Empirical Study on Enhancing Safety Culture and Behavior in Thailand's Construction Industry Through Safety Interventions
Bibliographic record
Abstract
The construction industry continues to face high rates of workplace accidents, particularly in building projects.This study investigates the effects of four categories of safety interventions: organizational, culture, attitudinal and belief-based, and behavioral, on safety behavior and culture among construction workers in Thailand.A survey conducted among 98 contractors in Bangkok and surrounding areas was analyzed using Structural Equation Modeling.Results indicate that behavioral interventions, positively influenced by attitudes and beliefs, as well as safety culture interventions, have a direct effect on enhancing safety behavior and culture.The study also suggests that organizational interventions do not directly influence these outcomes but exert their greatest impact indirectly by shaping culture, attitudes, and behaviors as mediators.Importantly, organizational interventions demonstrated the highest total effect in the model, underscoring their foundational role in driving change across other intervention types.Additionally, improvements in safety behavior and culture can be achieved by focusing on five intervention practices: safety training programs, PPE programs, maintenance of safety equipment, thorough safety inspections, and strict maintenance schedules for machinery and equipment.This study provides valuable insights for site management to improve safety behavior and culture through the effective use of safety interventions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".