The Effectiveness of Acceptance and Commitment Therapy on Chronic Fatigue Syndrome and Pain Perception in People With Multiple Sclerosis
Bibliographic record
Abstract
Objectives The present study investigated the effectiveness of Acceptance and Commitment Therapy (ACT) on Chronic Fatigue Syndrome (CFS) and Pain Perception (PP) in people with Multiple Sclerosis (MS). Methods This was a quasi-experimental study with a pre-test, post-test and a control group design. The statistical population was all individuals with MS referring to the MS Society of Ahvaz, Iran, in 2018. Thirty patients with SFS were selected and randomly assigned into two groups of test and control (15 per group). Moreover, Multidimensional Fatigue Inventory (MFI) (was used to measure chronic fatigue symptoms) and Fatigue Severity Scale (FSS) and McGill Pain Questionnaire (MGPQ) were used for data collection. The achieved data were analyzed by Multivariate Analysis of Covariance (MANCOVA) in SPSS. Results The MANCOVA results revealed a significant difference between two groups in the following variables: perception of sensory pain (F=14. 70, P≤ 0. 001), perception of pain assessment (F=70. 50, P≤0. 01), perception of various pain (F=8. 13, P≤0. 001), PP (F=14. 68, P≤0. 001,) and CFS (F=4, P≤0. 05). Conclusion The study finding suggested that ACT was effective in reducing the severity of CFS and PP in the experimental group; this reduction has led to a relative improvement in MS condition. Therefore, clinicians working in health centers can use this treatment along with pharmacotherapy.
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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".