A Hybrid Structural Equation Modeling–Partial Least Square and Analytic Hierarchy Process Framework for Evidence-Based Safety Management in High-Risk Electricity Industry Operations
Bibliographic record
Abstract
Evidence-based safety management strategies that combine strong analytical techniques with context-specific decision support are necessary for high-risk electrical activities, especially during live working operations, i.e., work under voltage conditions (WUVC).This study aims to identify, validate, and rank factors contributing to safety risks in the electricity distribution industry in Indonesia by developing a hybrid framework that combines Structural Equation Modeling-Partial Least Square (SEM-PLS) and the Analytic Hierarchy Process (AHP) approaches.The links between safety factors, accident history, personal and environmental components, safety climate, and safety threats were examined using SEM-PLS analysis of survey data from 200 WUVC operators at PT XYZ.Strategic improvement rankings were then produced by incorporating significant latent variables into an AHP-based prioritizing process, including five national experts.The results demonstrate that safety factors, safety climate, and accident history all have a significant impact on safety hazards; however, the AHP results suggest that safety factors are the most important (weight = 0.255; Consistency Ratio (CR) = 0.04).Establishing specialized institutional structures for WUVC operations, improving the safety climate, and regularly updating Standard Operating Procedures (SOPs) and safety signage are the top strategic recommendations.The SEM-PLS data were used to create a KPI-based handbook, which was piloted with thirty operators, demonstrating its usefulness for fieldlevel safety assessment.In high-voltage live working environments, the suggested hybrid approach provides a structured, data-driven foundation for enhancing safety performance.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".