A Contextualized Assessment of Duty-Related Bodily Harm Associated with Canadian Police Services
Bibliographic record
Abstract
Canadian police report substantially more mental health challenges than the general population, partially due to negative media coverage. Negative media coverage often focuses on critiquing police causing duty-related bodily harm (DRBH) without sufficient context. Direct comparisons of police to other professionals are difficult; however, analogous comparisons can be made to previously published data on Avoidable Harm during Hospitalization (AHH). The current study quantitatively analyzed publicly available Canadian data on DRBH involving use of force exceeding legally approved standard operating procedures or involving code of conduct violations (FELSOP) relative to total police occurrences. From 2014 to 2023, DRBH involving FELSOP proportions were 1.89 per 100 000 police occurrences and 5566.67 AHH instances per 100 000 hospitalizations. Criticisms of Canadian police interactions with the public appear inconsistent with the available data. DRBH reported without context and coupled with anti-police rhetoric likely causes harms to individual police, and undermines efforts at recruitment, retention, community engagement, and Indigenous reconciliation. Healthcare worker intentions are justifiably considered beneficent, and harms are considered unintentional by default; the same should be made true for police officers, absent a conviction. Concerted efforts are needed to reframe the Canadian police discourse, possibly informed by the supports already rightfully provided to healthcare workers.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".