Psychometric properties of the eating disorders inventory (EDI-1) in a nonclinical chinese population in Hong Kong
Bibliographic record
Abstract
Objectives: To evaluate the psychometric properties of the Chinese Eating Disorders Inventory (EDI-1) in a nonclinical population in Hong Kong. Method: 1,172 (females 606, males 566) Chinese undergraduates completed the Chinese EDI-1; 105 of them also completed the 12-item General Health Questionnaire (GHQ-12). Results: In female subjects, the Chinese EDI- 1 and its subscales met conventional standards of internal consistency, item- total, item-subscale, and subscale correlations, and exhibited an excellent degree of factorial integrity. The subscales discriminated among male, female, high Drive for Thinness, high Body Dissatisfaction, constitutionally slim, and Canadian female subjects. Female GHQ-12 cases and noncases were only distinguished by the Interpersonal Distrust, Interoceptive Awareness, and Ineffectiveness subscales. 3.3% of female subjects could be characterized as being pathologically weight preoccupied. Discussion: This study provides preliminary evidence that the Chinese EDI-1 is an economical, reliable, and potentially useful self-report instrument for investigating the psychological and behavioral dimensions of eating disorders in Hong Kong. But further work is needed to evaluate its transcultural validity in clinical and less modernized Chinese populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".