Relationship Between Emotional Processing, Drinking Severity and Relapse in Adults Treated for Alcohol Dependence in Poland
Bibliographic record
Abstract
AIMS: Growing data reveals deficits in perception, understanding and regulation of emotions in alcohol dependence (AD). The study objective was to explore the relationships between emotional processing, drinking history and relapse in a clinical sample of alcohol-dependent patients. METHODS: A group of 80 inpatients entering an alcohol treatment program in Warsaw, Poland was recruited and assessed at baseline and follow-up after 12 months. Baseline information about demographics, psychopathological symptoms, personality and severity of alcohol problems was obtained. The Schutte Self-Report Emotional Intelligence (EI) Test and Toronto Alexithymia Scale (TAS) were utilized for emotional processing assessment. Follow-up information contained data on drinking alcohol during the last month. RESULTS: At baseline assessment, the duration of alcohol drinking was associated with lower ability to utilize emotions. Patients reporting more difficulties with describing feelings drank more during their last episode of heavy drinking, and had a longer duration of intensive alcohol use. A longer duration of the last episode of heavy drinking was associated with more problems identifying and regulating emotions. Poor utilization of emotions and high severity of depressive symptoms contributed to higher rates of drinking at follow-up. CONCLUSIONS: These results underline the importance of systematic identification of discrete emotional problems and dynamics related to AD. This knowledge has implications for treatment. Psychotherapeutic interventions to improve emotional skills could be utilized in treatment of alcohol-dependent patients.
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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) | 0.001 | 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".