Risk of Musculoskeletal Disorder in the Working Posture of Online Motorcycle Drivers (Case Study: Drivers at Malang District)
Bibliographic record
Abstract
Variations in the dimensions of each type of motorcycle and variations in the anthropometry of the rider's body often cause complaints of discomfort in the body which can eventually lead to musculoskeletal disorders. The purpose of this study is to determine the value of the driver's body point complaints using Nordic body maps, determine the improvement of body posture in terms of the risk level of the driver's musculoskeletal disorders using the integration of rapid upper limb assessment and rapid whole body assessment. The research methodology uses a sample of 30 respondents, with 3 types of motorcycles. Stages of data analysis using Nordic body maps, RULA, REBA and Body Mass Index. The results of the study stated that there were 7 points of complaints of musculoskeletal disorders with the role of repetitive static activities. The working posture condition of the integration of RULA and REBA is stated that there is an improvement in body posture in the dimensions of RULA and REBA with the achievement of the need for the role of changing the dimensions of the motorcycle seat and handlebar as a development to suppress the occurrence of musculoskeletal disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".