The effectiveness of acceptance and commitment therapy on intensity pain and pain catastrophizing in women with chronic pain
Bibliographic record
Abstract
Background: The present study was conducted with the aim of determining the effectiveness of acceptance and commitment therapy on intensity pain and pain catastrophizing in women with chronic pain. Materials and methods:This study was semi-experimental with a pretest-posttest and follow-up with a control group design.The statistical population was made up of female patients with chronic pain referred to Emma Reza hospital in Tabriz city in 2023.In total, 30 people were selected by purposive sampling method, and randomly divided into two groups (15 people each).Members of the experimental group received their treatment in eight sessions of 1.5 hours; however, the control group did not receive any treatment.Both groups answered the 20-question sleep quality Pittsburgh McGill pain intensity and 29-question Sullivan et al pain catastrophic Questionnaires, before, after and 3 months after the intervention.Data were Analyzed using ANOVA with repeated measures. Results:The results showed that the acceptance and commitment therapy reduced the intensity pain and pain catastrophizing women with chronic pain in the post-test compared to the control group (P<0.001).The effect of acceptance and commitment therapy on intensity pain and pain catastrophizing was lasting in the follow-up phase (P<0.001).Conclusion: It seems that acceptance and commitment therapy can reduce the intensity pain and pain catastrophizing in women with chronic pain, therefore it is likely to be a useful treatment strategy to improve the intensity and pain catastrophizing in women 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 | 0.000 |
| 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".