THE EFFECT OF BACK SCHOOL INTERVENTION ON CHINESE PATIENTS WITH CHRONIC LOW BACK PAIN
Bibliographic record
Abstract
Background The Back School program has been recommended in many countries around the world for patients with low back pain (LBP) to help improve self-efficacy to enhance their prognosis. However, few studies have reported on the application of the Back School in East Asia, including China. This study aimed to explore the Back School’s effect on Chinese adults with chronic LBP based on four areas: posture, knowledge of LBP, physical activity and body performance. Material and methods There were 10 participants in the intervention group and 11 in the control group. Baseline data was collected prior to the intervention, including upper body physical examination, core and lower limb muscle examination, Roland-Morris Disability Questionnaire, LBP Knowledge Questionnaire and Global Physical Activity Questionnaire. Physical indicators and questionnaires were retaken after the 8-week Back School intervention. The differences between the two groups were compared before and after the intervention. Results There was a statistically significant increase in McGill trunk flexion test results and knowledge of LBP (especially basic knowledge and treatment sections) in the intervention group. Conclusions The Back School-based intervention model has a positive impact on muscle performance in the core area and knowledge acquisition of LBP in Chinese patients with chronic LBP.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".