The Relationship Of Injury Epidemiology In Canadian Wildland Firefighters During A Busy Fire Season
Bibliographic record
Abstract
Climate change has led to more frequent, intense, and longer wildfire seasons and has increased the number of injuries and fatalities sustained by Wildland Firefighters (WLFF). No studies have examined the impacts of climate change and increasingly busy fire seasons on injury epidemiology. PURPOSE: This study examined the relationship between injury types, location of the injury, assignment when injured, and participant characteristics (gender, age, and years of experience) of Canadian WLFF in a busy fire season. METHODS: We examined injury data from British Columbia Wildfire Service’s Occupational Health and Safety Incident Tracking database for 2021. Descriptive analysis and measure of central tendency were used to observe the relationships between injury types, location of injury and assignment when injured. Associations between injury types, location of the injury, assignment when injured, and participant characteristics (gender, age, and years of experience) were assessed using Pearson correlation and descriptive statistics. RESULTS: In 2021, 278 injuries were reported, with most injuries happening when assigned to the Fireline (68.0%). The predominant type of injury was sprain, strain, or repetitive strain injury (25.5%) and most injury at the knee (10.1%), back (9.7%), and ankle (7.5%). Injury type and assignment when injured were found to be moderately positively correlated, r(276) = 0.13, p = 0.027. In addition, injury type and age were moderately positively correlated r(276) = 0.12, p = 0.035. CONCLUSION: In busy fire seasons, most injuries are sprains or strains that predominantly occur on the Fireline. Wildfire organizations should focus their health and safety programs on reducing sprains and strains at the knee, back, and ankle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".