Alcohol use, aquatic injury, and unintentional drowning: A systematic literature review
Bibliographic record
Abstract
ISSUES: Drowning is a global public health issue, and there is a strong association between alcohol and risk of drowning. No previous systematic review known to date has identified factors associated with alcohol use and engagement in aquatic activities resulting in injury or drowning (fatal and non-fatal). APPROACH: Literature published from inception until 31 January 2017 was reviewed. Included articles were divided into three categories: (i) prevalence and/or risk factors for alcohol-related fatal and non-fatal drowning and aquatic injury, (ii) understanding alcohol use and aquatic activities, and (iii) prevention strategies. Methodological quality of studies was assessed using National Health and Medical Research Council (NHMRC) Level of Evidence and risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scales. KEY FINDINGS: In total, 74 studies were included (57 on prevalence and/or risk factors, 15 on understanding alcohol use, and two on prevention strategies). Prevalence rates for alcohol involvement in fatal and non-fatal drowning varied greatly. Males, boating, not wearing lifejackets, and swimming alone (at night, and at locations without lifeguards) were risk factors for alcohol-related drowning. No specific age groups were consistently identified as being at risk. Study quality was consistently low, and risk of bias was consistently high across studies. Only two studies evaluated prevention strategies. IMPLICATIONS: There is a need for higher quality studies and behavioural basic and applied research to better understand and change this risky behaviour. CONCLUSION: On average, 49.46% and 34.87% of fatal and non-fatal drownings, respectively, involved alcohol, with large variations among studies observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".