Assessing Dynamic Violence Risk in a High-Risk Treated Sample of Violent Offenders
Bibliographic record
Abstract
The present study featured an investigation of the predictive properties of risk and change scores of two violence risk assessment and treatment planning tools-the Violence Risk Scale (VRS) and the Historical, Clinical, Risk-20, Version 2 (HCR-20)-in sample of 178 treated adult male violent offenders who attended a high-intensity violence reduction program. The cases were rated on the VRS and HCR-20 using archival information sources and followed up nearly 10 years postrelease. Associations of HCR-20 and VRS risk and change scores with postprogram institutional and community recidivism were examined. VRS and HCR-20 scores converged in conceptually meaningful ways, supporting the construct validity of the tools for violence risk. Receiver operating characteristic curve analyses demonstrated moderate- to high-predictive accuracy of VRS and HCR-20 scores for violent and general community recidivism, but weaker accuracy for postprogram institutional recidivism. Cox regression survival analyses demonstrated that positive pretreatment and posttreatment changes, as assessed via the HCR-20 and VRS, were each significantly associated with reductions in violent and general community recidivism, as well as serious institutional misconducts, after controlling for baseline pretreatment score. Implications for use of the HCR-20 and VRS for dynamic violence risk assessment and management are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".