Canadian Career Firefighters’ Mental Health Impacts and Priorities
Bibliographic record
Abstract
Firefighters' perceptions of mental health can inform management. This qualitative study explored Canadian career firefighters' experiences, needs, and research priorities with respect to mental health. Thirty-nine career firefighters (33 men, 6 women) of different ranks and geographic locales were interviewed using a semi-structured interview guide. The interviews were recorded, transcribed, and qualitatively analyzed using thematic analysis within an interpretive description approach. Firefighters reported that critical incidents and chronic job stressors contributed to mental health symptoms that led to burnout, compassion fatigue, and mental and physical injury. They were concerned with family impacts, like lack of full openness, reduced financial stability, and risk of divorce; and work impacts, like interpersonal conflict, lack of support to fellow firefighters, task avoidance, and absenteeism. A broad array of barriers and facilitators were found in firefighter work, culture, programs, social supports, health care, and societal factors. Variability in access to help, the changing fire service, and the complexity of knowing what to do to achieve mental health were evident across themes. Firefighters identified the need for research in four areas: awareness and monitoring, understanding etiology of mental health, better prevention and treatment, and access to care. Across domains of inquiry, context, "two sides to the coin", and uncertainty were overarching themes.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".