Mental health implications of fire service membership.
Bibliographic record
Abstract
The primary goal of the current study was to add to the literature regarding mental health implications of fire service membership. Paid-professional firefighters (n = 94) were compared with workers from non-emergency-service occupations (n = 91) with respect to posttraumatic symptomatology as well as other symptoms of mental illness. The results suggested that firefighters self-reported greater posttraumatic symptomatology than comparison participants as measured by the Impact of Events Scale—Revised. In addition, the firefighters reported more distress on several subscales of the Symptom Checklist 90—Revised. Specifically, firefighters scored higher than the non-emergency-service participants on self-reported interpersonal sensitivity, anxiety, hostility, and psychoticism. Contrary to the original hypotheses, no links were evident between years of service and posttraumatic/mental health symptoms. Overall, this project suggests that firefighters are at substantially higher risk for traumatic stress symptoms as compared with other workers who do not work within the emergency services. In addition, it is suggested that previous reports of additional mental health symptoms experienced by firefighters may actually be more consistent with secondary reports of posttraumatic symptomatology. A secondary goal of this study was to provide exploratory data regarding potential links between firefighters’ mental health and self-reported personality characteristics. These data suggest that neuroticism may play a special role in the prediction of posttraumatic symptomatology for 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".