Firefighters, hostility, and satisfaction with life, job and marital relationship.
Bibliographic record
Abstract
Hostility is associated with negative health outcomes. Empirical research has indicated that high levels of hostility, in association with personal characteristics, may result in either aggressive actions and re-actions, or isolation and disengagement. The purpose of this study was to investigate hostility and its influence on mental health, overall satisfaction with life, job, and marital relationship, and cardiovascular health of professional firefighters. The study was analyzed in the context of Social Ecology Theory exploring how personality, spousal relationship, and social factors influenced the relationship between work and health. Firefighters were invited to engage their romantic partners in the study assessing how work stress impacted intimate relationship. Data analyses involved structural equation modeling, as well as repeated measures multivariate analysis of variance and multilinear regressions. The results indicated that work stress and exposure to toxic environment and hazardous conditions have a negative impact on the mental health and overall satisfaction of firefighters, but not on hostility. When controlling for personality, openness to experience revealed a significant relationship between work and hostility. No significant relationships were observed either between hostility and domestic conflict or between hostility and cardiovascular health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".