Does psychotherapy improve alexithymia? A comparison study among patients with mild or moderate depression
Bibliographic record
Abstract
Background Alexithymia is reported to be a risk factor for depression. Psychotherapy is efficient for treatment of depression. Yet, the effect of psychotherapies on alexithymia is poorly understood. Objectives We aimed to compare Cognitive Behavioral Therapy (CBT), Existential Psychotherapy (ExP) and Supportive Counseling (SUP) for therapeutic efficacy and effect on alexithymia in depression. Methods There were 22 patients for each patient group. Sessions were performed as eight consecutive weekly and following two monthly boosters. Sixty six healthy controls were added. Prior to the sessions, patients received Sociodemographic Data Form, the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-1), Hamilton Depression Rating Scale (HDRS) and 20-item Toronto Alexithymia Scale (TAS-20). The control group received Sociodemographic Data Form, SCID-1 and TAS-20. Patients additionally received HDRS and TAS-20 after their weekly and booster sessions. Results Patients’ mean TAS-20 score was greater than of controls, however, it did not have a significant change throughout the study. Mean HDRS scores of ExP and CBT groups were lower than SUP group at the end. Discussion Alexithymia did not improve with psychotherapy. The exception was effect of ExP on externally oriented thinking. Psychotherapies all improved depression. CBT and ExP were more helpful than SUP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".