Drowning deaths of zero‐ to five‐year‐old children in Victorian dams, 1989–2001
Bibliographic record
Abstract
OBJECTIVE: To examine drowning deaths of young children in Victorian dams to identify common contributing factors in order to develop strategies for future prevention. DESIGN: Case records of children aged zero to five years from the State Coroner's Office Victoria were reviewed for the 13-year period 1989-2001. Cases where the child drowned in a dam were extracted for analysis. RESULTS: During the 13-year period there were 27 deaths; 11 occurred on farms, five on hobby farms and 11 on properties where it was not specified whether the property was a farm. Almost three quarters of the children were male and the majority were aged between one year and three years. Half of the incidents occurred on the weekend and nearly half occurred during the summer months. Five major factors were common among incidents: stage of the child's development; absence of carer supervision; child playing outside the house; dam within 300 metres of where the child was playing; and lack of effective barriers between the dam and the child. CONCLUSION: The coronial information examined identified patterns of behaviour by both carers and young children that contributed to these deaths. The results support the implementation of strategies such as the promotion of child safe play areas and targeted public awareness campaigns for rural and regional aquatic environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".