Mental health implications of volunteer fire service membership
Bibliographic record
Abstract
Purpose The purpose of this paper is to add to the overall body of literature regarding mental health implications related to fire service membership; in particular, to look specifically at the implications of volunteer membership and to compare results with previous research looking at paid‐professional members. Design/methodology/approach Responses to the Impact of Event Scale‐Revised (IES‐R), the Neuroticism‐Extroversion‐Openness Personality Inventory (NEO‐PI) and the Symptom checklist (SCL)‐90R were collected from a sample of volunteer firefighters (n=64), as well as from a similar comparison sample (n=103). Findings Volunteer fire service members reported significantly higher rates of posttraumatic stress symptomatology when compared to a similar group of comparison participants. In contrast, no differences were found in other types of mental health symptomatology between the volunteer fire fighters and comparison group. Additionally, there appeared to be few differences in the patterns regarding prediction of mental health symptomatology from individual personality characteristics for the two groups. Generally, the authors’ results suggested that, regardless of group, neuroticism was a predictor of mental health symptomatology in many domains. Originality/value To the authors’ knowledge, this is the only available study to have as its primary intent to describe the mental health implications of volunteer fire service membership, as opposed to a similar comparison sample. In addition, the authors’ data provide some meaningful comparison with previously published results found in a paid‐professional sample; such comparison, to this point, has been unavailable.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".