Canadian water‐related fatalities: Demographic, situational, and environmental risk factors
Bibliographic record
Abstract
Unintentional water-related deaths are an ongoing global problem, despite being named by the United Nations as one of the leading preventable causes of death. To address the need for enhanced analysis of drowning risk factors, including demographic and situational conditions that may influence death outcomes, this research involved a three-phase multimodal risk assessment by utilizing unintentional water-related death records (n = 5105) from all Canadian provinces and territories from Jan 2006 to Dec 2016, census boundaries, hydrological shape files, and spectrum management data on all cellular towers. These were all accidental fatalities, where decedent demographics, situational case factors, and environmental conditions are known, including whether a rescue attempt occurred. It is believed that those who had a rescue attempt were better situated to have favorable outcomes but were unable to survive. Binary logistic regression shows that Indigenous persons experience 1.9× greater risk of not being rescued. Alcohol involvement doubled the risk of not being rescued. Differences in rescue likelihood by age were observed for youth, where there are greater expectations of guardianship. Results highlight the risk of being alone, and minors were found to be ineffectual interveners. Perimortem activities also show how many of these deaths involved unintentional water entry. Seasonal and temporal analyses reveal risky peak times during evenings and weekends and demonstrate the importance of per capita calculations in comparing risk between differently sized populations. Last, a novel approach was devised to stratify risk based on the probability of accessing cellular reception for emergency medical services at drowning locations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".