The limits of our knowledge: tracking the size and scope of police involvement with persons with mental illness
Bibliographic record
Abstract
Significant public discourse has focused recently on police–civilian interactions involving with persons with mental illness (PMI). Despite increasing public attention, and growing demands for policy change, little is actually known about the myriad of ways in which Canadian police encounter PMI in the context of routine police work. To assist policymakers in developing evidence-informed policy, this paper attempts to shed light on present difficulties associated with addressing fundamental questions, such as the prevalence of mental health related issues in police calls for service. To do this, we attempt to map the size and scope of police calls for service involving PMI, drawing on both the available scientific data and the limited knowledge to be gleaned from available police reports. Our focus is on two broad categories of police interactions with citizens: public safety concerns (wellness checks, suicide threats, missing persons, mental health apprehensions) and crime prevention and response (encountering PMI as victims–complainants and (or) as potential suspects). We also explore the challenges policy-makers face in relying on police data and the importance of overcoming weaknesses in data collection and sharing in relation to the policing of uniquely vulnerable groups. This paper concludes with some key recommendations for addressing gaps highlighted.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".