A scoping review of female drowning: an underexplored issue in five high-income countries
Bibliographic record
Abstract
BACKGROUND: Drowning is a significant public health issue, with females accounting for one third of global drowning deaths. The rate of female drowning has not decreased within high-income countries and presentations to hospital have increased. This scoping review aimed to explore adult female unintentional drowning, including risk factors, clinical treatment and outcomes of females hospitalised for drowning. METHODS: A systematic search of the literature following the PRISMA-ScR framework was undertaken. The databases OVID MEDLINE, Embase, CINAHL, OVID Emcare, Web of Science, Informit and Scopus were accessed. Study locations of focus were Australia, Canada, New Zealand, the United Kingdom, and the United States. Studies from January 2003 to April 2019 were included. The quality of evidence of included studies was assessed using GRADE guidelines. RESULTS: The final search results included 14 studies from Australia (n = 4), Canada (n = 1), New Zealand (n = 1), United States (n = 6), United Kingdom (n = 1), and one study reporting data from both Australia and United States. Nine studies reported risk factors for female drowning including age, with the proportion of female drowning incidence increasing with age. Although females are now engaging in risk-taking behaviours associated with drowning that are similar to males, such as consuming alcohol and swimming in unsafe locations, their exposure to risky situations and ways they assess risk, differ. Females are more likely to drown from accidental entry into water, such as in a vehicle during a flood or fall into water. This review found no evidence on the clinical treatment provided to females in hospital after a drowning incident, and only a small number of studies reported the clinical outcomes of females, with inconsistent results (some studies reported better and some no difference in clinical outcomes among females). CONCLUSION: Adult females are a group vulnerable to drowning, that have lacked attention. There was no single study found which focused solely on female drowning. There is a need for further research to explore female risk factors, the clinical treatment and outcomes of females hospitalised for drowning. This will not only save the lives of females, but also contribute to an overall reduction in drowning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| 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) | 0.001 | 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".