Alexithymia and Stress Response Patterns among Patients with Depressive Disorders in Korea
Bibliographic record
Abstract
OBJECTIVE: Alexithymic characteristics may represent cognitive and affective mediators between stressors and stress responses among those with depressive disorders. This study evaluated how alexithymic characteristics, as measured by the Korean version of the Toronto Alexithymia Scale-20 (TAS-20K), could be related to stress response patterns, as measured by the Stress Response Inventory (SRI), within a sample composed of individuals diagnosed with depressive disorders. METHODS: Participants comprised a cross section of patients diagnosed with depressive disorders (n=98). Data on demographic and psychosocial factors (i.e., sex, age, and level of education), clinical profiles {i.e., primary and comorbid psychiatric conditions meeting the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria at the time of the evaluation}, duration of illness, medications, and Clinical Global Impression (CGI) scores, and the results of psychological assessments (TAS-20K, SRI) were analyzed. RESULTS: Patients having depressive disorders with alexithymia obtained significantly higher scores in terms of all seven subscales of the SRI, as compared to those without alexithymia, a logistic regression model was used to assess possible predictors for the presence of alexithymia in patients with depressive disorders, including the seven subscales of the SRI, gender, age, and duration of illness. We found that aggressive and somatizing responses to stress were significantly associated with the presence of alexithymia among patients with depression. CONCLUSION: These findings suggest that patients having depression with alexithymia were more susceptible to stress than those without alexithymia. Clinicians might improve their treatment of depression by identifying the clinical predictors for alexithymia and by helping those individuals demonstrating such symptoms in coping with emotionally stressful situations.
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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 | 0.001 |
| 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 | 0.000 |
| 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".