Prioritising preventive measures for unintentional sport and recreation-related deaths in Québec, Canada, based on a 14-year review
Bibliographic record
Abstract
OBJECTIVES: This study analysed sport and recreation-related fatalities in Québec, Canada, from January 2006 to December 2019, focusing on the six activities with the highest mortality frequencies. It aimed to identify activity-specific risk factors to inform prevention priorities. METHODS: In this descriptive retrospective study, data extracted from the database of the Bureau du coroner du Québec were analysed. The characteristics and mechanisms of fatalities in all-terrain vehicles, snowmobiles, cycling, swimming, motorised navigation and non-motorised navigation activities were presented. Incidence rates were calculated using Canadian census data. RESULTS: Male fatalities predominated, ranging from 83% to 91%, in the six activities. Traumatic brain injuries or cranial traumas were reported in 55.7% of land-based activities-related deaths, particularly in 70.1% of cycling fatalities. In 44.2% of cycling-related cases, victims were not wearing a helmet, while in 44.1% of cases involving all-terrain vehicles, victims either wore a helmet improperly or did not wear one at all. Cycling deaths mainly occurred on roads (82.9%), with 63.9% involving collisions with motor vehicles. Alcohol-impaired driving was observed in 29.8% of victims involved in all-terrain vehicle and snowmobile activities combined. Natural water accounted for 67.1% of swimming fatalities. Alcohol consumption was documented in 28.8% of deaths related to water-based activities. Personal flotation devices were not worn in 61.5% of navigation-related fatalities. CONCLUSION: Activity-specific prevention priorities have been highlighted. A thorough examination of coroners' recommendations is now necessary to understand their characteristics, as this information can guide both the identification and implementation of preventive measures.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".