Eating Disorder Inventory-3, validation in Swedish patients with eating disorders, psychiatric outpatients and a normal control sample
Bibliographic record
Abstract
BACKGROUND: The Eating Disorder Inventory-3 (EDI-3) is designed to assess eating disorder psychopathology and the associated psychological symptoms. The instrument has been revised and has not yet been validated for Swedish conditions in its current form. AIMS: The aim of this study was to investigate the validity and reliability of this inventory and present national norms for Swedish females. METHODS: Data from patients with eating disorders (n = 292), psychiatric outpatients (n = 140) and normal controls (n = 648), all females, were used to study the internal consistency, the discriminative ability, and the sensitivity and specificity of the inventory using preliminary cut-offs for each subscale and diagnosis separately. Swedish norms were compared with those from Denmark, USA, Canada, Europe and Australian samples. RESULTS: The reliability was acceptable for all subscales except Asceticism among normal controls. Analysis of variance showed that the EDI-3 discriminates significantly between eating disorders and normal controls. Anorexia nervosa was significantly discriminated from bulimia nervosa and eating disorder not otherwise specified on the Eating Disorder Risk Scales. Swedish patients scored significantly lower than patients from other countries on the majority of the subscales. Drive for Thinness is the second best predictor for an eating disorder. The best predictor for anorexia nervosa was Interoceptive Deficits and Bulimia for the other diagnoses. Conclusions/clinical implications: The EDI-3 is valid for use with Swedish patients as a clinical assessment tool for the treatment planning and evaluation of patients with eating-related problems. However, it still exist some uncertainty regarding its use as a screening tool.
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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 | 0.003 |
| 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 | 0.000 |
| 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".