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Record W2090507361 · doi:10.1002/eat.20258

Do supplementary items on the eating disorder examination improve the assessment of adolescents with anorexia nervosa?

2006· article· en· W2090507361 on OpenAlexaff
Jennifer Couturier, James Lock

Bibliographic record

VenueInternational Journal of Eating Disorders · 2006
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsWestern University
Fundersnot available
KeywordsAnorexia nervosaPsychologyPsychopathologyInternal consistencyEating disordersClinical psychologyConsistency (knowledge bases)PsychometricsPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Given that adolescents with anorexia nervosa (AN) typically have lower scores on the Eating Disorder Examination (EDE) than expected, the current study examined whether the inclusion of eight supplementary items developed by the authors of the EDE better captured the symptoms of adolescents with AN. METHOD: A dataset consisting of EDEs from 86 adolescents was examined by 3 primary methods: (1) baseline subscale scores were compared before and after the addition of the supplementary items, (2) the internal consistency of the EDE with the addition of these items was examined, and (3) each of these items was compared before and after treatment. RESULTS: After the addition of the supplementary items, the Eating Concern and Weight Concern subscales were significantly increased, whereas the Restraint subscale was significantly decreased, and the Shape Concern subscale was unchanged. Internal consistency was improved on the Eating Concern, Weight Concern, and Shape Concern subscales, and was decreased on the Restraint subscale. Three of eight items showed a significant decrease with treatment. CONCLUSION: Although the addition of some of these eight supplementary items better captured the psychopathology of adolescents with AN, scores were still substantially below expected, indicating that the exploration of other methods of assessment is needed.

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)0.001
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 score0.016
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.313
Teacher spread0.302 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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