Baltimore Healthy Eating Zones program significantly improves food preparation methods among adult caregivers of low‐income African American youth
Bibliographic record
Abstract
The Baltimore Healthy Eating Zones (BHEZ) study, conducted in low income Baltimore City corner stores and recreation centers, was a youth‐targeted multi‐level environmental intervention trial to promote healthy food consumption. The trial included increased availability of healthy foods and interactive health communication strategies. Impact was assessed on a sample of 83 of the 242 youth and their primary caregivers. Pre‐ and post‐intervention, caregivers were asked about their preparation methods for chicken, pork, ground beef, eggs, and greens. Scores were based on the degree to which the preparation method added or reduced fat content of the cooked food. Deep frying or pan frying was given a score of −1; baked, grilled, or steamed were given +1; pan‐fried, drained then rinsed was given +2. The change in pre‐ and post‐intervention score was calculated. The control group averaged a change in score of +1.76 ±1.86 and the intervention group averaged +2.31 ±2.02. Once this calculated score was stratified to < 2 or > 2, caregivers in the intervention group were found to significantly decrease the use of fat in cooking as compared to those in the control group (Pearson Chi‐Square, p‐value= 0.033). This preliminary finding suggests that BHEZ was effective in promoting healthier cooking methods among caregivers; thereby potentially improving the healthiness of foods provided to youths at home. Grant Funding Source : The Robert Wood Johnson Foundation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".