Effectiveness of Acceptance and Commitment based Therapy on Pain Severity, Fatigue, and Alexithymia in Female Patients with Rheumatic Diseases
Bibliographic record
Abstract
Aim: The aim of this research was determine the effectiveness of acceptance and commitment based therapy on pain severity, fatigue, and alexithymia in female patients with rheumatic diseases. Methods: This study was quasi-experimental with a pretest, posttest and three month follow-up design with a control group. The research population was female patients with rheumatic diseases who referred to the rheumatology clinic of Imam Hossein Hospital of Tehran city in the spring of 2021, which number of 30 people of them after reviewing the inclusion criteria were selected by purposeful sampling method and randomly replaced into two equal groups. The experimental group was trained 8 sessions of 90 minutes (one session per week) with the acceptance and commitment based therapy method and the control group remained on the waiting list for training. Data were collected by revised version of the short-form McGill pain questionnaire (Dworkin et al., 2009), fatigue severity scale (Krupp et al., 1989) and Toronto alexithymia scale (Bagby et al., 1994) and analyzed by methods of repeated measures analysis of variance and bonferroni post hoc test in SPSS-21 software. Results: The results showed that acceptance and commitment based therapy reduced the pain severity, fatigue and alexithymia in female patients with rheumatic diseases and the results remained in the follow-up phase (P<0.001). Conclusion: The results showed the effectiveness of acceptance and commitment based therapy and its persistence in reducing pain severity, fatigue and alexithymia in female patients with rheumatic diseases. Therefore, health professionals and therapists can use acceptance and commitment based therapy along with other therapies methods to improve features, especially pain severity, fatigue and alexithymia.
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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 | 0.000 |
| 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".