Exploring the Relationship Between Disordered Eating and Executive Function in a Non-Clinical Sample
Bibliographic record
Abstract
Previous research suggests that individuals diagnosed with eating disorders (ED) may experience executive functioning deficits that help maintain their ED. Although this relationship is reported consistently in clinical samples, it is important to consider whether it holds for individuals with sub-clinical ED symptoms. One hundred eighty-eight university students participated in the present study examining the relationship between executive function (EF) and disordered eating behaviors. Participants completed a demographics questionnaire, self-report questionnaires measuring atypical eating behaviors (EAT-26; EDI-3), and a self-report measure of EF (BRIEF-A). Correlational analyses demonstrated significant positive associations between ED behaviors and problems with emotional control, shifting, inhibition, and self-monitoring. Six hierarchical multiple regressions were conducted, using EF scores to predict scores on EAT-26 subscales (Dieting, Bulimia, Total ED Risk) and EDI-3 scales (Drive for Thinness, Bulimia, Risk Composite). In all regression analyses, BRIEF-A Emotional Control emerged as a significant predictor. As would be expected, EDI-3 Bulimia scores were also predicted by problems with inhibition. These results provide preliminary evidence of an association between non-clinical patterns of disordered eating and executive dysfunction, specifically including the ability to control one's emotions, suggesting that emotional control problems may help predict ED risk. Future research could examine how these factors predict the development of eating disorders.
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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 | 0.001 | 0.004 |
| 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".