Building resilience in volunteer firefighters: Bridging the research to practice gap
Bibliographic record
Abstract
There are approximately 14,000 firefighters in British Columbia (BC); notably, over 10,000 are volunteers or paid-on-call. The volunteer fire rescue services (FRS) globally tend to be vastly under-resourced in terms of equipment, apparatus, and training yet the volunteer FRS are the life-blood of the majority of communities in BC. The FRS is considered a high-risk profession in relation to physical and psychological hazards faced by firefighters as part of their job. Volunteer FRS are challenged to maintain an engaged volunteer membership when the work of firefighters is unpredictable, risky, and takes time away from family, work, and other obligations. Given the multitude of ongoing stressors faced by firefighters, it has become evident that attention must be paid to the mental health of firefighters. However, when assets are scarce and require prioritizing, services to preserve healthy minds are often backgrounded to seemingly more critical choices of gear, equipment, and apparatus. Hence volunteer firefighters are often disadvantaged when it comes to information, education, and initiatives for mental health. The purpose of this research was to create, present, and evaluate the effectiveness of a resilience education programme for 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 | 0.038 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".