Examining the Associations Between Overeating, Disinhibition, and Hunger in a Nonclinical Sample
Bibliographic record
Abstract
Background Binge eating (BE) has long been identified as a correlate of overweight and obesity. However, less empirical attention has been given to overeating with and without loss of control (LOC) in nonclinical samples. Purpose The goal of the present study was to examine the association of (1) established correlates of BE, namely, weight and shape concerns, dietary restraint, and negative affect, and (2) three additional correlates, disinhibition, hun-ger, and interoceptive awareness (IA), to overeating in a nonclinical sample of college women. Method Female students (n=1,447) aged 18 to 21 years recruited from colleges in three Canadian metropolitan areas completed self-report questionnaires in class to assess sociodemographic and anthropomorphic characteristics, overeating, LOC, dietary restraint, negative affect, weight and shape concerns, IA, disinhibition, and hunger. Results The established correlates of BE were significant correlates of all types of overeating and explained 33 % of the variance. Disinhibition was the most strongly associated correlate of overeating. Conclusions Findings suggest that established correlates of BE are associated with other types of overeating such as objective overeating (OOE), as are disinhibition and hunger.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".