Risk Perception and Occupational Health and Safety: Evaluation in National and Global Context
Bibliographic record
Abstract
Risks can be prevalent problems both within national borders and beyond. Examples of recent global infectious diseases such as natural disasters, man-made disasters (such as exposure to radiation), Covid, H1N1, and Ebola viruses can serve as examples of this. Interpretations and subjective judgments about risk are called risk perceptions and are important determinants of health and risk-related decisions (such as policy decisions about nuclear power plants, genetically modified foods, processed meats). We conducted research on studies on perception around the world, trends and what can be studied in the future. In this study, we identified 137 relevant publications from the SCOPUS database between 1987 and 2023. All the data obtained were analyzed using the Bibliometrix computer program based on R-studio. Analyses included the analysis of co-occurrences of networks, thematic maps, and trending topics. According to the findings of the present study, all reports were published in 101 sources since 1987. These documents have an annual growth rate of 5.55, increasing significantly after 2009. The countries with the highest number of publications are Australia, followed by Canada, Italy, Portugal, and the United States. Due to the nature of the topic, the occurrence of joint publications among countries is quite low. Individuals’ awareness of occupational health and safety issues increases their awareness of workplace risks and, therefore, the likelihood of preventing workplace accidents and occupational illnesses.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.031 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".