The Intersection between Criminal Accusations, Victimization, and Mental Disorders: A Canadian Population-Based Study
Bibliographic record
Abstract
Objective:Understand the relationship between criminal accusations, victimization, and mental disorders at a population level using administrative data from Manitoba, Canada.Method:Residents aged 18 to 64 between April 1, 2007, and March 31, 2012 (N = 793,024) with hospital- and physician-diagnosed mental disorders were compared to those without. Overall and per-person rates of criminal accusations and reported victimization in the 2011/2012 fiscal year were examined. Relative risks were calculated, adjusting for age, sex, income, and presence of a substance use disorder. The overlap between diagnosed mental disorders, accusations, and victimization with a χ2 test of independence was studied.Results:Twenty-four percent (n = 188,693) of the population had a mental disorder over the 5-year time frame. Four to fifteen percent of those with a mental disorder had a criminal accusation, compared to 2.4% of the referent group. Individuals with mental disorders, especially psychotic or personality disorders, were often living in low-income, urban neighborhoods. The adjusted relative risk of accusations and victimization remained 2 to 5 times higher in those with mental disorders compared to the referent group. Criminal accusations and victimization were most prevalent among individuals with a history of attempted suicide (15.2% had an accusation and 8.1% were victims). The risk of victimization in the same year as a criminal accusation was significantly increased among those with mental disorders compared to those without (χ2 = 211.8, P < 0.001).Conclusions:Individuals with mental disorders are at elevated risk of both criminal involvement and victimization. The identification of these multiply-stigmatized individuals may lead to better intervention and support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".