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Cross-cultural validity of the eating disorder inventory: A study of Chinese patients with eating disorders in Hong Kong

2012· article· en· W6940451101 on OpenAlexaboutno aff

Bibliographic record

VenueThe HKU Scholars Hub (University of Hong Kong) · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAnorecticAnorexia nervosaEating disordersBulimia nervosaEating Disorder InventoryAnorexiaPsychometrics

Abstract

fetched live from OpenAlex

Objective: To evaluate the cross-cultural validity of the Chinese version of the Eating Disorder Inventory (EDI) in a clinical sample of patients with eating disorders in Hong Kong. Method: After comprehensive clinical assessment, a consecutive series (1990-1996) of Chinese patients with bulimia nervosa (N = 17) and broadly diagnosed anorexia nervosa (N = 26) completed the Chinese EDI. Results were compared with those of Chinese female undergraduates (N = 606) and Canadian patients with eating disorders. Results: The EDI profiles of bulimic and fat phobic anorectic patients were remarkably and modestly similar to those of their Canadian counterparts. The EDI meaningfully distinguished bulimic patients and fat phobic anorectic patients from local undergraduates, but exhibited deficient criterion- related validity in nonfat phobic anorectic patients. Discussion: The questionable validity of certain EDI subscales in nonfat phobic patients reflects the ethnospecific constructs upon which they are based, and weakens the efficacy of the EDI in screening for anorexia nervosa in Chinese populations. Apart from illustrating some of the conceptual and methodological issues that need to be tackled in the cross-cultural study of the eating disorders, this study furnishes empirical support for the syndromal homogeneity of bulimia nervosa, and the clinical grouping of anorexia nervosa into fat phobic and nonfat phobic subtypes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.220
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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