The Effectiveness of Transdiagnostic Therapy Based on Repetitive Negative Thoughts on the Alexithymia, Emotional Regulation in patient with Psychogenic Non Epileptic Seizures
Bibliographic record
Abstract
The purpose of this study was to investigate the effectiveness of Transdiagnostic Therapy Based on Repetitive Negative Thought on the Alexithymia, Emotional Regulation in patients with Psychogenic Non Epileptic Seizures. Method: In this research, an experimental single-case design with asynchronous multiple base line was used. The efficacy of Transdiagnostic Therapy Based on Repetitive Negative Thought was evaluated during three stages of intervention including baseline, treatment and follow up. Three patients were selected through purposive sampling and entered the study. In this study, Alexithymia Toronto Questionnaire and Barking Emotional Regulation were used. The data were analyzed using the Reliable Change Index, clinical significances and visual inspection.Results: In this study, the reliable change index for the Alexithymia variable in the first to third patients was 4.46, 4.08 and 4.46, respectively, which was significant (RCI >1.96), and therefore, no random changes were made. Recovery rates are 53%, 47% and 57% for the first patient, respectively, indicating that they are successful in the first and third patients and the relative success in the second patient. In the emotional regulation variable, the change in reliable change index was 2.02 in all three patients, which was significantly (RCI > 1.96) significant. The percentage of emotional regulation recovery in these three patients is 75%, 72%, and 57%, which is in the range of recovery and treatment success. Conclusion: The findings indicate the effectiveness of Transdiagnostic Therapy Based on Repetitive Negative Thought in decreasing Alexithymia and increased emotional regulation.
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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 | 0.000 | 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 | 0.000 |
| 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".