Costs for Long-Term Health Care After a Police Shooting in Ontario, Canada
Bibliographic record
Abstract
Importance: Police shootings can cause serious acute injury, and knowledge of subsequent health outcomes may inform interventions to improve care. Objective: To analyze long-term health care costs among survivors of police shootings compared with those surviving nonfirearm police enforcement injuries using a retrospective design. Design, Setting, and Participants: This population-based cohort analysis identified adults (age ≥16 years) who were injured by police and required emergency medical care between April 1, 2002, and March 31, 2022, in Ontario, Canada. Exposure: Police shootings compared with other mechanisms of injury involving police. Main Outcomes and Measures: Long-term health care costs determined using a validated costing algorithm. Secondary outcomes included short-term mortality, acute care treatments, and rates of subsequent disability. Results: Over the study, 13 545 adults were injured from police enforcement (mean [SD] age, 35 [12] years; 11 637 males [86%]). A total of 13 520 individuals survived acute injury, and 8755 had long-term financial data available (88 surviving firearm injury, 8667 surviving nonfirearm injury). Patients surviving firearm injury had 3 times greater health care costs per year (CAD$16 223 vs CAD$5412; mean increase, CAD$9967; 95% CI, 6697-13 237; US $11 982 vs US $3997; mean increase, US $7361; 95% CI, 4946-9776; P < .001). Greater costs after a firearm injury were not explained by baseline costs and primarily reflected increased psychiatric care. Other characteristics associated with increased long-term health care costs included prior mental illness and a substance use diagnosis. Conclusions and Relevance: In this longitudinal cohort study of long-term health care costs, patients surviving a police shooting had substantial health care costs compared with those injured from other forms of police enforcement. Costs primarily reflected psychiatric care and suggest the need to prioritize early recognition and prevention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".