Alexithymia as a Predictor of Residual Symptoms in Depressed Patients Who Respond to Short-Term Psychotherapy
Bibliographic record
Abstract
Residual symptoms are increasingly becoming recognized as an important problem in the treatment of major depression. It is unclear which individuals are more likely to suffer from residual symptoms following treatment. This study investigated the role of alexithymia in the prediction of residual symptoms following treatment with psychotherapy. The study utilized data from 33 outpatients with major depression who were positive responders to psychotherapy. Alexithymia was assessed prior to treatment using the 20-item Toronto Alexithymia Scale. Depressive and anxious symptomatology were assessed using the Beck Depression Inventory and the Spielberger State-Trait Anxiety Inventory, respectively. Alexithymia factor 1 (difficulty identifying feelings) was predictive of the severity of residual symptoms, over and above the effect of initial levels of depression and anxiety, form of psychotherapy, and use of antidepressant medication. The findings suggest that difficulty identifying feelings may constrain one's ability to effectively utilize psychotherapy, thereby contributing to the persistence of residual symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".