Transcending adversity: resilience in volunteer firefighters
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a theory of psychological resilience in volunteer firefighters. Design/methodology/approach Using a constructivist grounded theory (CGT) approach, the qualitative study engaged a purposive sample of eight firefighters in Canada, conducted in-depth interviews and analysed the data using comparative methods. Findings The results provided unique insights into resilience in firefighters and revealing resilience as multidimensional, complex, dynamic and contextual. Six core concepts interrelate to construct resilience: relationships, personal resources, meaning-making, leadership, culture and knowledge. Practical implications The findings of this research offer a framework for practical integration of resilience theory into workplace health policy and practice. The theory was co-created with firefighters hence is contextually sound to this population, but applicable to other emergency and health services. Originality/value Volunteer firefighters are under-represented in the literature, despite facing intermittent and frequently intense work-related stressors; this research begins to address the gap in the literature. As well, previous resilience theories have noted relationships between some components, but there is little evidence linking categories; this theory more patently represents the complex nature of resilience in volunteer firefighters.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".