Dimentions of Alexithymia, and their relationships to Anxiety and Depression in Psychodermatologic patients
Bibliographic record
Abstract
Aims: Alexithymia is a personality trait that demonstrates inability in emotional expression. This trait emerging from emotion disregulation has an effective role in etiology of psychosomatic illnesses. Psychosomatic patients, due to somatic illness and also difficulty in emotion regulation, experience anxiety and depression symptoms that exacerbate each other in a cycle with dermatologic illnesses. Methods: In this post-hoc, cross-sectional and correlational study, Toronto Alexithymia Scale-20 (TAS-20) and the Hospital Anxiety and Depression Scale (HADS) were administered to all 215 patients who were referred to psychodermatology clinic of Isfahan University of Medical Sciences from autumn 2010 to autumn 2012. The final sample consisted of 140 patients. To analyze the data, we used SPSS software V.18. Results: The results showed that the frequency of high alexithymia, anxiety and depression in psychodermatologic patients were 41.8%, 51.2% & 40%, respectively. Assessing the relationship between alexithymia and its subscales with anxiety and depression showed that there were significant positive correlations between alexithymia and difficulty in identifying and describing feelings with anxiety and depression (p<0/001). The results of regression analyses of alexithymia on anxiety and depression showed that difficulty in identifying and describing feelings predicts depression; and difficulty in identifying feelings predicts anxiety in patients. Conculusion: Alexithymia predicts anxiety and depression of psychodermatology patients and a complete treatment of these patients should be considered.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".