Healthy food marketing and purchases of fruits and vegetables in large grocery stores
Bibliographic record
Abstract
Healthy food marketing in the retail environment can be an important driver of fruit and vegetable purchases. In Los Angeles County, the Nutrition Education and Obesity Prevention (NEOP) program utilized this strategy to promote healthy eating among low-income families that shop at large retail chain stores. The present study assessed whether self-reported exposure to large retail NEOP interventions, including seeing at least one store visual, watching an in-store cooking demonstration, and/or seeing at least one program advertisement, were associated with increased fruit and vegetable purchases. During fall 2014, the Division of Chronic Disease and Injury Prevention in the Los Angeles County Department of Public Health partnered with Samuels Center to conduct store patron intercept surveys at six large food retail stores participating in NEOP across Los Angeles County. Of 1050 participants who completed the survey, almost a quarter (25.0%) reported seeing at least one visual throughout the store and 9.2% watched a cooking demonstration. Seeing at least one visual and watching a cooking demonstration were not significantly associated with percent dollars spent on fruits and vegetables each week. Among participants who reported being exposed to at least one store visual, those enrolled in the Supplemental Nutrition Assistance Program (SNAP) reported spending 6% more on fruits and vegetables than those who were not enrolled (p = 0.046). Although the NEOP store interventions did not individually increase store purchases, their educational value may still influence patron food selection, especially if coupled to the monetary resources of SNAP for those who are enrolled.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".