Female Firefighter Work-Related Injuries in the United States and Canada: An Overview of Survey Responses
Bibliographic record
Abstract
Objectives: This study explored how demographic characteristics, life experiences, and firefighting experiences have an impact on work-related injuries among female firefighters, and described events surrounding such work-related injuries. Methods: This online survey was available from June 2019 to July 2020. Questions related to demographic characteristics, life experiences, firefighting experiences, and work-related injuries. Descriptive analyses characterized variables by the presence or absence of work-related injury, injury severity, job assignment, and country of residence. Results: There were 1,160 active female firefighter survey respondents from the US and Canada, 64% of whom reported having at least one work-related injury. US respondents made up 67% of the total but 75% of the injured sample. Injured respondents were older, had been in the fire service longer, and had a greater number of fires and toxic exposures than non-injured respondents. Heavier weight, tobacco use, and alcohol consumption were more common among injured respondents. The two most common contributing factors to work-related injuries were human error and firefighter fatigue. Among respondents who reported an injury-related time loss claim, 69% were wearing protective equipment when injured, and 9% of the injuries directly resulted in new policy implementation. Conclusions: These findings can help inform resource allocation, and development of new policies and safety protocols, to reduce the number of work-related injuries among female firefighters.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".