Insights from coronial recommendations for preventing natural deaths in sport and recreation in Québec, Canada
Bibliographic record
Abstract
Introduction: This descriptive retrospective study analyzed coronial recommendations for natural deaths in sport and recreation from January 2006 to December 2019 using data from the Bureau du coroner du Québec. Methods: Reports with recommendations were analyzed by sex, age group, cause of death, context, and activity. The nature of recommendations was assessed using a public health-based model. Thematic analysis was conducted following a four-phase approach in which themes developed were emphasized and further connected with existing literature. Results: Reports involving individuals aged 18-24 and reports related to ice hockey were significantly more likely to contain recommendations. Reports related to individuals ≥45 years old, or related to cycling or hunting had higher death frequencies, but relatively low recommendation rates. Most recommendations aligned with the public health-based model but specifying implementation time frames was rare (11.7%). Nearly 60% of coroner's recommendations focused on automated external defibrillator implementation, delivery and training. Discussion: Mitigation of sudden cardiac arrest risk for individuals ≥45 years old, timely treatment of life-threatening arrhythmias especially for activity practiced in remote regions and specifying implementation time frames were identified as improvement areas. The multi-faceted approach to enhancing public access defibrillation developed by the International Liaison Committee on Resuscitation in 2022 addresses recurrent themes covered by coroners and holds the potential to inform evidence-based decision making.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".