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Drowning deaths of zero‐ to five‐year‐old children in Victorian dams, 1989–2001

2005· article· en· W2093536730 on OpenAlexaboutno aff
Lyndal Bugeja, Richard C. Franklin

Bibliographic record

VenueAustralian Journal of Rural Health · 2005
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsCoronerHobbyMedicineOccupational safety and healthSuicide preventionInjury preventionPromotion (chess)Quarter (Canadian coin)Poison controlDemographyGeographyPediatricsSocioeconomicsEnvironmental healthPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.381
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2005
Admission routes1
Has abstractyes

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