Marketing Sustainability Analysis of Stores Participating in a Healthier Retail Food Program
Bibliographic record
Abstract
OBJECTIVES: To examine how food retailers completing Shop Healthy NYC, a healthy food retail program, (1) changed availability, placement, and promotion of healthier food immediately after participation and (2) sustained changes 1-year postintervention. METHODS: From 2014 to 2017, stores in 2 high-poverty New York City neighborhoods participated in a low-intensity intervention focused on in-store advertising or a high-intensity intervention to meet 7 criteria related to availability, placement, and promotion of healthy items. Stores were assessed preintervention (Pre), 1-month postintervention (Post 1), and 12-16 months postintervention (Post 2). Analyses were restricted to stores that completed the intervention and were assessed at all time points (n = 64). Changes were compared across time points. RESULTS: Across stores participating in the low-intensity intervention, the ratio of unhealthy-to-healthy ads decreased from Pre to Post 1, and by Post 2 remained improved over baseline. Among stores participating in the high-intensity intervention, the median number of healthy criteria met increased from 3.5 to 6 from Pre to Post 1 and decreased to 5 at Post 2. CONCLUSIONS: Improvements in the marketing and availability, placement, and promotion of healthy products are feasible but may require reinforcement and additional support over time.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".