Gender Differences in Police Encounters Among Persons With and Without Serious Mental Illness
Bibliographic record
Abstract
OBJECTIVE: This study examined the rates, patterns, and types of police contacts among men and women with and without serious mental illness. METHODS: Data on type of contact, type and number of offenses, dispositions, and repeat offenses were extracted from an administrative database of all police encounters in a midsized Canadian city over a six-year period (N=767,365). RESULTS: Men and women with serious mental illness represented, respectively, .5% and .4% of men and women who had at least one contact with the police; however, they were involved in 3.2% and 3.0% of all interactions, respectively. Persons with mental illness were more likely than those without mental illness to be in contact with police as suspected offenders, to have a greater number of offenses, to reoffend more quickly, and to be formally charged for a suspected offense. Among persons without mental illness in contact with police, men were much more likely than women to be offenders, to have a greater number of offenses, and to reoffend more quickly. Among persons with mental illness, however, the gender gap for these measures was significantly smaller. CONCLUSIONS: More resources should be allocated to support persons with mental illness in the community because they tend to have high rates of repeated police contacts for a variety of offenses. The findings highlight the need for gender-specific intervention programs. Administrative databases can be useful tools in examining police contacts among persons with mental illness and monitoring change after policy and program implementation for those at risk of police encounters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".