Can child drowning be eradicated? A compelling case for continued investment in prevention
Bibliographic record
Abstract
Abstract Aim To explore temporal trends in fatal child drowning and benchmark progress across three high‐income countries to provide prevention and future investment recommendations. Methods A total population analysis of unintentional fatal drownings among 0‐ to 19‐year‐olds in Australia, Canada and New Zealand from 2005 to 2014 was undertaken. Univariate and chi‐square analyses were conducted, age‐ and sex‐specific crude rates calculated and linear trends explored. Results A total of 1454 children drowned. Rates ranged from 0.92 (Canada) to 1.35 (New Zealand) per 100 000. Linear trends of crude drowning rates show both Australia (y = −0.041) and Canada (y = −0.048) reduced, with New Zealand (y = 0.005) reporting a slight rise, driven by increased drowning among females aged 15‐19 years (+200.4%). Reductions of 48.8% in Australia, 51.1% in Canada and 30.4% in New Zealand were seen in drowning rates of 0‐ to 4‐year‐olds. First Nations children drowned in significantly higher proportions in New Zealand (X2 = 31.7; P < .001). Conclusion Continual investment in drowning prevention, particularly among 0‐ to 4‐year‐olds, is contributing to a reduction in drowning deaths; however, greater attention is needed on adolescents (particularly females) and First Nation's children. Lessons can be learned from each country's approach; however, further investment and evolution of prevention strategies will be needed to fully eradicate child drowning deaths.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".