1484 – Could Emotional Awareness Influence Drinking Outcomes In An Alcohol Dependent Population?
Bibliographic record
Abstract
Introduction Alexithymia has been studied in several addictive disorders with a special interest in alcohol dependence (Thorberg et al., 2009; Taieb et al., 2002). Nevertheless, recent studies failed to demonstrate the relationship with drinking outcomes (de Haan et al., 2012; Stasiewicz et al., 2012). Evaluation of emotional awareness in these populations could be added to specify the deficits of emotions’ differentiation and to understand the mechanisms underlying the maintenance of abstinence (Bochand & Nandrino, 2010; Carton et al., 2010). Aims We intend to evaluate short and long-term abstinent alcohol dependent subjects in a dimensional approach for alexithymia and emotional awareness, expecting relationship with drinking outcomes. Methods Thirty-two abstinent alcohol dependent participants and 28 matched controls were included. The two groups were assessed for anxiety (STAI state and trait) and depression (BDI-II). Alexithymia (TAS 20) and emotional awareness (Emotional Self-Awareness Questionnaire; ESQ) were also evaluated. The severity of alcohol dependence was specified with Alcohol Dependence Severity scale. Results Alexithymia was significantly higher in the group of alcohol dependent participants (50.1 vs 45.1; p=0.049). Within the alcohol dependent group, ESQ was negatively correlated to the severity of alcohol dependence (r=-0.29; p=0.028) with subscales “social awareness” and “social skills” negatively correlated to the duration of abstinence (r=-0.37; p=0.03 and r=- 0.38; p=0.01, respectively). Conclusions Alexithymia is significantly higher in the alcohol dependent group, compared to the control group, but within this group, evaluation of emotional awareness may become more interesting to evaluate the characteristics of alcohol dependent subjects and predict their drinking outcomes
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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 | 0.000 | 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) | 0.004 | 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".