The Canadian Criminal Code Provisions for Mentally Disordered Offenders: A Survey of Experiences, Attitudes, and Knowledge
Bibliographic record
Abstract
OBJECTIVE: To systematically survey Alberta psychiatrists and lawyers regarding their knowledge of, attitudes toward, and experiences with the Criminal Code provisions regarding mentally disordered offenders to better understand the lack of impact in practice patterns. METHOD: A survey design was used, and 2 questionnaires, 1 for lawyers and 1 for psychiatrists, were developed and mailed out. RESULTS: Out of 245 surveys sent to psychiatrists, 141 were returned, giving a response rate of 57%. The number of lawyers practising criminal law could not be determined, and 5273 surveys were sent to all lawyers on the Law Society of Alberta mailing list. Of these, 564 were returned, giving an overall response rate of 11%. The response rate for lawyers practising criminal law is unknown. Overall, lawyers were younger than psychiatrists. Most of the respondents in both groups were men. Overall, attitudes toward offenders with mental illness were very similar among lawyers and psychiatrists. Compared with lawyers, psychiatrists had significantly more correct responses to the items assessing knowledge. With a highest possible knowledge score of 27, the average score was 16 (SD 5.7) for psychiatrists and 13 (SD 7.23) for lawyers. CONCLUSIONS: The lack of familiarity with many of the key provisions among psychiatrists and lawyers is worrisome and suggests the need for educational materials to improve knowledge of the Criminal Code provisions governing mentally disordered offenders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".