Prevalence of Neck Pain and Associated Factors with Personal Charateristics, Physical Workloads and Pyschosocial Among Male Rubber Workers in FELDA Settlement Malaysia
Bibliographic record
Abstract
UNLABELLED: Rubber tapping processes posed potential risk of various health problems among rubber workers. It ranges from simple musculoskeletal aches to more serious and complicated structural damage to bone, muscles, tendons and nerves of musculoskeletal system. These health problems might be linked directly to the arduous demands of farm labor. OBJECTIVES: A cross-sectional study was conducted to determine the prevalence of neck pain (NP) and musculoskeletal symptoms (MSS) and its association with personal characteristics, physical workloads and psychosocial factors among rubber workers. METHODS: Stratified random sampling method was adopted and a total of 419 rubber workers in FELDA's scheme Malaysia participated in this study. Data was collected through face to face interview using modified Standardized Nordic Questionnaire (SNQ) and Job Content Questionnaire (JCQ). RESULTS: The results revealed the prevalence of NP was 59.9% and weak correlation with age (?= -0.184, p= 0.001) and a positive weak correlation with working hours per day (?= 0.099, p= 0.043) significantly. All physical workloads (neck flexion or rotation, awkward postures, repetitive motion and static postures) had significant weak to moderate positive correlation with NP (p<0.05). Job insecurity was found to have weak and positive correlation with NP (p<0.05). Binary logistic regression analysis showed risk factors for NP were decreased with age (OR= 3.92, 95% CI 1.61 - 9.58, p=0.003), increase in neck flexion or rotation (OR= 9.52, 95% CI 5.55 - 16.32, p= 0.001), awkward postures (OR=2.23, 95% CI 1.29 - 3.86, p= 0.004) and static postures (OR= 1.86, 95% CI 1.10 - 3.14, p= 0.021). CONCLUSION: This study showed that high prevalence of NP was associated with neck flexion or rotation, awkward and static postures.
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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".