“We are unique”: organizational stressors, peer support and attitudes toward mental health treatment among airport firefighters
Bibliographic record
Abstract
INTRODUCTION: Airport firefighters are responsible for providing emergency responses to aviation incidents on a runway or in the vicinity of an airport, including airplane crashes, mass casualty events, emergency landings and many other concerns on airport grounds. While data exist on the occupational stressors of firefighters and public safety personnel in general, there is a gap in knowledge regarding the experiences of airport firefighters, particularly in relation to their organizational stressors, peer supports and attitudes toward mental health treatment. METHODS: We conducted two focus groups with 10 career firefighters working at an airport in Atlantic Canada in 2019. Focus groups were recorded; the recordings were transcribed and later coded using thematic analysis, which took an inductive, iterative, narrative approach. RESULTS: Airport firefighters face unique challenges, and operational stressors are overshadowed by organizational stressors. Additionally, peer support is an integral aspect of coping with both organizational stressors and critical incidents. Firefighters were found to have positive attitudes toward mental health treatment in general, but several barriers still remain, such as stigma, fear of being placed on leave and fear of confidentiality breach. CONCLUSIONS: Specialized treatment options for public safety personnel and airport firefighters who engage in serious incidents outside of their regular duties are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".