Not Criminally Responsible on Account of a Mental Disorder for a Homicide: Examining Gender Differences to Identify Opportunities for Early Prevention
Bibliographic record
Abstract
This study examined the characteristics and healthcare service trajectories of 98 men and 29 women found not criminally responsible on account of mental disorder (NCRMD) for a homicide in Canada using data from Criminal Review Boards and police reports, as well as health administrative databases for a subsample in Québec ( n = 51). Three quarters of the sample had no prior criminal justice involvement, and half had contact with mental health services in the year preceding the offense. Victims were usually known to the people found NCRMD for a homicide (83%). Women were more likely to have a mood disorder as primary diagnosis, and less likely to have displayed overt psychotic symptoms at the time of the offense. They were more likely to be found NCRMD following an event of intrafamilial violence, typically involving their children. Women were more likely to commit a homicide shortly after seeking mental health services, with an average of 18.6 days elapsing since service contact compared to an average of 101.3 days for men. Overall, these findings signal unsuccessful attempts by a subgroup of individuals, particularly women, suffering from severe mental illness to seek timely, appropriate services. Strategies for prevention, including early intervention and services, are discussed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".