Prevalence of work-related musculoskeletal injuries among South Indian hand screen-printing workers
Bibliographic record
Abstract
BACKGROUND: Hand screen-printing (HSP) plays a predominant role in textile industries in developing countries. Workers from HSP industry were mostly affected by musculoskeletal injury due to monotonous, and prolonged work nature and poor workplace environment. OBJECTIVE: The present study aims to investigate the prevalence of work-related musculoskeletal disorder (MSD) symptoms and risk factors associated among the HSP industry workers. METHODS: Cochran's sample size for categorical data was used to select 385 HSP workers of 1000 samples from various provinces of Tamil Nadu, INDIA. Modified Nordic based questionnaire was used to assess the musculoskeletal injuries and risk factors among HSP workers. RESULTS: The statistical analysis revealed that 62.5% workers are prone to MSD symptoms with lower back (75.1%), shoulder (66.2%), knees (58.7%), and ankle/feet (55.6%). Age, experience, marital status, stress in the job were the risk factors which significantly (p < 0.05) associated with the reported MSDs. Further, this study result infers that the subjects with higher age and experience are exposed higher levels of MSD prevalence of 85.5% and 92.0% respectively in past 12 months than other groups. Among the different work categories in HSP task, the workers reported with the maximum discomfort during printing work (63.1%) with Odds ratio as 10.38 and 95% CI is 6.18-17.4. than the material handling and drying task. CONCLUSIONS: Study results infer that HSP workers are prone to lower back and shoulder pain followed by knees and ankle feet regions. Socio-demographic factors, awkward posture and repetitive movements contribute to cause MSD among hand screen-printing workers.
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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 | 0.001 |
| 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) | 0.002 | 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".