Lay rescuer intervention in fatal drownings in Canada, 2010–2019: a population-based cross-sectional analysis
Bibliographic record
Abstract
INTRODUCTION: Despite evidence that immediate rescue and initiation of resuscitation plays a vital role in determining the outcome of a drowning person, research on lay rescuer interventions remains limited. We explored lay rescuer interventions in fatal drowning incidents in Canada and described the characteristics of lay rescuers who had a fatal outcome while attempting to rescue another person. METHODS: We reviewed all unintentional drowning deaths that occurred in Canada (2010-2019) identified by the Drowning Prevention Research Centre. We determined the adjusted OR of lay rescuer intervention for different drowning incident characteristics using multivariable logistic regression. Using descriptive statistics, we described incidents where a lay rescuer fatally drowned while attempting to perform a rescue. RESULTS: During the study period, 4535 people died as the result of unintentional drowning incidents in Canada. There was an attempted rescue in 2480 cases (54.7%) and most were by lay rescuers (n=1846, 74.4%). Lay rescuers frequently used a high-risk, contact rescue technique (n=895, 48.5%). Lay rescuers were more likely to respond when the drowning person was a child, was female, the drowning occurred in a pool, only one person was drowning and when there was no ice present in the body of water. 74 lay rescuers fatally drowned while attempting to save another person. CONCLUSIONS: Lay rescuers frequently intervened on drowning incidents with a high-risk, contact rescue. Since characteristics differed between drowning incidents where a lay rescuer response occurred (vs not), further investigation into rescuer motivation may inform tailored interventions to reduce rescuer injury.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".