The prevalence of low back pain and its relationship with physical activity, age and BMI in Fars Payam-e Noor University staff
Bibliographic record
Abstract
Introduction: The purpose of this study was to determine the prevalence of low back pain and to evaluate its relationship with physical activity, age and BMI in Fars Payam-e noor University (PNU) staff. Materials and Methods: Of the total number of 709 Fars PNU employees, 182 men and women working in 12 branches of this university across Fars Province-Iran were chosen objectively. Low back pain data were collected via Quebec questionnaire. Chi-square test, Pearson correlation coefficient and ANOVA test were used for statistical analysis of data (significance level set at P < 0.05). Results: The study results showed a high prevalence of low back pain (86/3%) among studied sample, especially in the age range of 30 to 40 years. There was a significant difference between people with LBP and those without LBP (P = 0.000), but no significant difference between faculty members and university staff was observed (P > 0.05). Low back pain was negatively related to physical activity (P = 0.02). Moreover, there was no significant correlation between LBP and either age or sex (P > 0.05). Most subjects had normal body mass indexes (BMI). In addition, statistical analysis of data through ANOVA indicated a significant relationship between BMI and low back pain (P = 0.02). Conclusion: The Results indicate that employees who had regular physical activity have less low back pain. Given positive relation between regular activity and less low back pain we recommend that employees allocate some time for regular physical activity in their weekly schedule in order to prevent low back pain. Keywords: Low back pain, University staff, Regular activity
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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 | 0.001 | 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) | 0.003 | 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".