The Effectiveness of Acceptance and Commitment Therapy on Pain Control and Adherence to Treatment in Dialysis Patients
Bibliographic record
Abstract
Background: Pain control and adherence to treatment is one of the most common problems in dialysis patients. Psychological treatments can be effective in reducing the problems of these patients. This study attempted to investigate the effectiveness of acceptance and commitment therapy (ACT) on pain control and adherence to treatment among dialysis patients. Methods: It was a semi-experimental pre-test, post-test study with a control group. The statistical population consisted of 40 people who were referred to a dialysis clinic in 2022 and an available sampling method was used to select and randomly assign patients to two experimental and control groups. In the experimental group, ACT was performed in eight sessions of 90 minutes. McGill pain questionnaire (MPQ) and adherence to treatment scale were used. Data were analyzed using SPSS software, version 21 and analysis of covariance. Results: There was a significant difference between the mean scores of pain control and adherence to treatment in the two experimental and control groups (p<0.05). The effect of this treatment on increasing the pain control score was 51% and on increasing the adherence to treatment score was 44%. Conclusion: ACT can increase pain control and adherence to treatment in dialysis patients; thus, it can be used in designing treatment plans for dialysis patients.
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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 | 0.006 |
| 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".