A decade in focus: occupational health and safety research trends - a bibliometric approach
Bibliographic record
Abstract
Introduction: Occupational health and safety (OHS) is a critical area of research due to its direct impact on worker well-being and productivity. Understanding the evolving trends and patterns within this domain provides valuable perspectives on the global focus and advancements made over the past decade. This study conducts a bibliometric analysis of OHS literature to map its intellectual structure, identify influential contributors, and highlight emerging themes. Methods: A bibliometric study was conducted analyzing publications from 2014 to 2024 in Scopus on occupational health and safety. Descriptive statistics, co-word clustering, and citation network analysis were performed on 664 articles from 223 sources. Results: Results reveal significant increases in annual publications and citations over time, indicating a growing priority in the field. The US, Canada, Turkey, and Iran emerged as leading contributors. Core institutions, such as NIOSH and selected universities, demonstrated intense leadership. Key researchers publishing the most include Hasle, Gibb, Iavolici, and Mori. Key thematic areas included occupational health nursing, diseases, construction safety, risk assessment approaches, and management strategies, with risk assessment emerging as a particularly influential methodology. Conclusion: Occupational safety research is demonstrating dynamic global growth with sustained high-quality outputs from the leaders of core institutions. Methodological innovations and interdisciplinary priorities necessitate ongoing investigation.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.183 | 0.260 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".