The Association between Ergonomic Risk Factors, RULA Score, and Musculoskeletal Pain among School Children: A Preliminary Result
Bibliographic record
Abstract
A study was done to investigate the risk factors associated with musculoskeletal disorder (MSD) among school childrenin Malaysia. Two schools in the Klang Valley and one in Johor Bahru were selected. A cross sectional study was donebaginning February 2009 until May 2009. Two hundred and twenty nine pupils among 2nd Grade (age 8 years) and 5thGrade (age 11 years) from three school were selected to participate in this study. Musculoskeletal symptoms wererecorded using Modified Nordiq Body Map Questionnaires; Tanita weight measurement was used to measure schoolbag weight and students’ weight. Modified Rapid Upper Limb Assessment (RULA) was used to assess the awkwardposture of the students’ torso namely bending forward movement, leaning, sitting and twisting. A high prevalence ofMSD among school children aged 11 years old (5th grade) was found (68.0%) compared to 8 years old (2nd grade) with36.4%. Result also showed that 31.8% of 2nd grade and 62.2% of 5th grade students experience upper MSD. LowerMSD complains showed 5th grade students are at higher percentage than 2nd grade with 45.5% and 20.9% respectively.Logistic regression analysis controlling for body mass index, sport injuries and history of major accidents revealed thatthe following factors were related to MSD: home activities (involving television (TV) watching and personal computer(PC) used) [1.889, 95% CI 1.081 – 3.301] and bag perception complain among school children [2.148, 95% CI 1.086 –4.247]. For specific MSD symptoms it was found that the duration of home activities using PC or TV exceeding 2 hourswas the main the risk factor of upper MSD among school children [4.923, 95% CI 1.188-20.389]. In conclusion,combination of risks leads to high increase of MSD among school children in Malaysia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".