Risk relevance of psychometric assessment and evaluator ratings of dynamic risk factors in high‐risk violent offenders
Bibliographic record
Abstract
Purpose Relatively little research has been conducted with high‐risk violent (non‐sexual) offenders to establish whether measures administered to evaluate change during offending behaviour programmes contain risk relevant information. The present study aims to contribute to the evidence base relevant to decisions concerning whether or not psychometric assessments indicate how the violence risk presented by an individual may be understood differently pre‐ to post‐treatment. Methods Two hundred and twenty‐seven persistently violent offenders participating in Correctional Service of Canada’s Violence Prevention Program were assessed on measures of anger, impulsivity, and dynamic items of the Violence Risk Scale (VRS; Wong & Gordon, 1999–2003; Violence Risk Scale, University of Saskatchewan, Saskatchewan, CA) prior to and after programme completion and subsequently followed up in the community for an average of 3 years. Data were examined using receiver operating characteristic and logistic regression analyses employing fixed follow‐ups. Results With pre‐treatment status controlled, change on few of the measures convincingly predicted violent or general recidivism. An exception was that changes in VRS dynamic score were associated with decreased general (but not violent) recidivism, controlling for baseline pre‐treatment risk. Conclusions The measures tested are widely used to evaluate progress in violence interventions yet the implicit assumption that they contain risk relevant information has not been empirically validated. Since reduction in dynamic risk factors translates into reduced likelihood of reoffending, but psychometric measures provide little indication of change in recidivism risk, treatment providers are advised to carefully contextualize pre‐ to post‐treatment change within a comprehensive evaluation of static and dynamic risk using a measure such as the VRS. Present results are discussed further in terms of implications for policy and clinical practice, as well as future research directions.
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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 | 0.028 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".