Psychosocial and Occupational Risk Factors of Musculoskeletal Pains among Computer Users: Retrospective Cross-Sectional Study in Iran
Bibliographic record
Abstract
Evaluation an etiological modelwith psychosocial and occupational risk factors has applied implication fortherapeutic intervention. This research was aimed to investigate psychosocialand occupational risk factors of musculoskeletal pains among computer users inSemnan Province of Iran. In this cross-sectional study, 324 computerusers from governmental offices and private industrial/organizationalinstitutes in the province were enrolled by random sampling at the age of 25 to63 yr old. Data were collected by Demographical-Occupational and Musculoskeletalpains history Questionnaire and a set of specialist-validated questions, theDepression Anxiety Stress Scales, Toronto Alexithymia Scale, and theMultidimensional Scale of Perceived Social Support. Gathered data were examinedvia binary logistic regression analysis. The mean age was 39.76±7.77 years,48.8% were male and 51.2% were female. Age, duration of occupation, dailycomputer usage, incorrect body posture, work overload, poor ergonomicknowledge, social support, alexithymia, depression and somatization were significantly associated withmusculoskeletal pains (p<.000). Daily computer usage (OR=18.408[4.306-27.519]), incorrect body posture (OR=11.786 [2.864-24.528]), workoverload (OR=8.725 [2.831-13.527]), poor ergonomic knowledge (OR=12.370[6.520-20.095]), social support (OR=1.088 [1.034-1.144]), alexithymia (OR=1.934[.897-2.971]), depression (OR=2.894 [.836-3.956]) and somatization (OR=13.032[3.626-.25.546]) were significant predictors of musculoskeletal pains (p<0.001).Psychosocial factors, work-related factors and lack of support or appropriateergonomic knowledge were all important correlates of musculoskeletal pains.Thus, efficient preventive plans require addressing all these aspects
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".