Reformulating and testing the perfectionism model of binge eating among undergraduate women: A short-term, three-wave longitudinal study.
Bibliographic record
Abstract
The perfectionism model of binge eating (PMOBE) is an integrative model explaining why perfectionism is related to binge eating. This study reformulates and tests the PMOBE, with a focus on addressing limitations observed in the perfectionism and binge-eating literature. In the reformulated PMOBE, concern over mistakes is seen as a destructive aspect of perfectionism contributing to a cycle of binge eating via 4 binge-eating maintenance variables: interpersonal discrepancies, low interpersonal esteem, depressive affect, and dietary restraint. This test of the reformulated PMOBE involved 200 undergraduate women studied using a 3-wave longitudinal design. As hypothesized, concern over mistakes appears to represent a vulnerability factor for binge eating. Bootstrapped tests of mediation suggested concern over mistakes contributes to binge eating through binge-eating maintenance variables, and results supported the incremental validity of the reformulated PMOBE beyond perfectionistic strivings and neuroticism. The reformulated PMOBE also predicted binge eating, but not binge drinking, supporting the specificity of this model. The reformulated PMOBE offers a framework for understanding how key contributors to binge eating work together to generate and to maintain binge eating.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".