MétaCan
Menu
Back to cohort
Record W4283156206 · doi:10.5304/jafscd.2022.113.014

Food sovereignty, health, and produce prescription programs: A case study in two rural tribal communities

2022· article· en· W4283156206 on OpenAlexaboutno aff
Nadine Budd Nugent, Ronit Ridberg, Hollyanne E. Fricke, Carmen Byker Shanks, Sarah Stotz, Amber Jones Chung, Sonya Shin, Amy L. Yaroch, Melissa Akers, Roger Lowe, Carmen George, Kymie Thomas, Hilary K. Seligman

Bibliographic record

VenueJournal of Agriculture Food Systems and Community Development · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsFood sovereigntyMedical prescriptionIncentiveSovereigntyBusinessEconomic growthIntervention (counseling)NavajoPolitical scienceGeographyEnvironmental planningMedicineFood securityAgricultureNursingEconomicsPolitics

Abstract

fetched live from OpenAlex

Structural inequities contribute to food systems in which tribal communities in the U.S. are more likely to experience barriers to healthy food access, including financial barriers, lack of geographic proximity, or both. Food sovereignty movements improve food access by shifting power to local people to build food systems that support cultural, social, economic, and environmental needs. Finan­cial incentive programs, including produce pre­scription programs, have emerged as a promising intervention to improve food access and support food sovereignty. This case study describes the implementation of two federally funded produce prescription programs (Produce Prescription Pro­jects or PPR) under the U.S. Department of Agri­culture (USDA) Gus Schumacher Nutrition Incen­tive Program (GusNIP) in two rural tribal communities: the Yukon Kuskokwim Delta region in Alaska, and the Navajo Nation, which spans parts of New Mexico, Arizona, and Utah. We illus­trate how PPR can be tailored to accommodate local and diverse cultures, strengthen community power, and be uniquely suited for the challenges of increasing access to nutritious food in rural tribal communities. We also highlight recommendations and future areas of research that may be useful for other rural tribal communities implementing PPR.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.173
GPT teacher head0.398
Teacher spread0.225 · 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 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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agriculture Food Systems and Community DevelopmentSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207