The Effectiveness of Integrated Treatment Based on Acceptance and Commitment Therapy and Positivity on Adherence to Treatment and Life Expectancy of Women with Rheumatoid Arthritis: Single Subject Study
Bibliographic record
Abstract
Background: Rheumatoid arthritis (RA) is a progressive autoimmune disease with variable and inflammatory clinical symptoms that causes pain, dryness, and reduced joint function. The aim of this study was to investigate the effectiveness of integrated treatment based on acceptance and commitment therapy (ACT) and positivity on adherence to treatment and life expectancy of women with RA. Methods: In the present study, a single-case experimental design of the asynchronous multiple baseline type was used. The statistical population of the study included all women with RA in Khorramabad, Iran. The sample included 3 people who were selected by convenience sampling from among the affected women. Integrated treatment protocol based on ACT and positivity was performed during three stages of baseline, 10 sessions of 90-minute intervention, and three-stage follow-up, and the subjects responded to the adherence to treatment and life expectancy scales. Findings: The data were analyzed using visual drawing, reliable change index (RCI), and percentage change formula. Results of the study showed that the mean improvement of adherence to treatment variable in the post-treatment stage in the three patients was 62.7% and in follow-up was 60.5%; for the life expectancy variable in the post-treatment stage, the mean of all three was 53.3% and the follow-up mean was 51.03%. Conclusion: Integrated treatment based on ACT and positivity has been effective on treatment adherence and life expectancy of affected women. Therefore, it is suggested to use this treatment to increase compliance with treatment and life expectancy in these patients.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".