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Record W4415679720 · doi:10.1080/19320248.2025.2577183

Process Evaluation of an Intervention to Increase Staple Food Access in SNAP-Authorized Convenience Stores in Rural Appalachian Communities with Low Incomes

2025· article· en· W4415679720 on OpenAlexaff
Cori Sweet, Adeline Grier-Welch, Christopher Sneed, Karen Franck, Linda Bower, Janie Burney, Elizabeth Anderson Steeves

Bibliographic record

VenueJournal of Hunger & Environmental Nutrition · 2025
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsImpact
FundersRobert Wood Johnson Foundation
KeywordsIntervention (counseling)Food insecurityProcess (computing)Low incomeRural areaAppalachiaStaple foodHealthy food

Abstract

fetched live from OpenAlex

The Shop Smart Tennessee (SSTN) intervention was implemented in convenience stores in rural Appalachian communities with low incomes and aimed to increase access to and demand for staple foods/beverages. SSTN process evaluation metrics (reach, dose delivered, fidelity) assessed program implementation. This study found high-quality implementation for interventions related to increasing stock of healthier items and variable implementation for interventions related to increasing demand. Social media and text messaging interventions had the lowest quality implementation, offering opportunities for improvement in these areas. Key partnerships and regular review of process measures allowed researchers to enhance successful project delivery throughout the study.

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)0.014
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.453
Teacher spread0.381 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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