Static and Dynamic Assessment of Violence Risk Among Discharged Forensic Patients

Hogan, Neil R.; Olver, Mark E.

doi:10.1177/0093854819846526

Cited by 31 publications

(42 citation statements)

References 39 publications

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“…That said, a meta-analytic review contained in an unpublished doctoral dissertation (Chevalier, 2017) found that SPJ summary risk ratings demonstrated predictive validity for recidivism. Additional studies that were not included in Chevalier’s review have also found support for SPJ summary risk ratings, both in terms of overall risk/case prioritization (e.g., Hogan & Olver, 2019; Vargen et al, 2020) and in terms of imminent risk of institutional outcomes (e.g., Hogan & Olver, 2016, 2018).…”

Section: The Evolution Of the Clinical Versus Actuarial Debatementioning

confidence: 99%

An Examination of the Professional Override of the Level of Service Inventory–Ontario Revision

Orton

2020

Criminal Justice and Behavior

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This study examined the nature and impacts of the professional override on the Level of Service Inventory–Ontario Revision (LSI-OR), using a large archival database of 40,539 individuals’ information. Research questions focused on the predictive validity of various LSI-OR risk metrics, including total risk/need scores, initial risk categories, and adjusted risk categories, for various types of recidivism; how professional overrides were used; whether they were used more with some groups than others; and whether their impacts varied depending on recidivism type. Overrides were applied in 15.4% of cases, most often (94.1%) to increase risk levels. Override use varied based on gender, race, and the nature of index offenses. Based on receiver operating characteristic analyses, the results generally indicated that adjusted risk levels (incorporating professional overrides) demonstrated inferior predictive validity relative to unadjusted metrics. The results suggest a need for increased caution and consistency in the application of professional overrides.

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Section: The Evolution Of the Clinical Versus Actuarial Debatementioning

confidence: 99%

An Examination of the Professional Override of the Level of Service Inventory–Ontario Revision

Orton

2020

Criminal Justice and Behavior

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“…For evaluating change during institutional treatment, it is common (and often appropriate, e.g., Hogan & Olver, 2019) to enter both pretreatment and posttreatment scores as predictors in some form of regression analysis (e.g., logistic, Cox). Testing the incremental effect of the posttreatment score over the pretreatment score is equivalent to simultaneously entering the pretreatment score with the pre-post difference score (Laird & Weems, 2011 demonstrate the mathematical equivalence).…”

Section: Statistical Analysis Of Dynamic Prediction Datamentioning

confidence: 99%

Does Reassessment Improve Prediction? A Prospective Study of the Sexual Offender Treatment Intervention and Progress Scale (SOTIPS)

Hanson

Newstrom

Brouillette‐Alarie

et al. 2020

Int J Offender Ther Comp Criminol

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This prospective study examined the predictive validity of the Sex Offender Treatment Intervention and Progress Scale (SOTIPS; McGrath et al., 2012), a sexual recidivism risk/need tool designed to identify dynamic (changeable) risk factors relevant to supervision and treatment. The SOTIPS risk tool was scored by probation officers at two sites ( n = 565) for three time points: near the start of community supervision, at 6 months, and then at 12 months. Given that conventions for analyzing dynamic prediction studies have yet to be established, one of the goals of the current paper was to demonstrate promising statistical approaches for the analysis of longitudinal studies in corrections. In most analyses, static SOTIPS scores predicted all types of recidivism (sexual, violent, and general [any]). Dynamic SOTIPS scores, however, only improved the prediction of general recidivism, and only when the analyses with the greatest statistical power were used (Cox regression with time dependent covariates).

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“…The three treated correctional samples from the ABC Program (Coupland & Olver, 2020a; Lewis et al, 2013) and the VPP (Higgs et al, 2020) were broadly high risk-need, and would be characterized by serious offense histories of criminal violence, a large number of chronic and varied criminogenic needs, but also registering on average about two thirds to a full standard deviation of change from pre- to posttreatment on the dynamic factors from high intensity violence treatment programs. By contrast, the two forensic mental health samples (Eggert et al, 2020; Hogan & Olver, 2019) were broadly lower risk–need, as samples comprised of acutely mentally ill forensic patients with violent index offenses, but on average, sparser criminal histories and fewer criminogenic needs, which in turn, would be more likely to be linked to the ebb and flow of mental health symptoms. The Wong and Gordon (2006) subsample of prison inmates assessed at a single timepoint had the full spectrum of cases (hence large SD ), but on average were moderate to lower risk in terms of VRS score.…”

Section: Resultsmentioning

confidence: 99%

Assessing dynamic violence risk: Common language risk levels and recidivism rates for the Violence Risk Scale.

Olver¹,

Mundt²,

Hogan³

et al. 2022

Psychological Assessment

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The present study features the development of new risk categories and recidivism estimates for the Violence Risk Scale (VRS), a violence risk assessment and treatment planning tool. We employed a combined North American multisite sample (k = 6, N = 1,338) of adult mostly male offenders, many with violent criminal histories, from correctional or forensic mental health settings that had complete VRS scores from archival or field ratings and outcome data from police records (N = 1,100). There were two key objectives: (a) to identify the rates of violent recidivism associated with VRS scores and (b) to generate updated evidence-based VRS violence risk categories with external validation. To achieve the first objective, logistic regression was applied using VRS pretreatment and change scores on treated samples with a minimum 5-year follow-up (k = 5, N = 472) to model 2-, 3-, and 5-year violent and general recidivism estimates, with the resulting logistic regression algorithms retained to generate a VRS recidivism rates calculator. To achieve the second objective, the Council of State Governments’ guidelines were applied to generate five risk levels using the common language framework using percentiles, risk ratios (from Cox regression), and absolute violent and general recidivism estimates (from logistic regression). Construct validity of the five risk levels was examined through group comparisons on measures of risk, need, protection, and psychopathy obtained from the constituent samples. VRS applications to enhance risk communication, treatment planning, and violence prevention in light of the updated recidivism estimates and risk categories are discussed.

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Static and Dynamic Assessment of Violence Risk Among Discharged Forensic Patients

Cited by 31 publications

References 39 publications

An Examination of the Professional Override of the Level of Service Inventory–Ontario Revision

An Examination of the Professional Override of the Level of Service Inventory–Ontario Revision

Does Reassessment Improve Prediction? A Prospective Study of the Sexual Offender Treatment Intervention and Progress Scale (SOTIPS)

Assessing dynamic violence risk: Common language risk levels and recidivism rates for the Violence Risk Scale.

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