Discrimination and calibration properties of Violence Risk Scale scores as a function of Indigenous Canadian heritage in a multisite forensic-correctional sample.
Bibliographic record
Abstract
= 597) persons with conviction histories for violent offenses; approximately, two thirds of whom completed risk-need-responsivity based violence reduction treatment services. Indigenous men tended to score higher on VRS static, dynamic, and total scores and to be classified as higher risk; however, there were no differences between the groups in treatment change. In the aggregate sample, VRS total scores demonstrated broadly medium to large effects in the prediction of violent and general recidivism (median AUCs = .72 [Indigenous] and .71 [non-Indigenous]) across ethnocultural groups. Conversely, VRS change scores (controlling for pretreatment score) were significantly associated with decreased violent and general recidivism for Indigenous persons (median AUC = .62) but considerably less so, with small or lower effects, for non-Indigenous persons (median AUC = .48). These results were upheld when effect sizes were aggregated across the samples through meta-analysis. Calibration analyses demonstrated that integrating risk and change information via logistic regression modeling decreased disparities between ethnoracial groups in rates of recidivism associated with VRS scores. Implications for violence risk assessment, treatment, and management using the VRS with Indigenous persons who have a history of criminal violence are discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".