Retrospective Review of the Criminal Code Review Board in Quebec for the Year 2023
Bibliographic record
Abstract
Background/Objectives: The Commission d’examen des troubles mentaux (CETM), under Quebec’s Tribunal Administratif du Québec, reviews individuals found not criminally responsible on account of mental disorder (NCRMD). These hearings seek to balance public safety with reintegration, guided largely by treatment team recommendations. Despite the CETM’s central role in forensic psychiatry, limited empirical data exist on how its decisions align with clinical advice and which dynamic risk factors influence outcomes. This study aimed to (1) profile the CETM’s 2023 caseload, (2) evaluate concordance between CETM dispositions and treatment team recommendations, and (3) examine clinical, social, and legal factors associated with decision-making. Methods: We conducted a retrospective review of 1721 judgments issued by the CETM in 2023, retrieved from the publicly accessible Société Québécoise d’information juridique (SOQUIJ) database. Eligible cases included annual NCRMD review hearings, excluding trial fitness assessments and repeated hearings within the same year. A structured coding grid documented sociodemographic, administrative, legal, and clinical information, with emphasis on dynamic risk factors such as treatment adherence, substance use, and recent aggression. Descriptive analyses summarized population characteristics and concordance between clinical recommendations and CETM decisions. Results: The cohort was predominantly male (85%) with a mean age of 41 years. Psychotic disorders were the most frequent primary diagnoses (76%), frequently accompanied by substance use and antisocial traits. Most patients (79.6%) had prior psychiatric hospitalizations, while 25.5% had prior incarcerations. Nearly half displayed recent aggression or non-compliance. Treatment teams most often recommended conditional discharge (55%), followed by detention with conditions (21%) and unconditional release (19%). CETM decisions aligned with recommendations in 83.6% of cases; when divergent, rulings were more restrictive (8.6%) than permissive (4.6%). Conclusions: This study provides the first large-scale profile of Quebec’s CETM. High concordance with clinical teams was observed, but restrictive decisions were more frequent in cases of disagreement. The findings underscore the importance of incorporating standardized risk assessment tools to enhance transparency, consistency, and balance in forensic decision-making.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".