The National Trajectory Project of Individuals Found Not Criminally Responsible on Account of Mental Disorder in Canada. Part 4: Criminal Recidivism
Bibliographic record
Abstract
OBJECTIVE: To examine criminal recidivism rates of a large sample of people found not criminally responsible on account of mental disorder (NCRMD) in Canada's 3 most populous provinces, British Columbia, Ontario, and Quebec. Public concern about the dangerousness of people found NCRMD has been fed by media attention on high-profile cases. However, little research is available on the rate of reoffending among people found NCRMD across Canadian provinces. METHOD: Using data from the National Trajectory Project, this study examined 1800 men and women in British Columbia (n = 222), Ontario (n = 484), and Quebec (n = 1094) who were found NCRMD between May 2000 and April 2005 and followed until December 2008. RESULTS: Recidivism was relatively low after 3 years (17%). There were interprovincial differences after controlling for number of prior criminal offences, diagnosis, seriousness of the index offence, and supervision by the review boards. British Columbia (10%) and Ontario (9%) were similar, whereas Quebec had almost twice the recidivism (22%). People who had committed severe violent index offences were less likely to reoffend than those who had committed less severe offences. People from the sample were less likely to reoffend when under the purview of review boards, across all 3 provinces. CONCLUSION: The results of this study, along with other research on processing differences, suggest systemic differences in the trajectories and outcomes of persons found NCRMD need to be better understood to guide national policies and practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".