Discrimination and Calibration Properties of the Violence Risk Appraisal Guide–Revised in a Not Criminally Responsible Provincial Population
Bibliographic record
Abstract
This study examined the discrimination and calibration properties of the Violence Risk Appraisal Guide–Revised (VRAG-R) within a large subset of the population of 574 individuals who had been found Not Criminally Responsible on Account of Mental Disorder (NCRMD) in Alberta. The VRAG-R was scored on all individuals identified via The Alberta NCR Project database from every file that contained sufficient relevant information and recidivism data were obtained via official criminal records. The VRAG-R demonstrated strong discrimination properties for general and violent recidivism over 5-year, 10-year, and global follow-ups. Calibration analyses, however, indicated that the VRAG-R substantially over estimated violence risk and that there was poor agreement between expected and observed recidivism rates for this population. When examined in the male subsample, these issues remained but to a lesser degree; examination of VRAG-R discrimination and calibration for females was not possible due to a lack of recidivists. Results indicated strong discrimination but poor calibration properties of the VRAG-R in this NCRMD population. Overall, the results support the use of the VRAG-R within a population of persons found NCRMD when employed in tandem with other measures as part of a comprehensive psychological risk assessment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".