Comorbidity of alcohol dependence and abnormal eating behaviours in women in early and middle adulthood
Bibliographic record
Abstract
Introduction:The problem of comorbidity of disorders appears quite often in scientific publications.However, there is an area especially interesting for us: abnormal eating behaviour in alcohol-dependent women treated with the therapeutic community method. Material and methods:The aim of research was to verify the determinants of the propensity to develop eating disorders in women treated for alcohol dependence.The Eating Behaviour Questionnaire, the Cope Inventory and the Social Competence Profile were used for diagnosis.The study was conducted in a group of 60 women, 30 of whom were women with alcohol dependence and 30 healthy volunteers. StreszczenieWprowadzenie: Problematyka współwystępowania zaburzeń pojawia się dość często w publikacjach naukowych.Obszar szczególnie nas interesujący to nieprawidłowe zachowania jedzeniowe u kobiet uzależnionych od alkoholu.Materiał i metody: Celem badań własnych było zweryfikowanie uwarunkowań powodujących skłonność do nieprawidłowych zachowań jedzeniowych u kobiet pozostających w terapii z powodu uzależnienia od alkoholu.Do diagnozy wykorzystano Kwestionariusz Zachowań Związanych z Jedzeniem (KZZJ), Inwentarz COPE -Inwentarz do Pomiaru Radzenia Sobie ze Stresem oraz Profil Kompetencji Społecznych.Badania realizowano w grupie 60 kobiet, z czego 30 stanowiły kobiety leczące się z powodu uzależnienia od alkoholu, a 30 -zdrowe ochotniczki.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".