Predictors of Musculoskeletal Disorders Among Teachers: An Exploratory Investigation in Malaysia
Bibliographic record
Abstract
Purpose: The present study aimed to examine the prevalence and gender differences in MSDs among teachers, as well as the interaction of associated predictor .In addition, another aim of the study was to investigate the contribution of these predictors, which have not been examined thoroughly particularly in Malaysia. Methodology: A cross-sectional study was employed in this study. A questionnaire was used to measure physical factors, psychosocial factors, workload, work-life balance, general well-being, and MSDs levels among primary school teachers (N=460) from 10 primary schools in Kota Kinabalu. Findings: The prevalence of MSD in the past 6 months was 61.7% (95% CI: 57.4% – 65.9%). The present study findings also indicated that there were significant gender differences in MSDs between female and male teachers (t = 1.04, p< .05). Hierarchical multiple regression was conducted to examine a range of predictors related to MSDs. Physical factors (ß = .17, p<0.05). Multiple regression was used for a variety of predictors that are associated with MSD. Physical factors (ß = .17, p<0.05), psychosocial factors (ß = -.14, p<0.05), and general well-being (ß = .43, p<0.01) are significantly associated with MSD in Malaysian primary school teachers. Overall, model statistic result was F (3, 276) = 36.730, p=0.001, R² = .45 and adjusted R² = .435. The model explained 44.7% (r= 0.67) of the variance in MSD discomfort. Conclusion: The studies concerning MSDs among teachers revealed the need for a significant effort, not only to examine the risk factors but also to develop interventions to minimize MSDs for those in the teaching profession.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".