Relationship between musculoskeletal body region pain complaints, depression and physical activity level in major hospitals of Abbottabad, Pakistan
Bibliographic record
Abstract
Abstract Introduction/Aim The growing recognition of musculoskeletal disorders in the northern region of Pakistan, specifically Abbottabad, lacks contextually relevant data for devising effective prevention and intervention strategies in the mitigation of MSDs burden. This study aims to explore the relationship between musculoskeletal body region pain complaints, depression and physical activity of those visiting major hospitals in Abbottabad. Methodology The cross-sectional study design aimed to determine the incidence of MSDs (NMQ: Nordic Musculoskeletal complaints rating questionnaire) in 384 female participants in Abbottabad. Informed consent was obtained from all the subjects. Secondly, the relationship between MSD regions, depression (PHQ-9: patient depression questionnaire) and physical activity (IPAQ: International Physical Activity Questionnaire) was sought via phi value, Cramer's V value and logistic regression. Results The incidence of MSDs was noticeably higher in females above 40 years of age and who were employees. There was a significant (p < 0.05) negative weak to moderate correlation (Phi − 0.103 to -0.148) between employment and MSDs regions. In contrast, depression and physical activities had a significant (p < 0.05) positive correlation (Cramer's V: 0.129 to 0.225) with the upper back, shoulders and elbow. Individuals who were depressed were more likely to complain about their upper back and shoulders (OR 1.4, p < 0.01). Conclusion A significant relationship was observed between depression and MSDs. Higher musculoskeletal pain complaints were noticed in females who were not employees. The study suggested that awareness and multi-dimensional interventions addressing physical, psychological and social barriers are required for prevention and decrease in MSD complaints.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".