The Effectiveness of Cognitive-Behavioral Intervention on Pain Perception and Subjective Well-Being in Patients with Chronic Low Back Pain
Bibliographic record
Abstract
Introduction:Low back pain is one of the mos t common causes of health care visits and has many health-economic consequences for individuals and society.The purpose of this s tudy was to inves tigate the effectiveness of the cognitive-behavioral intervention on pain perception and mental well-being in patients with chronic low back pain.Materials and Methods: This was a quasi-experimental s tudy with the pre-tes t and pos t-tes t design.30 patients with chronic low back pain referred to Karaj medical centers were selected.Research tools, including the McGill pain ques tionnaire and subjective well-being ques tionnaire, were used for this s tudy.In the intervention group, the cognitive-behavioral program was adminis tered in 10 sessions (1 session per week for 1 hour), and then the pos t-tes t was taken.Results: The results showed that cognitive-behavioral intervention had a significant and positive effect on perceived pain and mental well-being in the experimental group.Conclusion: The results of this s tudy showed that cognitive-behavioral intervention can be effective in reducing chronic pain and improving mental well-being in patients with chronic low back pain.Therapis ts can use this approach to improve community-based health promotion behaviors and rehabilitation for patients with chronic pain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".