Supervision and guardianship during drowning fatalities among Canadian youth: An 11-year review of a preventable paediatric public health crisis
Bibliographic record
Abstract
Drowning is a leading cause of injury-related deaths among youth worldwide, often linked to inadequate supervision and ineffective guardianship. While paediatric studies have examined demographics and risk factors, little is known about how these variables interact to influence rescue likelihood. This study addresses that gap using a multivariate binary logistic regression model to assess the odds of not receiving a rescue attempt, using fatality records (n = 638) from coroners and medical examiners across Canada (2006-2016). This approach captures all known accidental fatal drownings but findings cannot be generalized to include incidents in which the individual survived. The best model explained 45.1% of the variance in rescue outcomes (Nagelkerke Pseudo R² = .451) with 84.6% accuracy. Findings revealed that age, sex, alcohol use, perimortem activity, water body type and urban/rural location significantly impacted rescue attempts. However, chronic medical conditions and general bystander presence showed no significant relationship at the bivariate level. When demographics and situational factors were considered, bystander type (with adult, minors only or alone/not witnessed) became a key model contributor for predicting rescue attempt likelihood. Teenagers (15-18 years) accounted for 33.5% of drownings, followed by toddlers (2-4 years, 21.9%) and children (5-11 years, 20.5%). Compared to infants, older children and teens faced greater risks of not being rescued, and bystander presence does not equate to capable guardianship. Open water environments posed the highest risk, with ocean drownings 7.9 times more likely to result in no rescue attempt, compared to domestic settings.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".