Gender Differences in Police Encounters Among Persons With and Without Serious Mental Illness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it