The Effect of Acceptance and Commitment Therapy on the Psychological Consequences of Anxiety, Pain Intensity, and Fatigue in Women with Fibromyalgia: Study with the Effect of Waiting
Bibliographic record
Abstract
Background and Objective: Fibromyalgia syndrome is associated with major psychiatric disorders, such as anxiety and debilitating fatigue.This study aimed to determine the effect of Acceptance and Commitment Therapy on anxiety, chronic fatigue, and pain intensity in women with fibromyalgia with the effect of waiting.Materials and Methods: This quasi-experimental study with a pretestposttest design was conducted on 40 patients of Bouali Hospital in Tehran, Iran, in 2019.The samples were selected by the available sampling method and randomly assigned to the experimental and control groups.The subjects completed Beck Anxiety, McGill Pain, and Krupp Fatigue questionnaires before and after Acceptance and Commitment Therapy training.The collected data were analyzed in SPSS software (version 22) using analysis of covariance and repeated measures. Results:The results of analysis of covariance showed that Acceptance and Commitment Therapy reduced anxiety ( F=20.92, P<0.001), fatigue (F=13.66,P=0.001), and pain intensity (F=6.17,P=0.019) in patients with fibromyalgia, which was significant (P<0.001).Conclusion: Acceptance and Commitment Therapy through the creation and development of psychological acceptance and flexibility, can reduce patients' psychological damage and lead to a decrease in anxiety, pain intensity, and fatigue in women with fibromyalgia.
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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) | 0.003 | 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".