Performance optimization of human factors and safety performance using an integrated DEA-TOPSIS approach: A case study in the process industry
Bibliographic record
Abstract
Stress, fatigue, and work situation awareness are key contributors to accidents and unsafe behaviors in process industries. Given the significance of these factors, this study aimed to assess the employees' perceptions of the effects of stress, fatigue, and work situation awareness on safety performance in a process industry. The data of this study were collected through a questionnaire, and their reliability was evaluated and confirmed. The Data Envelopment Analysis (DEA) method was used to identify and analyze the most influential factors and sub-factors influencing employees' perceptions of safety performance. Additionally, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied to rank alternatives and validate the DEA results. Sensitivity analysis revealed that work situation awareness significantly affected safety performance compared to stress and fatigue. Furthermore, the findings showed that distraction, chronic fatigue, and demands were the most influential sub-factors of work situation awareness, fatigue, and stress, respectively. The Pearson correlation test confirmed a strong agreement between the DEA and TOPSIS results. Given these findings, stress, fatigue, and work situation awareness play an important role in safety performance of employees in the process industries.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".