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Record W4409373933 · doi:10.1093/heapro/daaf015

Considerations for diverse, equitable, and inclusive school food programs in the USA and Canada

2025· article· en· W4409373933 on OpenAlexafffundabout
Preetama Badyal, Tina Moffat

Bibliographic record

VenueHealth Promotion International · 2025
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcMaster UniversityWestern University
FundersEmployment and Social Development CanadaGovernment of CanadaMcMaster UniversityU.S. Department of Agriculture
KeywordsGrey literatureEquity (law)Stigma (botany)Sociocultural evolutionHealth equityMedical educationPublic relationsPsychologyPolitical scienceMedicineMEDLINENursingPublic health

Abstract

fetched live from OpenAlex

School food programs have been shown to support the nutrition of children and their long-term health outcomes in tandem with reducing nutritional inequities experienced by low-income, food insecure, and racialized populations. Understanding the specific needs and outcomes of these equity-deserving groups is crucial when enhancing program implementation and participation in school food programs. A scoping review of equitable, diverse, and inclusive considerations for school food programs was conducted on Canadian and American peer-reviewed and grey literature. The search strategy identified 18 peer-reviewed publications and three grey literature reports that supported the creation of five themes to be explored for school food programs: universal access, food preparation and delivery, sociocultural food preferences, partner involvement, and equitable nutrition. Analysis revealed that while literature surrounding these themes is developing, they serve as a crucial starting point for further research and consideration of the enhancement of school food programs. These themes can support the delivery of a program that is accessible to all students, accommodates their individualized needs, and is free of stigma.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.248
GPT teacher head0.479
Teacher spread0.231 · 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.

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

Citations5
Published2025
Admission routes3
Has abstractyes

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