Alexithymia and Alcohol Dependence: The Roles of Negative Mood and Alcohol Craving
Bibliographic record
Abstract
Background: Alexithymia is a personality trait associated with emotion regulation difficulties. Up to 67% of alcohol-dependent patients in treatment have alexithymia. Objectives: The objective of this study was to investigate the direct and indirect effects of alexithymia, negative mood (stress, anxiety, and depression) and alcohol craving on alcohol dependence severity. Methods: Three hundred and fifty-five outpatients (mean age = 38.70, SD = 11.00, 244 males, range 18–71 years) undergoing Cognitive-Behavioral Therapy for alcohol dependence completed the Toronto Alexithymia Scale (TAS-20), Depression Anxiety Stress Scales (DASS-21), Obsessive Compulsive Drinking Scale (OCDS), and Alcohol Use Disorders Identification Test (AUDIT) prior to the first treatment session. Results: Alexithymia had an indirect effect on alcohol dependence severity, via both negative mood and alcohol craving (b = 0.03, seb = 0.008, 95% CI: 0.02–0.05). An indirect effect of negative mood on alcohol dependence via alcohol craving was also observed (b = 0.12, seb = 0.03, 95% CI: 0.07–0.16). Conclusions/importance: Alexithymia worked through negative mood and alcohol craving leading to increased alcohol dependence severity, indicating that craving had an indirect effect on the relationship between alexithymia and alcohol dependence severity. Targeting alcohol craving and negative mood for alcohol-dependent patients with alexithymia seems warranted.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".