The Use of Risk and Need Factors in Forensic Mental Health Decision‐Making and the Role of Gender and Index Offense Severity
Bibliographic record
Abstract
Canadian legislation makes Review Boards (RBs) responsible for rendering dispositions for individuals found Not Criminally Responsible on account of Mental Disorder (NCRMD) after considering public safety, the mental condition of the accused, and his/her potential for community reintegration. We reviewed 6,743 RB hearings for 1,794 individuals found NCRMD in the three largest Canadian provinces to investigate whether items from two empirically supported risk assessment measures, the Historical Clinical Risk Management-20 and the Violence Risk Appraisal Guide, were considered. Less than half the items were included in expert reports or in RBs' reasons for dispositions, and consideration of these items differed according to gender and index offense severity of the accused. These items included evidence-based risk factors and/or legally specified criteria: mental health, treatment, and criminal history. These results illustrate the gap between research on risk factors and the integration of this evidence into practice. In particular, we recommend the implementation of structured measures to reduce the potential for clinicians to be unduly influenced by gender and offense severity.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".