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Record W4403877301 · doi:10.1111/cdev.14191

Experiences of discrimination and snacking behavior in Black and Latinx children

2024· article· en· W4403877301 on OpenAlexaff
Katherine B. Ehrlich, Julie M. Brisson, Elizabeth R. Wiggins, Sarah M. Lyle, Manuela L. Celia-Sanchez, Daisy J. Gallegos, Kharah M. Ross, Mary A. Gerend

Bibliographic record

VenueChild Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsAthabasca UniversityUniversity of Calgary
FundersNational Institute on Drug AbuseJacobs FoundationBrain and Behavior Research Foundation
KeywordsSnackingModerationPsychologyBody mass indexAssociation (psychology)Developmental psychologyPercentileSocial psychologyObesityMedicine

Abstract

fetched live from OpenAlex

Abstract Little is known about how discrimination contributes to health behaviors in childhood. We examined the association between children's exposure to discrimination and their snacking behavior in a sample of youth of color (N = 164, M age = 11.5 years, 49% female, 60% Black, 40% Hispanic/Latinx). We also explored whether children's body mass index (BMI) or sleepiness moderated the association between discrimination and calorie consumption. The significant link between discrimination and calorie consumption was moderated by children's BMI, such that discrimination was associated with calorie consumption for children with BMI percentiles above 79%. Children's sleepiness did not serve as an additional moderator. Efforts to promote health should consider children's broader socio-contextual experiences, including discrimination, as factors that may shape eating patterns.

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)0.001
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.354
Teacher spread0.321 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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