An Interpersonal Circumplex/Five-Factor Model Analysis Of The Eating Disorders Inventory-3
Bibliographic record
Abstract
A combined interpersonal circumplex/five-factor model approach was used to investigate personality correlates of Eating Disorders Inventory-3 (EDI-3; Garner, 2004) scales for a non-clinical sample of 234 college women. EDI-3 non-symptom scales and composites had appreciable loadings in the two-dimensional interpersonal circumplex space, with angular locations ranging mainly from Cold (180°) to Submissive (270°). In the five-factor analyses, Neuroticism made significant positive contributions to all of the EDI-3 scales and composites; Conscientiousness made contributions (all negative, save one) to 11 of the 18 scales. The results affirm the centrality of negative affect (i.e., Neuroticism) in disordered eating, but highlight also the importance of assessing interpersonal deficits, which in previous studies have been associated both with the etiology of eating-related problems and increased risk of dropout from treatment. Finally, collapsing or “weighting” EDI-3 item scores may compromise unnecessarily the psychometric properties of the scales—particularly in non-clinical populations—and we recommend derivation of additional EDI-3 norms, based on unweighted item scores.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".