Beneath the Surface: A Retrospective Analysis of Pediatric Drowning Trends & Risk Factors in Quebec
Bibliographic record
Abstract
PURPOSE: Despite the known importance of water safety, and recent efforts to enact pool safety legislation, drowning remains a leading cause of unintentional injury death in Canada. To date, little is known about the rates of pediatric drownings in Québec, the severity of these drownings, and the trends associated with the adoption of provincial regulations of pool enclosures - legislation which has been delayed twice, and remains to be fully enacted. This study aims to assess these knowledge gaps. METHODS: Retrospective observational study of all provincial pediatric drownings from January 1, 2017 to December 31, 2021. Three databases were accessed and subsequently analysed using descriptive statistics to identify trends and risk factors in the data, categorized by drowning severity (emergency room visits, hospitalizations, deaths). RESULTS: Throughout the study period, 655 drowning events were identified (an average of 92 ER visits, 29 hospitalizations, and 10 deaths, per year). Drownings were most prevalent in pools, and among children aged 1-4 (Table 1). The highest number of drownings occurred in 2020, possibly linked to the COVID-19 pandemic. Drowning events peaked in summer months, averaging 1 per day. The presence of safety features such as enclosures, or the presence of an accompanying individual was uncommon among drowning deaths. CONCLUSION: Our results illustrate that younger children, particularly those aged 1-4, are at greatest risk of drowning events. Drowning deaths occurred most commonly in the absence of safety features, indicating an opportunity for improved drowning prevention education, and enforcement of evolving enclosure legislature to improve water safety. LEVEL OF EVIDENCE: Level 2 (prospectively collected data, retrospective analysis).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".