Mental health outcomes and psychosocial risk factors in wildland firefighters and support staff
Bibliographic record
Abstract
,Wildfire seasons are increasing in intensity and duration, compounding the demands of an already challenging occupation. Wildland firefighters are a unique first responder population not yet adequately represented in the literature or in first responder mental health services. Existing research on wildland firefighters focuses almost exclusively on the experiences of direct suppression staff, thereby excluding a significant portion of the workforce involved in support and management roles. This research addresses that gap by assessing mental health outcomes and psychosocial risk factors in BC Wildfire Service wildland firefighters and support staff during the 2023 fire season. High prevalence rates of probable PTSD (26%), probable major depressive disorder (30%), probable generalized anxiety disorder (30%), high stress (27%), high suicide risk (34%), and high rates of moderate- to high-risk tobacco (40%), alcohol (23%), cannabis (33%) and caffeine use (83%) were identified. Logistic regression was used to identify predictive factors at the individual, positional, and organizational levels for each outcome variable. A distinct combination of variables significantly predicted each mental health outcome. Negative mental health outcomes were most often predicted by existing mental health diagnoses and significant prior trauma at the individual level and by excess job demands, lack of career development, and inadequate work/life balance at the organizational level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".