The Role of Artificial Intelligence in Enhancing the Occupational Safety and Health Management Systems
Bibliographic record
Abstract
Aim: This study aimed to investigate the role of Artificial Intelligence (AI) in enhancing the occupational health and safety of workers within Oman context. Study design: A quantitative approach was used to collect and analyse data on participants’ perceptions, on benefits, challenges, and preferences among professionals engaged in safety. Methodology: A self-administered questionnaire was distributed to 125 health and safety professionals across selected workplace in Oman. The study sample included engineers, healthcare professionals, and health and safety practitioners who use artificial intelligence within their daily tasks. The study sample was chosen purposively. SPSS software version 26.0 was used to analyze quantitative data. Results: The results of this study indicate a wide support for the deployment of Artificial Intelligence within safety management domain, with some concerns regarding privacy, data reliability, and job security. There were no statistically significant differences in perception among participants roles, indicating consistent views on AI adoption in the Occupational Health and Safety (OHS) domain. However, the results might have been influenced by the predominance of younger more technologically savvy participants. Conclusion: Artificial intelligence is considered a valuable addition to Occupational Health and Safety (OHS) systems in Oman, and by extension, in the GCC and the world. To guarantee an effective integration of AI into OHS in Oman, it is essential to establish national training and readiness initiatives to enhance workforce skills and digital proficiency across various sectors. This study proposes a clear roadmap for the deployment of AI within OHS in Oman by highlighting the relevant concerns. It also provides practical guidance for policymakers and practitioners in support of safer and more resilient workplaces while incorporating advanced technologies into OHS practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".