Combined’ Neck/Back Pain and Psychological Distress/Morbidity Among the Saudi Population: A Cross-Sectional Study
Bibliographic record
Abstract
Background Psychological distress/morbidity is amongst the primary reason for the cause of pain at multiple sites, its progression, and recovery. Though still not very clear if physical pain in the neck or the back may predict psychological morbidities or not. Thus, we investigated the association between combined neck or back pain and psychological distress/morbidity. Methods A cross-sectional study was conducted in Al-Kharj, Saudi Arabia, including 1,003 individuals. The questionnaire comprised of General Health Questionnaire-12 (GHQ-12) and some questions about neck and back pain. Data analysis was done using statistical software SPSS version 26.0. Results The results of the multivariate analysis revealed a significant positive association between neck/back pain status and total GHQ score (unstandardized Beta = 2.442, P ≤ 0.0001). Having neck/back pain had almost a 2.5 times greater risk of psychological distress/morbidity. Further, females were more likely to have a higher risk of psychological distress/morbidity (unstandardized Beta = 1.334, P = 0.007) than males while adjusting for sociodemographic and clinical characteristics. Conclusion The combination of neck and back pain was significantly associated with the Saudi population’s psychological problems. Therefore, the Saudi government needs to devise high-risk strategies and allocate adequate resources to the cause so that at-risk people can be shielded from the adverse complications arising from this condition in the long run.
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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 | 0.000 | 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) | 0.001 | 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".