Mitigating unintentional injury deaths in sport and recreation: insights from 14 years of coroner recommendations in Québec, Canada
Bibliographic record
Abstract
Background Unintentional injury deaths in sport and recreation represent a significant public health concern. This study analysed coronial recommendations related to such deaths, focusing on case specifics and recurring themes from January 2006 to December 2019. Methods This mixed-methods study used data from the Bureau du coroner du Québec. Reports with recommendations were analysed by sex, age group, context, mechanism and activity. A four-phase thematic analysis was conducted to emphasise the developed themes and connect them with the existing literature. Results Of 1937 coronial reports reviewed, 13.3% (n=258) contained at least one recommendation, totalling 609 recommendations (31 per 100 activity-related deaths). Reports were more likely to contain at least one recommendation for women (20.3%, p=0.0004), paediatric populations (≤5 years: 30.3%, p<0.0001; 6–11 years: 29.3%, p=0.0003; 12–17 years: 27.6%, p<0.0001), and organised events (55.0%, p<0.0001), despite most deaths occurring among men, adults and during unstructured events. All-terrain vehicle and snowmobile activities showed significantly lower rates of reports with recommendations (8.1%, p=0.0008 and 8.6%, p=0.0044, respectively). Most frequently addressed themes were Development, inspection and modification of bicycle infrastructure for cycling and Lake and river safety measures for swimming. Conflict with other types of users was the top theme for land motorsports, while Personal flotation device use was the most common for navigation activities. Conclusions Patterns from reports with recommendations will be shared with the Bureau du Coroner du Québec to improve coronial practices. Integrating recurrent themes and recommendations with activity-specific risk factors will help identify critical patterns and inform preventive measures holistically.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".