Prevalence of musculoskeletal pain in school going adolescents using school bags - A co-relational research
Bibliographic record
Abstract
Children aged 12 – 18 years undergo rapid musculoskeletal development and an application of external forces (school bags) cause musculoskeletal disorders. The aim of this investigation was to assess the prevalence of neck, shoulder and back pain in the 1st term in school going adolescents using school bags. A co-relational research was conducted in Mangalore which included 580 students aged 13 -15 years. Their bag weight, body weight and height was measured and the subjects having pain either in the neck, shoulder or back were given McGill Melzack pain questionnaire to be filled. Descriptive analysis revealed that the percentage of bag weight on body weight ratio is more in females (mean ± SD 9.18 ± 3.71) compared to males (mean ± SD 8.88 ± 3.65). 6.03% of subjects carried bag weight weighing more than 15%, out of which 8.57% subjects complained of pain either in the neck, shoulder or back. The correlation between bag weight and pain was analysed using Karl Pearson’s correlation which is perfect positive (0.78). Analysis of correlation between BMI with percentage of bag weight in males (0.413) is more compared to females (0.086). The prevalence of adolescents having pain in the 1st term of school was 2.93% due to school bags. Hence, it is important to investigate further and take appropriate measures for adolescent problems with the use of school bags as it is a predictor for musculoskeletal disorder in adulthood.
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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 | 0.000 | 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".