Exploring Alexithymia, Depression, and Binge Eating in Self-Reported Eating Disorders in Women
Bibliographic record
Abstract
TOPIC: Binge eating is often a way of life for many women even if the diagnostic criteria for the tentative DSM-IV-TR diagnosis of binge eating disorder is not met. METHODS: Binge eating was conceptualized as a problem in affect regulation. Affective indices of alexithymia and depression were measure with the Toronto Alexithymia Scale (TAS), the Alexithymia-Provoked Response Questionnaire (APQR), and the Beck Depression Inventory (BDI), respectively. This study was an exploratory study of 65 subjects, 35 of whom self-reported as eating disordered and 30 as non-eating disordered. FINDINGS: Of the eating-disordered subjects, 95% scored significantly on the Eating Habits Checklist as binge eaters, 18% as anorexic, and 23% as bulimic. Significant relationships were found between alexithymia and binge eating and depression. A stepwise logistic regression found that both alexithymia and depression discriminated between women with and without binge eating at .001 and .002, respectively. CONCLUSIONS: This study found that alexithymia was more highly correlated with binge eating than with either anorexia or bulimia. In addition, a significant history of trauma and health problems for those who reported as binge eaters was reported. Implications for practice are discussed.
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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 | 0.000 | 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".