Convergent and predictive properties of three risk assessment instruments in a Canadian forensic mental health sample
Bibliographic record
Abstract
The use of risk assessment instruments is essential for the assessment, treatment, and management of violence risk; it is thus critical to examine their properties when implemented in novel settings with diverse forensic subpopulations. This study evaluated the convergent and predictive properties of three risk assessment instruments in a sample of 109 forensic patients found Not Criminally Responsible on Account of Mental Disorder (NCR). A retrospective longitudinal cohort design was employed to examine the Historical Clinical Risk Management-20 Version 3 (HCR-20V3), Revised Violence Risk Appraisal Guide (VRAG-R), and Level of Service/Case Management Inventory (LS/CMI) rated from archived hospital records. LS/CMI risk scores and risk bands predicted general (area under the curve [AUC] = .70–.73) and violent (AUC = .76–.91) recidivism with moderate to large effects and performed similarly to the VRAG-R and HCR-20V3. Calibration analyses demonstrated that LS/CMI scores overpredicted the risk of general recidivism in Moderate to Very High risk bands. Results supported the convergent validity and discrimination properties of study measures; however, mixed evidence was found for the calibration properties of the LS/CMI. The potential utility of risk instruments in the appraisal and management of offending behavior among forensic mental health patients is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".