Analysis of Occupational Health and Safety Risk Management: Hazard Identification, Risk Assessment, and Risk Control-HIRARC for Workers at Health Quarantine Offices in Makassar, Indonesia
Bibliographic record
Abstract
Work hazards and risks are closely related to occupational activities and have the potential to cause injuries and occupational diseases. Every workplace carries the risk of accidents, as reflected in data from Indonesia's Work Accident Insurance Program (JKK BPJS Ketenagakerjaan). The number of workers experiencing fatalities due to occupational accidents and diseases decreased from 4,007 cases in 2019 to 3,410 cases in 2020 but increased again to 6,552 cases in 2021. This study aims to assess occupational health and safety risk management using the Hazard Identification, Risk Assessment, and Risk Control (HIRARC) method among workers at the Makassar Health Quarantine Center. This descriptive study involved a population of 133 workers, with a sample of 57 workers selected using simple random sampling. Data were collected using the HIRARC questionnaire and analyzed using univariate analysis. The results showed that the majority of respondents were aged 40–49 years (57.9%), and 73.7% worked more than 8 hours per day when assigned to night shifts. The HIRARC assessment identified that the most common occupational hazard experienced by workers was ergonomic risk, with complaints of back, waist, and shoulder pain, classified as a moderate risk. In conclusion, ergonomic hazards pose a significant issue among workers, categorized as a moderate risk level. Therefore, it is recommended that the Makassar Health Quarantine Center enhance its occupational health and safety risk management and conduct regular evaluations of workplace hazards and risks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".