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Language and unintentional injury mortality in Quebec, Canada

2015· article· en· W2106447099 on OpenAlexaffabout
Stephanie Burrows, Nathalie Auger, Ernest Lo

Bibliographic record

VenueInjury Prevention · 2015
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsInjury preventionPoison controlOccupational safety and healthForensic engineeringSuicide preventionHuman factors and ergonomicsMedical emergencyEngineeringEnvironmental healthMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Language-based differences in unintentional injury are poorly understood, despite the importance of language as a determinant of health. This study assessed inequalities in unintentional injury mortality between Francophones and Anglophones of Quebec, Canada. We calculated age-standardised rates of death by period, region, residential deprivation and cause of injury, and estimated rate ratios for Francophones relative to Anglophones. Francophones had higher unintentional injury mortality rates than Anglophones. Inequalities decreased over time for men, but rates remained 50% higher for Francophones at the end of the study period. Rates were stable for women, but were 30% higher for Francophones compared with Anglophones. Inequalities were larger at age 15-44 years, in urban areas, and for MVCs. Better understanding of risk factors for MVCs may benefit injury prevention in Quebec. Language-based differences in injury mortality warrant attention in other multilingual populations, especially across different demographic, temporal, regional and cause-of-injury groups.

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)0.000
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 score0.330
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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