Resilience and Health Promotion in High-risk Professions: A Pilot Study of Firefighters in Canada and the United Kingdom [pre-print]
Bibliographic record
Abstract
This study, conducted by researchers from Canada and England in collaboration with four fire rescue services, explored Canadian and UK firefighters’ experiences of distress, coping, and resilience related to workplace traumatic events. Questions addressed in the research included: Are firefighters resilient? How do firefighters define resilience? Does stress education enhance/sustain resilience? A cross sectional, mixed methods study design was used with a qualitative theoretical drive supplemented with quantitative measures to compare and contrast firefighters’ phenomenological cross-cultural experiences. Research outcomes include: a variety of diverse and intricate definitions for resilience reflecting the complexity of the concept of resilience yet demonstrating cultural commonalities across both countries; a range of reactions to critical incidents that generally fell into one or more domains: emotional, cognitive, physical, behavioural, and ‘spiritual’; a range of strategies that are implemented to cope with stress reactions - overwhelmingly ‘talking’ about the incident, reactions, and coping mechanisms is most helpful; personal and organizational attributes that assist in managing stress and stressful events within the culture of the fire service; and health promoting strategies for building resilience. The study recommendations, utilizing a health promotion lens, offer guidance in planning for, and responding to, traumatic events in high-risk professions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".