Ergonomic Hazard Control Modeling for Informal Welding Workers in Greater Bandung: A Study on Musculoskeletal Disorders (MSDs)
Bibliographic record
Abstract
Ergonomic hazards are one of the causes of health problems in workers, including causing Musculoskeletal Disorders (MSDs) complaints. MSDs in workers affect physical fitness, reduce working days/hours, and ultimately are unable to work. WHO states that around 1.71 billion people have musculoskeletal conditions worldwide. MSDs complaints in Indonesia are a separate focus, research on MSDs complaints in the informal sector shows that 66% of workers experience MSDs complaints. One industry that has a high risk of MSDs is welding. The purpose of this study was to create an ergonomic hazard control model for informal welding sector workers in Greater Bandung. The research approach is quantitative, the type of analytical observational research with a cross-sectional design. The study population was informal welding sector workers in Greater Bandung, sample was taken using a purposive sampling technique, and the total number of samples in this study was 100 workers. The analysis used in this study was the chi-square test to see the relationship between variables. Modeling in this study will use binary logistic regression. The results of the study showed that working climate, working posture, workload, and physical fitness simultaneously influenced MSDs complaints of informal welding workers (p-value < 0.05). Based on these findings, the control of MSDs complaints can be achieved by effectively managing work climate, working posture, workload, and physical fitness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".