OSH risk management policies between North America and Southeast Asia—a comparative review
Bibliographic record
Abstract
Introduction: Literature suggests that a comparative analysis of occupational safety and health (OSH) policies may provide valuable insights into creating and maintaining safer and healthier workplaces. However, there are ongoing debates about which type of OSH policies will be more effective. Furthermore, there is limited or no knowledge in the literature on the comparative analysis of OSH risk management policies between North America (Saskatchewan, Canada, and the USA) and Southeast Asia (Malaysia, Singapore, and Thailand). Methods: This review employed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to ensure the eligibility of included regional OSH policies and employed the Population, Intervention, Comparison, Outcome, and Study (PICOS) framework to develop search questions. This review conducted a high-level qualitative analysis to assess and compare the types of OSH policies and utilized a quantitative analysis to determine the effectiveness of these policies in the regions based on the data associated with Sustainable Development Goal (SDG) 8.1.1. Results: A qualitative analysis of regional OSH policies revealed that the North American regions adopted more performance-based OSH policy styles. In contrast, Southeast Asian regions tended to practice more prescriptive OSH policies. Singapore reported the lowest injury rates (both non-fatal and fatal) and the highest ratio of OSH inspectors to workers. General multivariate regression analysis indicated a significant and positive relationship between the ratios of OSH inspectors to employed persons and non-fatal injury rates, but the negative relationship between the OSH inspectors and fatal injury rate was neither significant nor reliable. Conclusion: The findings of this research validate the current literature. Additionally, higher ratios of OSH Inspectors to employed persons may significantly contribute to reducing regional non-fatal injuries. With larger sample sizes and primary research data, future researchers can build upon the findings of this research, including the optimal effective ratios of OSH inspectors to employed persons to prevent or minimize human suffering and loss. Practitioners may constantly monitor the effectiveness of the ratios to enhance the Sustainability Development Goal (SDG) 8.1.1 performance in the regions.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".