Medically Tailored Grocery Delivery for Food Pantry Clients with Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of a home-delivered, medically tailored grocery intervention on glycemic control and diet quality among participants with type 2 diabetes (T2D) experiencing food insecurity. METHODS: A single-arm prepost study was conducted. One hundred one English, Spanish, or Marshallese-speaking adults were recruited from food pantries in Northwest Arkansas (from August 2021 to February 2023). Twelve weekly T2D-appropriate food boxes with diabetes self-management education and support materials were home-delivered. Primary outcomes measured at preintervention and postintervention included hemoglobin A1c and diet quality (i.e., Healthy Eating Index-2015). RESULTS: Mixed-effects regressions controlling for age, sex, race/ethnicity, household size, education, and employment found hemoglobin A1c scores significantly decreased by 0.56% (units) at postintervention compared with preintervention (P = 0.01). No significant changes in Healthy Eating Index-2015 scores were found (P = 0.47). CONCLUSIONS AND IMPLICATIONS: Future research may build on this study's findings and explore mechanisms whereby medically tailored groceries can benefit people across communities experiencing high rates of food insecurity and T2D.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".