Is Alexithymia Associated with Specific Mental Disorders
Bibliographic record
Abstract
BACKGROUND: Alexithymia is characterized by restrictions in the perception, differentiation and regulation of affects. It is considered to be an important vulnerability factor for the development of mental disorders. Little is known, however, of whether alexithymia is associated with specific mental disorders. SAMPLING AND METHODS: Data from 1,461 patients of an outpatient clinic for psychosomatic medicine with various mental disorders (depressive disorders, anxiety disorders, adjustment disorders, somatoform disorders, eating disorders, and psychological and behavioral factors of physical illness) were collected between January 2007 and October 2009. The 20-item Toronto Alexithymia Scale (TAS-20) was administered to study alexithymia. The diagnoses were made following ICD-10 guidelines. RESULTS: In our sample, the total prevalence of alexithymia (TAS-20 ≥ 61) was 21.36%. The percentage of alexithymic patients was significantly increased in the group of patients with depressive disorders (26.9%) as compared to other diagnostic groups. Using TAS-20 as a continuous measure, multiple hierarchical regression analyses revealed that higher TAS-20 total scores were significantly associated with depressive and anxiety disorders. However, after controlling for the level of depression, the association of anxiety disorders with alexithymia was no longer significant. With regard to TAS-20 subscales, 'difficulty describing feelings' (subscale 2) was also significantly related to depressive disorders. CONCLUSIONS: According to the results, the prevalence of alexithymia is relatively high in patients with mental disorders. The increased prevalence of highly alexithymic subjects suggests that alexithymia is associated with a higher vulnerability to mental illness. The prevalence of alexithymia was especially increased for depressive disorders. Thus, further evidence supporting the concept of 'alexithymic depression' was provided. From a therapeutic perspective, treatments should be developed that take the specific needs of highly alexithymic patients into account.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".