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Record W4388930510 · doi:10.1016/j.jneb.2023.10.015

State Agency Perspectives on Successes and Challenges of Administering the Child and Adult Care Food Program

2023· article· en· W4388930510 on OpenAlexvenueno aff
Tatiana Andreyeva, Melissa McCann, Judy Prager, Erica L. Kenney

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesRobert Wood Johnson Foundation
KeywordsOutreachAgency (philosophy)Child careFood and drug administrationState (computer science)Adult careNursingBusinessMedicinePolitical scienceEnvironmental healthGerontologyYoung adult

Abstract

fetched live from OpenAlex

The federal Child and Adult Care Food Program (CACFP) improves nutrition and reduces food insecurity for young children while helping cover food costs for care providers and families. Despite its important benefits, the program is underutilized. This report uses qualitative interviews with state CACFP administrators representing 28 states to explore federal and state policies and practices that support or discourage CACFP participation among licensed child care centers. We report on successful approaches to program outreach and administration, barriers that make CACFP participation challenging, and recommendations to expand access to CACFP for eligible child care providers and the populations they serve.

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)0.045
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.056
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.006
Scholarly communication0.0090.003
Open science0.0010.005
Research integrity0.0020.004
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 head0.148
GPT teacher head0.471
Teacher spread0.323 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nutrition Education and Behavior→Same topicFood Security and Health in Diverse Populations→French-language works237,207→