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Record W3187539729 · doi:10.14288/1.0397309

Determinants of Injury and Death in Canadian Firefighters : A Case for a National Firefighter Wellness Surveillance System

2021· article· en· W3187539729 on OpenAlexaffabout
Rachel Ramsden, Jennifer Smith, Kate Turcotte, Len Garis, Kenneth Kunz, Paul Maxim, Larry Thomas, Ian Pike

Bibliographic record

VenuecIRcle (University of British Columbia) · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOccupational safety and healthMedical emergencyPoison controlEnvironmental healthMedicineComputer securityComputer science

Abstract

fetched live from OpenAlex

Occupational injury is a significant concern facing the Canadian workforce resulting in lost work time and income, medical expenses, compensation costs, and long-term health problems or disability. Previous research has shown health risks associated with employment as a firefighter, and exposure to a variety of injury-related hazards in the course of their occupation. Extreme temperatures, toxic substances, strenuous physical labour, violence and other traumatic events are potential risks that firefighters may experience when responding to emergencysituations. The purpose of this report is to describe injury, disease and death among Canadian firefighters. The report aims to help the reader to understand the causes of injury, disease and death among Canadian firefighters through an extensive review of previous research, as well as a detailed analysis of injury claims data. Claims data from the Association of Workers’ Compensation Boards of Canada (AWCBC) and WorkSafeBC for the years 2006 to 2015 for professional and volunteer firefighters are presented to define priority issues for targeted health promotion and injury prevention interventions.

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 score0.195
Threshold uncertainty score0.413

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 head0.296
Teacher spread0.273 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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