The Mediating Effect of Safety Culture on Safety Communication and Human Factor Accident at the Workplace
Bibliographic record
Abstract
Rapid development in industrialization and global economy has contributed to the increased number of workplace injuries and accidents. Nowadays, with the advancement and the reliability of technology, accidents caused by equipment and machinery failures seem to be on decline. However, human element tends to feature as a significant contributor to workplace accidents: statistical reports and evidence indicate that around 80 to 90 percent of work-related accidents can be attributed to human factors. Meanwhile, effective safety communication is believed to play a vital role in human factor accidents at the workplace. Effective communication among the workers and leaders is believed to help in the attenuating the risk of human factor accidents. Against this background, this research examines 394 sets of questionnaires with 89.14% response rate from respondents of manufacturing companies in Negeri Sembilan, Malaysia. Based on the results, the interaction between safety communication and human factor accident is found to be significant.In addition, this study investigates the mediating effect of safety culture between safety communication and human factor accident. The results show that safety culture significantly mediates by the relation of safety communication and human factor accident.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".