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Record W2473961611 · doi:10.1186/s12889-016-3221-8

A population based study of drowning in Canada

2016· article· en· W2473961611 on OpenAlexafffundabout
Tessa Clemens, Hala Tamim, Michael Rotondi, Alison Macpherson

Bibliographic record

VenueBMC Public Health · 2016
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsMedicineEpidemiologyInjury preventionPoison controlCase fatality rateDemographyOccupational safety and healthBiostatisticsPopulationSuicide preventionPublic healthMortality rateEnvironmental healthSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Although water-related fatality rates have changed over time, the epidemiology of drowning in Canada has not recently been examined. In spite of the evidence supporting varying drowning death rates by age, information on how characteristics of drowning incidents differ by age group remains limited. The primary objective of this study was to examine the epidemiology of drowning in Canada. A secondary objective was to describe the characteristics of these drowning incidents as they vary by age group. METHODS: A retrospective descriptive analysis was conducted using data that were collected for incidents occurring in Canada between January 1, 2008 and December 31, 2012. The main outcome variable was a water-related fatality, in the majority of cases (94 %) the primary cause of death was drowning. Age specific frequencies, proportions and rates per 100,000 population were calculated and compared among six age groups. RESULTS: There were 2392 unintentional water-related fatalities identified in Canada between 2008 and 2012. Death rates (per 100,000) varied by age group 0-4 (1.05), 5-14 (0.57), 15-19 (1.27), 20-34 (1.70), 35-64 (1.44), 65+ (1.74). The male to female ratio was 5:1. Differences in the characteristics of drowning by age group were identified across: sex, body of water, urban versus rural location, time of year, activity type, purpose of activity, alcohol involvement, personal flotation device use, accompaniment, and whether a rescue was attempted. CONCLUSIONS: The study results suggest that there may be a need for drowning prevention strategies that are tailored to specific age groups. Rural areas in Canada may also benefit from targeted drowning prevention.

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)0.001
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 score0.074
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.078
GPT teacher head0.372
Teacher spread0.294 · 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

Citations58
Published2016
Admission routes3
Has abstractyes

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