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Record W4316037081 · doi:10.1016/j.jneb.2022.10.002

Using the Exploration, Preparation, Implementation, and Sustainment (EPIS) Framework to Advance the Science and Practice of Healthy Food Retail

2023· article· en· W4316037081 on OpenAlexvenueno aff
Bailey Houghtaling, Sarah Misyak, Elena Serrano, Rachael D. Dombrowski, Denise Holston, Chelsea R. Singleton, Samantha M. Harden

Bibliographic record

VenueJournal of Nutrition Education and Behavior · 2023
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersAgricultural Center, Louisiana State UniversityNational Institute of Food and AgricultureLouisiana State UniversityU.S. Department of Agriculture
KeywordsPerspective (graphical)BusinessHealthy foodClinical PracticeMarketingMedicineFood scienceArtVisual artsNursing

Abstract

fetched live from OpenAlex

Although healthy food retail strategies are widely used, there appears to be a limited understanding of the processes and determinants for successful adoption, implementation, and sustainment. To fill this gap, we recommend the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework to be used to advance the science and practice of healthy food retail. In this perspective, we: (1) introduce EPIS and describe why it was chosen as a recommended implementation science framework for healthy food retail, (2) highlight healthy food retail evidence supporting EPIS, and (3) discuss research and practice needs moving forward.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.085
GPT teacher head0.457
Teacher spread0.372 · 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 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
Published2023
Admission routes1
Has abstractyes

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