The Effectiveness of Emotion Focused Therapy on Alexithymia and Psycho -somatic Complaints in Patients of COVID-19 under Home Treatment
Bibliographic record
Abstract
The purpose of the present study was to determine the effectiveness of emotion focused therapy on alexithymia and psychosomatic complaints in patients of COVID-19 under home treatment. A quasi-experimental design was used. The statistical population included patients of COVID-19 under home treatment in early 6 months of 2021 in Tehran. Through snowball sampling method, 30 participants who met the inclusion criteria were selected and randomly assigned to two groups of EFT or control ( n1=n2=15(. Research tools were Takata & Sakata’s Psychosomatic Questionnaire, Toronto Alexithymia Scale, which were administered in three stages for two groups and Goldman, Greenberg’s EFT to the experimental group during ten weekly, 90- minute sessions and data were analyzed using mixed model repeated measures analysis variance. Finding showed that EFT could decrease psychosomatic complaints, difficult in recognizing feelings, difficult description of emotions and concrete thought and this effect remained stable at follow- up. Therefore, EFT effect on decreasing Alexithymia and psychosomatic complaints in patients of COVID-19. According to efficacy this treatment package, was suggested it be used for decreasing Alexithymia and psychosomatic complaints in patients of COVID-19.
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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 | 0.001 |
| 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) | 0.002 | 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".