Comparing the effectiveness of treatment based on acceptance and commitment and mindfulness therapy on chronic pain in people with drug addiction
Bibliographic record
Abstract
Introduction: Not only can the experience of pain be a factor for drug use, but it can also be a motivation to use drugs again during and after treatment. Aim: The purpose of the present study was to compare the effectiveness of acceptance and commitment-based therapy and mindfulness-based therapy on chronic pain in People with drug addiction. Method: The research method was semi-experimental with a pre-test-post-test design and a control group with one-month follow-up. The statistical population included people suffering from drug abuse who referred to addiction treatment clinics in Arak city in 2021. Using available sampling method, 60 people were selected and randomly put/ placed in experimental and control groups (20 people in each group). The research tool included the revised McGill Pain Questionnaire (2009). Mindfulness therapy was implemented in 8 sessions (90 minutes each session) and acceptance and commitment based therapy was also implemented in 8 sessions (90 minutes each session). Data were analyzed using SPSS-25 statistical software and covariance analysis. Results: The results of covariance analysis for chronic pain in the post-test and follow-up phase showed/ demonstrated a significant difference (P=0.001). Both treatments had a significant effect on the improvement of chronic pain, and it is worth noting that this effect was greater for the treatment group for whom the treatment was based on acceptance and commitment (P=0.01). Conclusion: Treatment based on acceptance and commitment showed more effectiveness in improving chronic pain compared to mindfulness. Therefore, the use of acceptance and commitment-based therapy is recommended to improve chronic pain in people suffering from drug abuse.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".