Police-Citizen Encounters That Involve Mental Health Concerns: Results of an Ontario Police Services Survey
Bibliographic record
Abstract
The present study surveyed police services in Ontario to learn about changes in volume of contacts with persons with mental illness and use of pre-arrest diversion practices between 2003 and 2007, when significant new funding was provided to community mental health services. Participants included 37 municipal services (54% of services serving 92% of provincial population) and the Ontario Provincial Police. Findings indicated a trend of increasing police encounters with persons with mental illness. Police services had a range of diversion practices in place although actual implementation was lower. Some of these practices were implemented after 2005, coinciding with the entry of the new resources, although other system activities during that period also promoted police-mental health system collaboration and pre-arrest diversion. Police service ability to report data improved over the study period, but common reporting practices are lacking. Continuing work to create a provincial standardized database of police-citizen encounter data would facilitate efforts to better understand when and how diversion practices are implemented and with what results.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".