Drowning prevention: A global health promotion imperative, now more than ever
Bibliographic record
Abstract
Drowning prevention: A global health promotion imperative, now more than everHealth promotion and injury prevention have in common the mobilisation of communities and coalitions to develop and implement a range of evidence-informed strategies to prevent disease, protect health and ultimately, reduce mortality and morbidity.1 Over the past three decades, health promotion and injury prevention have been brought together in the Health Promotion Journal of Australia across articles on many injury prevention areas, including drowning prevention.This has included the publication of two special issues almost 20 years apart-in 1991 and again in 2018.Since that first special issue on injury prevention so early in the HPJA's inception, the journal has published a growing number of papers on drowning prevention, from those focusing on exploring
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".