The Effectiveness of Acceptance and Commitment Therapy on Pain Severity, Perceived Stress, and Aggression in Patients with Multiple Sclerosis in Isfahan, Iran
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is the most common neurological disease. The aim of this study was to determine the effectiveness of acceptance and commitment therapy (ACT) based on pain severity, perceived stress, and aggression in patients with MS. Methods: This experimental research was conducted with a pretest-posttest design. The study population included all patients with MS referred to health centers in Isfahan, Iran, in 2016. The study participants consisted of 60 patients selected using convenience sampling. The participants were divided into two groups (30 patients in the experimental group and 30 patients in the control group). The data collection tools included the short-form McGill Pain Questionnaire (SF-MPQ) and Perceived Stress Scale (PSS). Data analysis was performed using multivariate analysis of covariance (MANCOVA) and analysis of covariance (ANCOVA). Results: The results showed that ACT was effective in reducing pain (F = 28.22; P < 0.01), perceived stress (F = 5.16; P < 0.03), and aggression (F = 6.86; P < 0.01) in patients with MS, and these results were persistent in the follow-up period. Conclusion: ACT is effective in reducing pain, perceived stress, and aggression in patients with MS.
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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 | 0.001 |
| 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) | 0.001 | 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".