Health research priorities for wildland firefighters: a modified Delphi study with stakeholder interviews
Bibliographic record
Abstract
OBJECTIVES: The increase in global wildland fire activity has accelerated the urgency to understand health risks associated with wildland fire suppression. The aim of this project was to identify occupational health research priorities for wildland firefighters and related personnel. DESIGN: In order to identify, rank and rate health research priorities, we followed a modified Delphi approach. Data collection involved a two-stage online survey followed by semi-structured interviews. SETTING: British Columbia, Canada. PARTICIPANTS: Participants included any current or past wildland firefighter or individuals engaged in related roles. There were 132 respondents to the first survey. Responses to the first survey were analysed to produce 10 research topics which were ranked by 75 participants in the second survey (response rate: 84%). PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the identification, ranking and level of agreement of research priorities through a two-round online survey. We contextualised these findings through deductive and inductive qualitative content analysis of semi-structured interviews. RESULTS: The most important research priorities identified were (% consensus): effects of smoke inhalation on respiratory health (89%), fatigue and sleep (80%), mental health (78%), stress (76%) and long-term risk of disease (67%). Interviews were completed with 14 individuals. Two main themes were developed from an inductive content analysis of interview transcripts: (1) understanding the dynamic risk environment; and (2) organisational fit of mitigation strategies. CONCLUSIONS: Participants expressed a general concern with the unknown mental and physical health impacts of their jobs, including the long-term risk of morbidity and mortality. Future research must address knowledge gaps in our understanding of the health impacts of wildland fire and work to develop appropriate mitigation strategies while considering the needs of workers and unpredictable workplace environment. TRIAL REGISTRATION NUMBER: Open Science Framework, https://osf.io/ugz4s/.
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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 | 0.092 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".