Characteristics associated with criminal responsibility assessment outcomes among women in Central Canada
Bibliographic record
Abstract
The number of women involved with forensic mental health systems internationally is rising, however, limited research has explored the characteristics of those assessed for criminal responsibility. We investigated the demographic, psychiatric, and criminological characteristics of women recommended as eligible or ineligible for the defence of Not Criminally Responsible (NCR) on account of mental disorder following a criminal responsibility assessment in Central Canada. Data were collected through retrospective chart reviews of court-ordered criminal responsibility assessments for 109 women referred for evaluations between 2003 and 2019. Accused were an average age of 34.55 years, predominately identified as Indigenous (37.7%) or Caucasian (20.8%), and had often been charged with assault (47.7%). Women identified in the reports as NCR-eligible were significantly more likely to be employed, experience delusions during the index offence, and have expert reports linking their mental health symptoms to NCR legal criteria. They were also significantly less likely to have a personality disorder, substance-related diagnosis, or have used substances during the index offence. Delusions during the index offence significantly predicted assessment recommendations when controlling for age at assessment order, current substance-related diagnosis, and whether the expert report linked mental health symptoms to NCR legal criteria. Findings indicate the key factors considered by forensic mental health professionals when conducting criminal responsibility assessments with women. Meaningful differences exist between women identified as NCR-eligible and ineligible, with findings illustrating who may be more likely to receive services within the Canadian forensic mental health system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".