A Comparative Study on Alexithymia in Depressive, Somatoform, Anxiety, and Psychotic Disorders among Koreans
Bibliographic record
Abstract
OBJECTIVE: Little is known about the characteristic differences in alexithymic construct in various psychiatric disorders because of a paucity of direct comparisons between psychiatric disorders. Therefore, this study explored disorder-related differences in alexithymic characteristics among Korean patients diagnosed with four major psychiatric disorders (n=388). METHODS: Alexithymic tendencies, as measured by the Korean version of the 20-item Toronto Alexithymia Scale (TAS-20K), of patients classified into four groups according to major psychiatric diagnosis were compared. The groups consisted of patients with depressive disorders (DP; n=125), somatoform disorders (SM; n=78), anxiety disorders (AX; n=117), and psychotic disorders (PS; n=68). RESULTS: We found that substantial portions of patients in all groups were classified as having alexithymia and no statistical intergroup differences emerged (42.4%, 35.9%, 35.3%, and 33.3% for DP, SM, PS, and AX). However, patients with DP obtained higher scores in factor 2 (difficulties describing feelings) than those with SM or AX, after adjusting for demographic variables. CONCLUSION: These findings suggest that alexithymia might be associated with a higher vulnerability to depressive disorders and factor 2 of TAS-20K could be a discriminating feature of depressive disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".