Risk Factors for Low Back Pain Among Filipino Manufacturing Workers and Their Anthropometric Measurements
Bibliographic record
Abstract
This study looked into the prevalence of and risk factors for low back pain among workers in manufacturing industries in the Philippines. Anthropometric measurements were also done to establish the design principles of the working equipment, protective equipment, and tools of the Filipino worker to prevent musculoskeletal disorder. This was a cross-sectional study using a stratified random sampling technique. Thirty-one industries were selected. Various workstations were sampled from each industry where subjects were selected. There were 495 workers surveyed for the symptoms questionnaire and 544 for the anthropometric measurements. Results showed that 5.1 percent experienced discomfort, 2 percent had trunk rigidity, and 1.4 percent had both limitations of trunk motion and activities of daily living. Logistic regression showed that low back pain was significantly associated with leaning, bending, and carrying for 2-8 hours (p at.05), and with standing for 2-8 hours (p at.001). It was also found to affect work performance and more likely to occur 14 times as often after work as during the initial work sessions. Anthropometric measurements of the workers showed that the mean height is 159.96 cm, mean chest height is 115.70 cm, waist height is 96.95 cm, and knee height is 47.37 cm. Anthropometric data between sexes was also presented. This anthropometry can be used for the design of working equipment of Filipino workers. It is recommended that risk factors for low back pain be understood and equipment be designed according to the body proportions of the workers identified in this study.
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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 | 0.000 | 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) | 0.002 | 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".