Does One Size Fit All?

Wilson, Holly A.; Gutierrez, Leticia

doi:10.1177/0093854813500958

Cited by 79 publications

(75 citation statements)

References 35 publications

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“…What these findings demonstrate is that there are likely variables and contextual circumstances unique to Aboriginal group membership, and not necessarily tied to risk or treatment performance per se, that partly account for the higher rates of general violent recidivism observed in this group (see Wilson & Gutierrez, 2014, for a detailed analysis of possible considerations). Thus, risk and change information on its own does not explain all individual differences in recidivism rates at least for general violence.…”

Section: Discussionmentioning

confidence: 85%

Predictive Accuracy of Violence Risk Scale–Sexual Offender Version Risk and Change Scores in Treated Canadian Aboriginal and Non-Aboriginal Sexual Offenders

Olver

Sowden²,

Kingston

et al. 2016

Sex Abuse

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The present study examined the predictive properties of Violence Risk Scale-Sexual Offender version (VRS-SO) risk and change scores among Aboriginal and non-Aboriginal sexual offenders in a combined sample of 1,063 Canadian federally incarcerated men. All men participated in sexual offender treatment programming through the Correctional Service of Canada (CSC) at sites across its five regions. The Static-99R was also examined for comparison purposes. In total, 393 of the men were identified as Aboriginal (i.e., First Nations, Métis, Circumpolar) while 670 were non-Aboriginal and primarily White. Aboriginal men scored significantly higher on the Static-99R and VRS-SO and had higher rates of sexual and violent recidivism; however, there were no significant differences between Aboriginal and non-Aboriginal groups on treatment change with both groups demonstrating close to a half-standard deviation of change pre and post treatment. VRS-SO risk and change scores significantly predicted sexual and violent recidivism over fixed 5- and 10-year follow-ups for both racial/ancestral groups. Cox regression survival analyses also demonstrated positive treatment changes to be significantly associated with reductions in sexual and violent recidivism among Aboriginal and non-Aboriginal men after controlling baseline risk. A series of follow-up Cox regression analyses demonstrated that risk and change score information accounted for much of the observed differences between Aboriginal and non-Aboriginal men in rates of sexual recidivism; however, marked group differences persisted in rates of general violent recidivism even after controlling for these covariates. The results support the predictive properties of VRS-SO risk and change scores with treated Canadian Aboriginal sexual offenders.

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Section: Discussionmentioning

confidence: 85%

Predictive Accuracy of Violence Risk Scale–Sexual Offender Version Risk and Change Scores in Treated Canadian Aboriginal and Non-Aboriginal Sexual Offenders

Olver

Sowden²,

Kingston

et al. 2016

Sex Abuse

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“…Third, the LS scales have been found to have general applicability across many forensic populations. This includes adults and youth in custody or on community supervision, male and female populations, and various ancestral/ethnic backgrounds and cultures on diverse measures of recidivism, ranging from technical violations to criminal charges and convictions (e.g., Olver et al, 2009;Smith et al, 2009;wilson & Gutierrez, 2014). Fourth, the LS scales have multiple applications in corrections.…”

Section: The Level Of Service (Ls) Family Of Risk-assessment Toolsmentioning

confidence: 99%

The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism

Ghasemi

Anvari

Atapour

et al. 2020

Criminal Justice and Behavior

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The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed.

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“…Taken together, evidence on the predictive validity of the Central Eight for ethnic minorities or culturally diverse (sub)samples is ambiguous. While some authors propose the cross-cultural transferability (Bhutta & Wormith, 2016;Olver et al, 2014;Takahashi, Mori, & Kroner, 2013;Zhang, & Liu, 2015), in some studies trends of reduced predictive power have been reported (Gutierrez, Wilson, Rugge, & Bonta, 2013;Onifade, Davidson, & Campbell, 2009;Singh, Grann, Fazel, 2011;Wilson & Gutierrez, 2014;Wormith, Hogg, & Guzzo, 2015) and yet in others a total lack of predictive validity was found (e.g., Dahle & Schmidt, 2014;Schlager & Simourd, 2007;Shepherd, et al, 2015). Furthermore, offenders with an MB from Turkey or Arab countries have rarely been studied, although they comprise a large percentage of European prison populations.…”

Section: Disparities In Predictive Validity Of the Central Eightmentioning

confidence: 99%

How Culture and Migration Affect Risk Assessment

Schmidt

Meer

Tydecks³

et al. 2018

The European Journal of Psychology Applied to Legal Context

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We systematically linked cross-cultural literature to the Central Eight risk factors as an example of risk assessment procedures. We expected offenders with a migration background (MB) from Turkey or Arab countries to score higher on criminal history, criminal attitudes, antisocial companions, and education and employment problems in comparison to German offenders without an MB. In contrast, for offenders with an MB from Turkey or Arab countries, a reduced risk for unsatisfactory relationships with their parents, alcohol, and leisure-related factors was assumed. The Central Eight risk factors were applied retrospectively for male offenders serving a sentence length of more than 12 months. German offenders without an MB (n = 214) were compared to offenders with a Turkish (n = 135) or Arab (n = 112) MB concerning risk profiles and predictive validity. Risk profiles of offenders with an MB deviated (d = 0.25-0.56) from risk profiles of German offenders without an MB. For offenders without an MB criminal history, antisocial personality, criminal attitudes, antisocial companions, and alcohol/drug problems significantly predicted (AUC = .56-.73) different recidivism events. Similar results were found for offenders with a Turkish MB (AUC = .60-.70) except for antisocial companions (AUC = .50). Results for offenders with an Arab MB were inconclusive; only alcohol/drug problems consistently showed good predictive values (AUC = .66-.68). Findings demonstrate that a culture-sensitive approach in risk assessment is inevitable and recommendations for culture-sensitive research, risk assessment, and offender treatment are discussed.

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Does One Size Fit All?

Cited by 79 publications

References 35 publications

Predictive Accuracy of Violence Risk Scale–Sexual Offender Version Risk and Change Scores in Treated Canadian Aboriginal and Non-Aboriginal Sexual Offenders

Predictive Accuracy of Violence Risk Scale–Sexual Offender Version Risk and Change Scores in Treated Canadian Aboriginal and Non-Aboriginal Sexual Offenders

The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism

How Culture and Migration Affect Risk Assessment

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