Informing Community Coalitions Through the Use of Existing Databases to Inform Drowning Prevention Action
Bibliographic record
Abstract
Introduction: Drowning is a leading cause of unintentional injury-related deaths, claiming approximately 300,000 lives annually.1 Beyond the immediate loss of life, its impact extends to families, communities, and healthcare systems. Drowning is defined as the process of experiencing respiratory impairment from submersion or immersion in liquid, with outcomes classified as fatal or non-fatal.2 In non-fatal drowning, respiratory impairment is stopped before death.2,3 Recent efforts aim to improve drowning incident reporting for a clearer understanding of its burden and effective interventions. Despite its global impact, drowning remains a neglected public health issue with limited research attention. Objectives: This study aims to analyze drowning mortality data from 2011 to 2019 within Elgin, Middlesex, London, and Oxford counties to inform the Elgin County Drowning Prevention Coalition’s (ECDPC) community-based initiatives The study seeks to identify priority recommendations for community action, policy development, and future research to support local drowning prevention efforts. Methods: A retrospective analysis was conducted on water-related fatalities that occurred in the four counties between January 1, 2011, and December 31, 2019. Key variables examined included sex, age group, body of water, time of year, activity type, purpose of activity, accompaniment, and whether a rescue was attempted. These variables provided a comprehensive overview of drowning incidents and patterns. Results: A total of 40 water-related fatalities in the region were identified during the study period. Regional rates compared to provincial rates were higher in bathing and pool fatalities, with an increase in ages 15-64. Conversely, boating fatalities and drownings among those aged 65+ showed a decline. Conclusion: The novel use of regional and community-specific data are essential for developing evidence-based drowning prevention strategies. Findings suggest a need for tailored prevention efforts and improved non-fatal drowning data collection to assess the full burden of drowning in the community.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".