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Record W4387617100 · doi:10.1186/s40337-023-00904-x

The social epidemiology of binge-eating disorder and behaviors in early adolescents

2023· article· en· W4387617100 on OpenAlexaff
Jason M. Nagata, Zacariah Smith-Russack, Angel Paul, Geomarie Ashley Saldana, Iris Yuefan Shao, Abubakr A A Al-Shoaibi, Anita V. Chaphekar, Amanda E. Downey, Jinbo He, Stuart B. Murray, Fiona C. Baker, Kyle T. Ganson

Bibliographic record

VenueJournal of Eating Disorders · 2023
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Toronto
FundersNational Institute on Drug AbuseNational Institute of General Medical SciencesNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteEuropean Regional Development FundPlan Nacional sobre DrogasNational Institutes of HealthDoris Duke Charitable FoundationMinisterio de Ciencia e InnovaciónInstitució Catalana de Recerca i Estudis Avançats
KeywordsBinge-eating disorderDemographyBinge eatingEpidemiologyOdds ratioBinge drinkingMedicineLogistic regressionPsychiatryCohortPsychologyEating disordersBulimia nervosaPoison controlInjury preventionEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Binge-eating disorder (BED) is the most common eating disorder phenotype and is linked to several negative health outcomes. Yet, little is known about the social epidemiology of BED, particularly in early adolescence. The objective of this study was to examine the associations between sociodemographic characteristics and BED and binge-eating behaviors in a large, national cohort of 10-14-year-old adolescents in the United States (U.S.) METHODS: We conducted a cross-sectional analysis of two-year follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study (2018 - 2020) that included 10,197 early adolescents (10 - 14 years, mean 12 years) in the U.S. Multivariable logistic regression models were used to assess the associations between sociodemographic characteristics and BED and binge-eating behaviors, defined based on the Kiddie Schedule for Affective Disorders and Schizophrenia. RESULTS: In this early adolescent sample (48.8% female, 54.0% White, 19.8% Latino/Hispanic, 16.1% Black, 5.4% Asian, 3.2% Native American, 1.5% Other), the prevalence of BED and binge-eating behaviors were 1.0% and 6.3%, respectively. Identifying as gay or bisexual (compared to heterosexual; adjusted odds ratio [AOR]: 2.25, 95% CI 1.01-5.01) and having a household income of less than $75,000 (AOR: 2.05, 95% CI: 1.21-3.46) were associated with greater odds of BED. Being male (AOR: 1.28, 95% CI: 1.06-1.55), of Native American (AOR: 1.60, 95% CI: 1.01-2.55) descent, having a household income less than $75,000 (AOR: 1.34, 95% CI: 1.08-1.65), or identifying as gay or bisexual (AOR for 'Yes' Response: 1.95, 95% CI: 1.31-2.91 and AOR for 'Maybe' Response: 1.81, 95% CI: 1.19-2.76) were all associated with higher odds of binge-eating behaviors. CONCLUSION: Several sociodemographic variables showed significant associations with binge-eating behaviors, which can inform targeted screening, prevention, and education campaigns for BED among early adolescents.

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)0.001
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 score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.033
GPT teacher head0.369
Teacher spread0.335 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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