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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".