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Record W3157912041 · doi:10.1177/08982643211014770

Exploring a Hidden Epidemic: Drowning Among Adults Aged 65 Years and Older

2021· article· en· W3157912041 on OpenAlexaffabout
Tessa Clemens, Amy E. Peden, Richard C. Franklin

Bibliographic record

VenueJournal of Aging and Health · 2021
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsDemographyInjury preventionMedicinePoison controlSuicide preventionOccupational safety and healthPopulationOlder peopleGerontologyHuman factors and ergonomicsGeographyMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

Objectives: To explore trends in unintentional fatal drowning among older adults (65 years and older). Methods: Total population retrospective analysis of unintentional fatal drowning among people aged 65 years and older in Australia, Canada and New Zealand (2005–2014) was conducted. Results: 1459 older adults died. Rates ranged from 1.69 (Canada) to 2.20 (New Zealand) per 100,000. Trends in crude drowning rates were variable from year to year. A downward trend was observed in New Zealand (y = −.507ln(x) + 2.9918), with upward trends in Australia (y = .1056ln(x) + 1.5948) and Canada (y = .1489ln(x) + 1.4571). Population projections suggest high annual drowning deaths by 2050 in Australia (range: 120–190; 1.69–2.76/100,000) and Canada (range: 209–430; 1.78–3.66/100,000). Significant locations and activities associated with older adult drowning differed by country and age band. Conclusions: Drowning among older adults is a hidden epidemic claiming increasing lives as the population ages. Targeted drowning prevention strategies are urgently needed in Australia, Canada, New Zealand and other similar countries.

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 score0.110
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.116
GPT teacher head0.377
Teacher spread0.261 · 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

Citations25
Published2021
Admission routes2
Has abstractyes

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