Extinguishing Stigma among Firefighters: An Examination of Stress, Social Support, and Help-Seeking Attitudes
Bibliographic record
Abstract
Firefighters are exposed to highly stressful environments, often witnessing multiple traumatic events throughout their careers. The cumulation of stress and traumas firefighters are exposed to have left many in the profession with physical and psychological injuries, and with such injuries left untreated, can lead to lifelong suffering or suicide. The primary objectives for this research investigate firefighter occupational stress, peer support, and attitudes towards help-seeking for mental health in the hopes to fill in gaps understanding why firefighters continue to suffer in silence. Employing a mixed-methods research design, a survey questionnaire was collected from 254 firefighters from a large fire department in British Columbia, Canada. Consistent with the existing literature, findings suggest that the levels of peer support mitigated occupational stress, that is, those who reported higher levels of peer support also reported lower occupational stress levels. Firefighters provided information on what types of support they prefer according to the types and severity of stressors. Suggestions from the respondents provide information on how barriers to receiving help, such as stigma, may be addressed at the organizational level. Implications and recommendations for interventions addressing stigma and help-seeking amongst firefighters are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".