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Record W2125414209 · doi:10.1002/eat.20561

Developmental pathways of eating problems in adolescents

2008· article· en· W2125414209 on OpenAlexaff
Annie Aimé, Wendy Craig, Debra Pepler, Depeng Jiang, Jennifer Connolly

Bibliographic record

VenueInternational Journal of Eating Disorders · 2008
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsYork UniversityQueen's UniversityUniversité du Québec en Outaouais
Fundersnot available
KeywordsPsychologyPsychopathologyEating disordersDevelopmental psychologyDisordered eatingClinical psychologyDepression (economics)Binge eatingDepressive symptomsPsychiatryAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the developmental eating trajectories of adolescents and identify psychological correlates and risk factors associated with those trajectories. METHOD: Seven hundred thirty-nine adolescents completed self-reported measures of eating problems, internalizing and externalizing behaviors, alcohol and drug use, peer victimization, and depression. RESULTS: Five eating trajectories were obtained. The proportions of males and females were the same in the increasing eating problems trajectory. For both genders, internalizing and externalizing problems were identified as associated risk factors of an eating pathology and reporting at least some eating problems was associated with an increased likelihood of psychological problems. Other risk factors found only in boys were frequency of drug use, victimization, and depressive symptoms. CONCLUSION: Externalizing problems in girls and internalizing behaviors in boys with disordered eating should not be overlooked. Atypical eating behaviors in boys are of particular concern since it increases their risk of cooccurring psychopathology.

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)0.002
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.298
Teacher spread0.266 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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