Factors Considered for the Assessment of Risk in Administrative Review Boards of Canada: A Scoping Review
Bibliographic record
Abstract
Background: This scoping review examines the risk factors considered in assessing the dangerousness of individuals found Not Criminally Responsible on Account of Mental Disorder (NCRMD) in Canadian administrative courts. This review aims to identify the factors used by mental health review boards during annual case reviews to guide decisions on detention or release. Methods: Using a scoping review approach following PRISMA guidelines, this study analyzed research across multiple databases to identify relevant studies focusing on risk assessment for NCRMD cases. Results: The findings indicate that five primary categories of risk factors—historical, clinical, behavioral, legal, and miscellaneous—are influential in the decision-making process. Historical factors, such as past violence and early psychiatric contacts, are critical in predicting future risk. Clinical factors, including psychiatric diagnosis and treatment adherence, are key to evaluating current and potential future risks. This study reveals variability in the application of standardized risk assessment tools, highlighting a need for more consistent practices across Canadian jurisdictions. Conclusion: This review concludes that, while a multifaceted approach to risk assessment is essential for balancing public safety with individual rehabilitation, further research is needed to refine these processes and establish more uniform standards for managing NCRMD cases in forensic psychiatry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.185 | 0.461 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.057 | 0.055 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".