The role of organizational supports in mitigating mental ill health in firefighters: A cohort study in Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Little is known about the effectiveness of ongoing mental health support in reducing the mental health impacts of a traumatic deployment. METHODS: A cohort of firefighters was established among those deployed to a devastating wildfire in Alberta, Canada in May 2016. Firefighters completed three questionnaires: at recruitment giving details of exposures, a first follow-up reporting mental health supports before, during, and after the fire and a second follow-up, at least 30 months after the fire, with screening questionnaires for anxiety, depression, and posttraumatic stress disorder (PTSD). Fire chiefs were interviewed about mental health provisions. The impact of supports on mental ill health was estimated, adjusting for clustering within fire service and potential confounders. RESULTS: Of 1234 firefighters in the cohort, 840 completed the questionnaire on mental health supports. In total, 78 of 82 fire chiefs were interviewed. Analysis of the impact of supports on mental ill health included 745 firefighters from 67 fire services. Only 45.8% of reports of peer support were concordant between firefighters and fire chiefs. After adjusting for confounding, the odds ratios (OR) for peer support reported by both fire chief and firefighter were depressive disorder: OR = 0.22, 95% confidence interval (CI), 0.08-0.61; anxiety disorder: OR = 0.45, 95% CI, 0.24-0.82; PTSD: OR = 0.62, 95% CI, 0.37-1.02. Symptoms of anxiety and depression but not PTSD were reduced by resiliency training before the fire and by support offered within 48 h of return from deployment. CONCLUSION: The results suggest peer support in firefighters is protective but its availability is poorly recognized. PTSD was somewhat less responsive, perhaps reflecting the cumulative effects of previous exposures.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".