Exploring the burden of fatal drowning and data characteristics in three high income countries: Australia, Canada and New Zealand
Bibliographic record
Abstract
Drowning is a leading and preventable cause of death that has suffered an attention deficit. Improving drowning data in countries would assist the understanding of the full extent and circumstances of drowning, to target interventions and evaluate their effectiveness. The World Health Organization identifies data collection as a key strategy underpinning effective interventions. This study compares unintentional fatal drowning data collection, management and comparison using the databases of Australia, Canada and New Zealand. Cases of fatal unintentional drowning between 1-January-2005 and 31–December-2014 were extracted. Cases were combined into a single dataset and univariate and chi square analysis ( p < 0.01) were undertaken. Location and activity variables were mapped and combined. Variables consistently collected across the three countries were compared to the ILCOR Drowning Data Guideline. The authors also recommend variables for a minimum core dataset. Of 55 total variables, 19 were consistent and 13 could be compared across the three databases. When mapped against the ILCOR Drowning Data Guideline, six variables were consistently collected by all countries, with five compared within this study. The authors recommend a minimum core dataset of 11 variables including age, sex, location, activity, date of incident, and alcohol and drug involvement). There were 8176 drowning deaths (Australia 34.1%, Canada 55.9%, New Zealand 9.9%). All countries achieved reductions in crude drowning rates (Australia − 10.2%, Canada − 20.4%, New Zealand − 24.7%). Location and activity prior to drowning differed significantly across the three countries. Beaches (X 2 = 1151.0; p < 0.001) and ocean/harbour locations (X 2 = 300.5;p < 0.001) were common in Australia and New Zealand, while lakes/ponds (X 2 = 826.5; p < 0.001) and bathtubs (X 2 = 27.7;p < 0.001) were common drowning locations in Canada. Boating prior to drowning was common in Canada (X 2 = 66.3;p < 0.001). The comparison of data across the three countries was complex. Work was required to merge categories within the 20% of variables collected that were comparable, thus reducing the fidelity of data available. Data sources, collection and coding varied by country, with the widest diversity seen in location and activity variables. This study highlights the need for universally agreed and consistently applied categories and definitions to allow for global comparisons and proposes a core minimum dataset.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".