Occupational Safety and Health Management in Selected Industrial Sectors in Sudan
Bibliographic record
Abstract
Introduction: Since Heinrich's early studies, work has been recognized as a substantial contributor to psychological and physical illness. Fast technological, economic, and social advancements have increased the number of occupational fatalities and illnesses in developing nations. Nonetheless, it is demonstrated that the creation, application, and enforcement of Occupational Safety and Health Management Systems (OSHMS) reduce accidents and enhance employees' well-being. This study aims to understand Sudan's current occupational safety and health situation and identify any challenges or gaps in the current system. Methods: A mixed methods approach deploying a literature review and secondary data was adopted to answer the research question about the status of occupational health and safety in Sudan. Results: A comparison of the artisanal and organized gold mining sectors over the years 2018-2020 shows an increase in the number of accidents in the artisanal sector but a sharp decrease in both the number and severity of accidents in the organized sector. The frequency rate declined in the organized sector but fluctuated in the artisanal sector. It was also found that many OSH incidents of different types and levels of severity occurred. In 2020, the Fatal Accident Rate (FAR) was 66.48 in artisanal gold mining, 0.55 in organized gold mining, and 0.01 in oil and gas. However, calculating and comparing other sectors' performance indicators to evaluate OSH's status was not possible for many reasons. Conclusion: Findings were constrained, possibly due to the limited occupational health and safety data. There is an urgent need to strengthen and improve the governance of occupational safety and health in Sudan. A more comprehensive study needs to be undertaken to assess the status of the OSH in formal and non-formal sectors and investigate the correlation of OSH to workers’ well-being and the Sudanese economy.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".