S05-3INTERPERSONAL AND INTRAPERSONAL EMOTIONAL PROCESSES IN TREATED ALCOHOL-DEPENDENT PATIENTS AND NON-ADDICTED HEALTHY INDIVIDUALS
Bibliographic record
Abstract
Although the presence of various emotional processing deficits in alcohol-dependent (AD) individuals is largely confirmed, the specificity and relevance of them still warrant investigation. The current investigation aimed at comparing selected aspects of emotional processing (i.e., mental state recognition, alexithymia, and emotional intelligence) between treated AD subjects and non-addicted healthy individuals. The AD sample consisted of 92 abstinent AD men inpatients, participating in an 8-week abstinence-based treatment program in Warsaw, Poland. The healthy control (HC) group consisted of 86 men recruited from the Medical University of Warsaw and the Nowowiejski Hospital administrative staff. Information about demographics, severity of alcohol problems, and psychopathological symptoms was obtained. Reading the Mind in the Eyes Test was used to assess mental states recognition. Alexithymia was measured with Toronto Alexithymia Scale and the Schutte Self-Report Emotional Intelligence Test was used as a self-report measure of emotional intelligence. In the MANCOVA models accounted for potentially confounding variables (demographics, severity of depression, anxiety symptoms), ADs presented deficits in identification and description of their own emotional states, as well as lower emotion regulation skills when compared to HCs. No between-group differences were observed in self-reported recognition of other people’s emotions, social skills, and a behavioral measure of mental states recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".