A Comparative Study of the Effectiveness of Acceptance and Commitment Therapy and Transcranial Direct Current Stimulation on Anxiety, Depression, and Physical Symptoms of Individuals Suffering from Chronic Pain
Bibliographic record
Abstract
Background: The present research was conducted to compare the effectiveness of acceptance and commitment therapy (ACT) and transcranial direct current stimulation (tDCS) on anxiety, depression, and physical symptoms. Methods: This research falls among semi-probationary plans, with a pretest-posttest design, two groups, and follow-up. The research statistical population included all male and female out-patients who referred to any treatment centers in Tehran, Iran, in the years 2019-2020 with a chronic pain complaint and received a definitive diagnosis of chronic pain by neurologists and rheumatologists. In order to establish 3 groups using targeted sampling method (considering the inclusion and exclusion criteria), 30 patients were initially selected, and then, 15 patients were placed in the first experimental group and 15 patients in the second experimental group randomly. The research tools consisted of the Beck Depression Inventory (BDI) (1961), Beck Anxiety Inventory (BAI) (1990), and Mcgill Pain Questionnaire (MPQ) (2007). Research data were analyzed using repeated measures ANOVA. Results: The result of data analysis indicated that ACT and tDCS lead to a decrease in depression, anxiety, and physical symptoms. In addition, compared to tDCS, ACT had a more significant effect on reducing anxiety in individuals suffering from chronic pain (P < 0.05). Conclusion: Both ACT and tDCS had a significant effect on improving depression, anxiety, and physical symptoms in people with chronic pain. ACT was also more effective in reducing anxiety. However, there was no significant difference between ACT and tDCS in influencing depression and physical symptoms.
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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) | 0.001 | 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 | 0.000 |
| 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".