Alexithymia and Outcome in Psychotherapy
Bibliographic record
Abstract
BACKGROUND: About 25% of all patients seeking psychotherapeutic treatment are considered to be alexithymic. Alexithymia has been assumed to be negatively associated with therapeutic outcome. On the other hand, it is unclear to which extent alexithymia itself may be modified by psychotherapeutic interventions. METHODS: From 414 consecutively admitted inpatients, 297 were followed up after 4 weeks (t1) and after 8-12 weeks (t2) upon discharge. Patients were treated with psychodynamic group therapy in a naturalistic setting. The Toronto Alexithymia Scale (TAS-20) and the Symptom Checklist-90 were administered. RESULTS: Twenty-seven percent of the patients were alexithymic (TAS-20 >/=61) at baseline. Multivariate models with repeated measurements indicated significant changes in Global Severity Index of the Symptom Checklist-90 in both alexithymic and nonalexithymic subjects. However, alexithymic subjects had significantly higher Global Severity Index scores than nonalexithymic subjects at t0, t1 and t2 (p < 0.001). The TAS-20 scores demonstrated a high relative stability in the total sample. However, in the alexithymic group, the TAS-20 scores changed considerably from baseline to discharge [66.3 (SD = 4.7) to 55.9 (SD = 9.9); t = 8.69; d.f. = 79; p < 0.001]. CONCLUSION: The inpatient treatment program including psychodynamic group therapy significantly reduced psychopathological distress and alexithymic features in alexithymic patients. Still, these patients suffered from higher psychopathological distress at discharge than nonalexithymics. Therefore, alexithymic features may negatively affect the long-term outcome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".