Determinants of Injury and Death in Canadian Firefighters : A Case for a National Firefighter Wellness Surveillance System
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".