Alexithymia disrupts emotion regulation processes and is associated with greater negative affect and alcohol problems
Bibliographic record
Abstract
OBJECTIVE: Alexithymia is common among people who abuse alcohol, yet the mechanisms by which alexithymia exerts its influence remain unclear. This analysis tested a model whereby the three subscales of the Toronto Alexithymia Scale exert an indirect effect on alcohol problems through difficulties with emotion regulation and psychological distress. METHOD: Men and women (n = 141) seeking alcohol use disorder (AUD) treatment completed the Toronto Alexithymia Scale, the Difficulties with Emotion Regulation Scale, the Brief Symptom Inventory, the Short Inventory of Problems, and the Alcohol Dependence Scale. RESULTS: The Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale was positively associated with alcohol problems through emotion dysregulation and psychological distress. The other two subscales, Difficulty Describing Feelings and Externally oriented Thinking, were not associated with any other variables. CONCLUSION: People with alexithymia may consume alcohol to help regulate undifferentiated states of emotional arousal. Given the prevalence of alexithymia among people who abuse alcohol, treatment supplements that enhance the identification of emotions are needed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".