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Record W2060442525 · doi:10.1002/eat.22019

An examination of early childhood perfectionism across anorexia nervosa subtypes

2012· article· en· W2060442525 on OpenAlexaff
Katherine A. Halmi, Dara Bellace, Samantha Berthod, Samiran Ghosh, Wade H. Berrettini, Harry Brandt, Cynthia M. Bulik, Steve Crawford, Manfred M. Fichter, Craig L. Johnson, Allan S. Kaplan, Walter H. Kaye, Laura M. Thornton, Janet Treasure, D. Blake Woodside, Michael Strober

Bibliographic record

VenueInternational Journal of Eating Disorders · 2012
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsToronto General HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Human Genome Research InstituteNational Institute of Mental Health
KeywordsPerfectionism (psychology)PsychologyAnorexia nervosaEating disordersClinical psychologyBinge-eating disorderPsychiatryBulimia nervosa

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine childhood perfectionism in anorexia nervosa (AN) restricting (RAN), purging (PAN), and binge eating with or without purging (BAN) subtypes. METHOD: The EATATE, a retrospective assessment of childhood perfectionism, and the eating disorder inventory (EDI-2) were administered to 728 AN participants. RESULTS: EATATE responses revealed general childhood perfectionism, 22.3% of 333 with RAN, 29.2% of 220 with PAN, and 24.8% of 116 with BAN; school work perfectionism, 31.2% with RAN, 30.4% with PAN, and 24.8% with BAN; childhood order and symmetry, 18.7% with RAN, 21.7% with PAN, and 17.8% with BAN; and global childhood rigidity, 42.6% with RAN, 48.3% with PAN and 48.1% with BAN. Perfectionism preceded the onset of AN in all subtypes. Significant associations between EDI-2 drive for thinness and body dissatisfaction were present with four EATATE subscales. DISCUSSION: Global childhood rigidity was the predominate feature that preceded all AN subtypes. This may be a risk factor for AN.

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 score0.114
Threshold uncertainty score0.555

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 communication0.0000.001
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 head0.014
GPT teacher head0.332
Teacher spread0.318 · 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

Citations45
Published2012
Admission routes1
Has abstractyes

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