Emotional Eating, Alexithymia, and Binge‐Eating Disorder in Obese Women
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationships between alexithymia and emotional eating in obese women with or without Binge Eating Disorder (BED). RESEARCH METHODS AND PROCEDURES: One hundred sixty-nine obese women completed self-report questionnaires, including the Beck Depression Inventory, the State Trait Anxiety Inventory, the Stress Perceived Scale, the Dutch Eating Behaviour Questionnaire, and the Toronto Alexithymia Scale. The presence of BED, screened using the Questionnaire of Eating and Weight Patterns, was confirmed by interview. RESULTS: Forty obese women were identified as having BED. BED subjects and non-BED subjects were comparable in age, body mass index, educational level, and socioeconomic class. According to the Dutch Eating Behaviour Questionnaire, BED subjects exhibited higher depression, anxiety, perceived stress, alexithymia scores, and emotional and external eating scores than non-BED subjects. Emotional eating and perceived stress emerged as significant predictors of BED. The relationships between alexithymia and emotional eating in obese subjects differed between the two groups according to the presence of BED. Alexithymia was the predictor of emotional eating in BED subjects, whereas perceived stress and depression were the predictors in non-BED subjects. DISCUSSION: This study pointed out different relationships among mood, alexithymia, and emotional eating in obese subjects with or without BED. Alexithymia was linked to emotional eating in BED. These data suggest the involvement of alexithymia in eating disorders among obese women.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".