Corner Store Inventories, Purchases, and Strategies for Intervention: A Review of the Literature.
Bibliographic record
Abstract
INTRODUCTION: An increasingly popular strategy to improving the food retail environment and promoting healthy eating in low-income and minority communities is the corner store conversion. This approach involves partnering with small 'corner' food stores to expand access to high-quality fruits, vegetables, and other healthy foods. METHODS: We conducted a structured review of the literature to assess inventories and sales in corner stores, as well as to identify intervention strategies employed by corner store conversions. RESULTS: Our review returned eight descriptive studies that discussed corner store inventories and sales, as well as ten intervention studies discussing six unique corner store conversion interventions in the United States, the Marshall Islands, and Canada. Common intervention strategies included: 1) partnering with an existing store, 2) stocking healthy foods, and 3) social marketing and nutrition education. We summarize each strategy and review the effectiveness of overall corner store conversions at changing peoples' food purchasing, preparation, and consumption behaviors. CONCLUSIONS: Consumption of fresh, healthy, affordable foods could be improved by supporting existing retailers to expand their selection of healthy foods and promoting healthy eating at the neighborhood level. Additional corner store conversions should be conducted to determine the effectiveness and importance of specific intervention strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".