The Impact Of Injury Characteristics On Injury Severity Among Canadian Wildland Firefighters During An Intense Fire Season
Bibliographic record
Abstract
INTRODUCTION: Climate change has led to more frequent and prolonged wildfire seasons, along with development in the wildland-urban interface, which has increased the risks to Wildland Firefighters (WLFF). These higher stakes are increasing the severity of injuries, leading to more fatalities, and there is a gap in understanding how injury characteristics relate to injury severity. PURPOSE: This study examined the association between injury characteristics and injury severity among Canadian WLFFs during the 2021 fire season. METHODS: Injury data from 278 reports in the 2021 British Columbian Wildfire Service’s (BCWS) Occupational Health and Safety Incident Tracking Database was used in a multiple linear regression analysis. The analysis evaluated the influence of ten predictors (age, sex, years of experience, position, assignment, activity, injury location, type of injury, cause of injury, and time loss) on the severity of injury (assessed by the days lost due to injury). RESULTS: Of the 278 injury reports, the highest frequency occurred in the lumbar region (n = 35), followed by the hand or wrist (n = 32), the ankle or foot (n = 29), and the knee (n = 28). The overall regression analysis was statistically significant (R2 = 0.2066, F(10, 263) = 6.848, p < 0.001). Of the ten predictors, the location of injury (p = 0.0312) and the occurrence of time loss (p < 0.001) were significant determinants of the severity of injury. The other factors, like age, gender, and years of experience, did not significantly affect the severity of injury. CONCLUSION: This study demonstrates that the location of the injury is an essential factor in predicting the severity of injury among WLFF, as denoted by time off work. Intervention strategies should focus on core stability while improving hip and thoracic mobility to reduce injury potential for the lower back (lumbar region). Wildfire organizations should also focus worker education programs on correct biomechanical movement patterns when bending over and lifting objects, as this is a common task on the fireline that results in injury. These findings emphasize the importance of wildfire organizations, like BCWS, developing injury prevention programs that target the location of injury.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".