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Record W2140703017 · doi:10.1177/0272431602022001004

Risk and Protective Factors Associated with Disordered Eating During Early Adolescence

2002· article· en· W2140703017 on OpenAlexaff
Gail McVey, Debra Pepler, Ron Davis, Gordon L. Flett, Mohamed Abdolell

Bibliographic record

VenueThe Journal of Early Adolescence · 2002
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsYork UniversityLakehead UniversityHospital for Sick Children
Fundersnot available
KeywordsDisordered eatingPsychologyPerfectionism (psychology)Competence (human resources)Developmental psychologyHuman physical appearanceSocial supportClinical psychologySocial competenceEating disordersSocial psychologySocial change

Abstract

fetched live from OpenAlex

Risk and protective factors associated with disordered eating were examined among 363 girls (X age =12.9 years) in middle-level school. The variables included self-report ratings of competence and of the importance of physical appearance and social acceptance by peers, self-oriented and socially prescribed perfectionism, negative events, and parental support. In a multivariate regression analysis, low competence in physical appearance, high importance of social acceptance, high self-oriented perfectionism, and low paternal support were correlated significantly with reports of high levels of disordered eating. The negative influence of low physical appearance competence on disordered eating was attenuated for those girls who placed low, as compared with high, levels of importance on physical appearance. Paternal support was found to have a protective function in regard to disordered eating for those girls who experienced high, as compared with low, levels of school-related negative events. Implications for school-based prevention strategies are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations134
Published2002
Admission routes1
Has abstractyes

Explore more

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