Analysis of Reports on the Occupational Health and Safety in The Agricultural Industry: A bibliometrix-Aided Approach
Bibliographic record
Abstract
Agricultural activities are fundamental to societies, including the planting, growing, harvesting and processing of agricultural crops. However, there are a large number of occupational risks that can arise during the course of agricultural activities. These risks could result in serious injury or even death. This requires introducing and providing the relevant bodies and workers with knowledge, perception and awareness of the risk. The present study assessed the available reports on occupational health and safety using a bibliometric analysis and dimension reduction approach. Briefly, the reports were extracted from SCOPUS database. We identified 943 relevant and available peer-reviewed publications from the Scopus database. These were published between 1956 and 2022. The retrieved documents were analysed with the R-studio based software Bibliometrix. For the analysis, co-occurrences of networks, thematic maps and trending topics were analyzed. The results of the present study show that the time span of the documents ranges from 1956 to 2022 and these documents, including journals, books, book chapters and conference papers, were disseminated in 313 different sources. The estimated annual growth rate of these documents is 6.35%. Even the first paper dates back to the 1950s, the average age of the documents are 10.7. Considering the spatial distribution of the documents, USA topped at the list and was followed by Australia, Brazil, Italy, Canada, UK, and China. It is interesting to note that 'confined spaces' were found to be the trending topic according to the trend topic analysis of the keywords. Also, after the basic keywords (occupational health and safety and agriculture) of the study, ergonomics was the core keyword of the relevant analysis. Critically, the level of co-operation between countries was very low, with a rate of 0.025-0.207 for co-operation between countries (MCPs). For Turkey, the MCP was found to be 0.000. According to the thematic map, the motor theme is composed of two major clusters. One relates to food safety, risk analysis, knowledge and awareness and hygiene. To the best of our knowledge, this is the first study of its kind to identify the key issues in occupational health and safety in the agricultural industry. Therefore, the study has potential to contribute to the field.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.169 | 0.151 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".