedded within three of the 15 RNR principles: first, in conjunction with crimi-

nogenic needs: “move criminogenic needs in the direction of becoming strengths” (p. 176);

second, as specific responsivity factors: “when working with the weakly motivated build on

strengths . . .” (p. 176); and finally, as a core principle/key clinical issue, “Strength: Assess

strengths to enhance prediction and specific responsivity effects” (p. 177).