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Record W4408432648 · doi:10.1016/j.jand.2025.03.004

The Role of Registered Dietitian Nutritionists within Food Is Medicine: Current and Future Opportunities

2025· article· en· W4408432648 on OpenAlexaff
Eliza Short, Lisa Akers, Emily A. Callahan, Cara Cliburn Allen, Mayra Crespo‐Bellido, Kirsten Deuman, Emily Dimond, Miguel Ángel López, Elizabeth Anderson Steeves

Bibliographic record

VenueJournal of the Academy of Nutrition and Dietetics · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsNutrition International
Fundersnot available
KeywordsMedicineFamily medicineGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

Food Is Medicine (FIM) programs are rapidly expanding as a solution to address the health impacts of food insecurity, experienced by 12.8% of US households.1 FIM encompasses a spectrum of food-based nutrition programs that are typically integrated into the health care system to support disease management, prevention, or optimal health by connecting participants to nutritious foods.2 FIM initiatives include program models such as medically tailored meals (MTMs) or groceries (MTGs) and produce prescriptions (PPRs).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.409
Teacher spread0.306 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2025
Admission routes1
Has abstractyes

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