A study of school bag weight and back pain among intermediate female students in Dammam City, Kingdom of Saudi Arabia
Bibliographic record
Abstract
Background: The most common cause of low back pain in children is muscle sprain and strain which can occur from carrying a heavy backpack or from activities. This study aimed to assess the relationship between school bag weight and back pain among female students in Dammam city.Methods: A total of 300 female students were included in this study both from east and west sectors of Dammam city, Saudi Arabia. Tools: Data were collected using (1) A structured questionnaire sheet including, socio-demographic data of the students, and close-ended questions about the school-bags as methods of carrying, (2) A weight scale that measured student’s body weight and weight of the school bags, (3) A self-report (Numeric pain rating scale) that assessed pain intensity. Univariate and Multivariate Statistical analysis was performed to test the relationship between the study variables.Results: A total of 288 school children (96.2% out of 300) were carrying bags of weight more than 15% of their body weight. Shoulder and neck pain were reported by 40% of the female students. Statistically there is a significant relationship was found between school bags weight and severity of shoulder pain (p = .042).Conclusion and recommendation: The weights of schoolbags of Dammam city intermediate female students were higher than the internationally acceptable standards. Ministry of Education should set standards to prevent and mange problems of carrying heavy school bags in the intermediate school.
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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 | 0.001 | 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) | 0.002 | 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".