Alexithymia in a sample of alcohol-dependent patients: Clinical correlations and cognitive patterns
Bibliographic record
Abstract
Introduction Alexithymia represents a personality construct, characterized by an inability in identifying and verbally describing own and others’ emotions. According to the recent research on emotional dysregulation and the alexithymic construct, it has been described a positive correlation between alcoholism and alexithymia. The present study aims to evaluate the presence of alexithymia in a sample of alcohol dependant patients and, therefore, analyze how the presence of these alexithymic traits may influence/interact with a range of cognitive processes such as the anger rumination, metacognitive capabilities and dissociative experiences. Methods A sample of 40 alcohol dependant inpatients affected with alcohol dependence and alcohol-related issues were recruited, evaluated and compared with a sample of 40 healthy controls. A clinical evaluation and a complete clinical and psychological assessment were carried out in order to investigate alcohol-related clinical patterns, alexithymia construct, anxiety and depression symptomatology and cognitive pattern. Results Subjects with alcohol addiction show higher total scores in all tests except the scale that evaluates anxiety, compared to healthy controls. Finally, a factorial ANOVA analysis demonstrated that alcoholism seems to be determined by the lack of emotional recognition from which derives a dissociative state, which consequently generates a depressive rumination. Conclusions According to the recent literature, the present study identifies a significant proportion of alexithymic patients within the sample of subjects affected by alcohol-related disorders. Other clinical variables (i.e. depression, pathological anxiety-related worry, anger rumination, dissociation and metacognitive capabilities) mirror a specific cognitive pattern in the sample of alcoholics rather than the healthy group. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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".