Alexithymia and personality in relation to dimensions ofpsychopathology in male alcohol-dependent inpatients -
Bibliographic record
Abstract
Alexithymia and personality in relation to dimensions of psychopathology in male alcohol-dependent inpatients Objective: The aim of this study was to examine the capacity of alexithymia to predict psychiatric symptoms relative to other personality dimensions and age. Method: Participants were 176 consecutively admitted male alcohol dependent inpatients who were administered the Toronto Alexithymia Scale (TAS-20), Temperament and Character Inventory (TCI), and the Symptom Checklist-Revised (SCL-90-R). Results: Fifty three patients (30.1%) had alexithymia. Means of subscales of SCL-90 and the global severity index were higher among those with alexithymia than those without. The difficulty in identifying feelings factor of the TAS-20 significantly predicted all SCL-90-R subscale scores, whereas none of the SCL-90-R subscales were predicted by difficulties describing feelings or externally orientated thinking. The TCI dimensions emerged as distinct and conceptually meaningful predictors for the different SCL-90-R subscales. Somatization measured by subscale of SCL-90 was not as strongly predictable by difficulty in identifying feelings factor in alcohol dependents as was in general psychiatric patients. Conclusions: The present study supported the hypothesis that a current psychopathology is associated with difficulties in cognitively processing emotional perceptions among alcohol dependent inpatients. Since this was a study of cross-sectional design, the question of whether alexithymia represents a risk factor for psychopathology among alcohol dependent inpatients requires further evaluation.
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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 | 0.001 | 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) | 0.002 | 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".