Safety climate, safety behaviors and line-of-duty injuries in the fire service
Bibliographic record
Abstract
Purpose – The purpose of this paper is to test an initial model of safety climate for firefighting. Relationships between safety climate, safety behaviors and firefighter injuries were examined. Design/methodology/approach – Data were collected from 398 professional firefighters in the southeastern USA. Structural equation modeling, using a zero-inflated Poisson regression method, was used to complete the analyses. Findings – Safety climate, as a higher order factor, was comprised of four factors including management commitment to safety, supervisor support for safety, safety programs/policies and safety communication. Both safety compliance behaviors and safety participation behaviors were significantly, positively associated with safety climate. Both behaviors were deemed protective and were associated with reductions in injury. Safety climate relations to injury were interesting, but somewhat ambiguous. Safety climate significantly predicted membership in the “always zero” injury group. For those not in the “always zero” group, the relationship between safety climate and injury was positive, which was not completely surprising as direct relationships between safety climate and injury have been insignificant and opposite to predictions in studies using retrospective data and may be attributed to reverse causation. Originality/value – This novel study illustrates the importance of both organizational and work unit factors in helping shape safety climate perceptions among firefighters. The results also support the safety climate – behavior – injury model and show that a positive safety climate encourages safer behaviors among firefighters. Lastly, the findings confirm that both safety compliance behaviors and safety participation behaviors are important to reducing individual firefighter injury experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".