Temporal Stability of Alexithymia Over a Five-Year Period in Outpatients with Major Depression
Bibliographic record
Abstract
BACKGROUND: Previous research on alexithymia and depression has led to a controversy over whether alexithymia should be viewed as a state-dependent phenomenon or as a stable personality trait. The aim of this 5-year follow-up study was to examine the temporal stability of alexithymia in outpatients suffering from major depression. METHODS: The study population comprised 116 (49 male and 67 female) outpatients with major depression. Alexithymic features were assessed with the Toronto Alexithymia Scale (TAS-20) and the degree of depression with the Beck Depression Inventory. The patients were retested after a period of 5 years. RESULTS: Mean alexithymia and depression scores decreased significantly over the 5-year period. Alexithymia and depression were associated with each other, but the high test-retest correlations in the TAS-20 scores indicate relative stability of alexithymia. The three factors of alexithymia behaved differently. Difficulty in identifying feelings and difficulty in describing feelings were associated with alleviation of depressive symptoms, whereas externally oriented thinking was not. CONCLUSIONS: Alexithymia seems to be related with the severity of depression in outpatients with major depression, but it also shows relative stability over 5 years. Our findings support the view that the alexithymia construct represents a stable personality trait, but is also a state-dependent phenomenon.
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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 | 0.003 |
| 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 | 0.000 |
| 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".