Firefighters in Atlantic Canada: Balancing professional obligations and familiarity in rural communities
Bibliographic record
Abstract
Objective Rural firefighters encounter similar yet different experiences to urban firefighters. All firefighters experience potentially psychologically traumatic events, however, not all firefighters become traumatized. We set out to explore the factors rural firefighters identify as influencing a potentially psychologically traumatic event to become personally psychologically traumatic.Methods Six focus groups were conducted with 44 rural firefighters, both career (22) and volunteer (22). Participants were recruited from fire departments in two rural communities of an Atlantic Canadian province and self-identified as men (n = 38), and women (n = 6). The data were coded and analyzed using narrative analysis and were discussed repeatedly amongst team members until themes were reached.Results Four main themes emerged indicating what rural firefighters believe intensifies their likelihood of a potentially psychologically traumatic event becoming traumatic: “Dimensions of familiarity,” “Intrusive reminders,” “Unfavorable outcomes,” and “Personal connections.”Conclusions This study suggests that potentially psychologically traumatic events may become injurious for rural firefighters, both volunteer and career, due to the close personal connections with their work and communities. In these smaller communities, it was also noted that reminders, both physical and through the media impacted how firefighters viewed and reacted to these events.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".