Shedding Light on the Dark Figure of Police Mental Health Calls for Service
Bibliographic record
Abstract
Abstract Recent discussions around police reform have acquired a significant degree of traction. Within these discussions have been calls to remove the police as primary responders to calls involving persons with perceived mental illness (PwPMI). While previous research shows that ∼1% of all calls for service involve PwPMI, limitations around police data recording practices likely mask the true proportion of PwPMI within and across calls for service. Accordingly, following manual review and text search of qualitative data appended to all calls for service made to a Canadian police service in 2019, we sought to identify the true proportion of calls for police service that involve PwPMI and predict the extent to which PwPMI are involved within and across different call classifications. Our findings reveal that while the ‘Mental Health’ call classification only comprised 0.9% (n = 397) of calls for service, PwPMI were in fact involved in 10.8% (n = 4,646) of calls. Furthermore, logistic regression models reveal that PwPMI are more likely to be involved in certain call classifications relative to others. Implications for police practice and reform are discussed.
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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 | 0.032 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".