Use of Force in Canadian Policing: An Examination of Incidents Involving Persons in Crisis
Bibliographic record
Abstract
While persons in crisis (PICs) are overrepresented in police Use of Force (UoF) incidents, the contexts and underlying causes of this overrepresentation remain unclear. Using data from standardized reports collected over a two-year period from a large Canadian police agency, this study compared UoF incidents involving PICs and persons not in crisis. PICs comprised 17.1% of subjects across all UoF incidents (i.e., drawn, displayed, and applied force; n = 2,191) and 24.2% of subjects in applied UoF incidents (n = 1,146). Results indicated that situational factors – including officer, subject, and environmental characteristics – were significantly associated with whether a subject was perceived to be in crisis. Controlling for these factors, PICs had higher odds of having force applied to them, but lower odds of officers perceiving the UoF as effective, compared to persons not in crisis. However, PICs were no more likely to be injured by the UoF, nor were officers more likely to be injured. Findings show the need for enhanced de-escalation and crisis intervention training that prepares officers to manage crisis encounters more effectively, and suggest that alternative response models (i.e., co-response teams) may be better suited to respond to PICs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".