Health risks and mitigation strategies from occupational exposure to wildland fire: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Due to accelerating wildland fire activity, there is mounting urgency to understand, prevent, and mitigate the occupational health impacts associated with wildland fire suppression. The objectives of this review of academic and grey literature were to: 1. Identify the impact of occupational exposure to wildland fires on physical, mental, and emotional health; and 2. Examine the characteristics and effectiveness of prevention, mitigation, or management strategies studied to reduce negative health outcomes associated with occupational exposure to wildland fire. METHODS: Following established scoping review methods, academic literature as well as government and industry reports were identified by searching seven academic databases and through a targeted grey literature search. 4679 articles were screened using pre-determined eligibility criteria. Data on study characteristics, health outcomes assessed, prevention or mitigation strategies studied, and main findings were extracted from each included document. The results of this scoping review are presented using descriptive tables and a narrative summary to organize key findings. RESULTS: The final sample was comprised of 100 articles: 76 research articles and 24 grey literature reports. Grey literature focused on acute injuries and fatalities. Health outcomes reported in academic studies focused on respiratory health (n = 14), mental health (n = 16), and inflammation and oxidative stress (n = 12). The identified studies evaluated short-term outcomes measuring changes across a single shift or wildland fire season. Most research was conducted with wildland firefighters and excluded personnel such as aviation crews, contract crews, and incident management teams. Five articles reported direct study of mitigation strategies, focusing on the potential usage of masks, advanced hygiene protocols to reduce exposure, fluid intake to manage hydration and core temperature, and glutamine supplementation to reduce fatigue. CONCLUSIONS: While broad in scope, the evidence base linking wildland fire exposure to any one health outcome is limited. The lack of long-term evidence on changes in health status or morbidity is a clear evidence gap and there is a need to prioritize research on the mental and physical health impact of occupational exposure to wildland fire.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".