Assessing the influence of safety climate-related factors on safety performance using an Integrated Entropy-TOPSIS Approach
Bibliographic record
Abstract
This research investigated the effects of safety climate and psychosocial safety climate factors on safety performance of employees in a process industry. Operators and supervisors of control rooms participated in this study, and the collected data were analyzed by multi-criteria decision-making (MCDM) approaches. The entropy approach was employed to prioritize safety climate and psychosocial safety climate factors. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) got applied to rank the alternatives. The findings obtained by the entropy approach showed that “rule breaking” (among safety climate factors) and “organizational communication” (among psychosocial safety climate factors) had the greatest influence on safety performance of employees, respectively. TOPSIS results revealed that supervisors experienced a greater degree of safety performance than operators. The findings of this study demonstrated that safety professionals should consider both safety climate and psychosocial safety climate factors to promote safety performance in high-hazard process facilities.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".