Impact of Baltimore Health Eating Zones Study on Psychosocial Factors among African American Caregivers
Bibliographic record
Abstract
Childhood obesity is a growing public health concern in the US. The Baltimore Healthy Eating Zones (BHEZ) study is a point‐of‐purchase intervention trial to improve access and consumption of healthy foods in low‐income neighborhoods. During 9‐month intervention trial in recreation centers (7 intervention, 7 control) and corner stores, we led youth‐led health education and nutrition promotion. In evaluation, 242 dyads of African‐American youths (ages 10–14) and their adult caregivers were surveyed at pre‐ and post‐ intervention. Three food‐related psychosocial variables were assessed: self‐efficacy (SE) (Cronbach's α=0.623); intentions (I) (α=0.706); knowledge (K) (α=0.282). Paired sample t‐ tests for pre‐ and post‐intervention data compared the impact between control and intervention groups, and showed no significant changes among adult caregivers in the intervention zone; SE (p=0.593), I (p=0.216) and K (p=0.671). Using independent samples t‐test, the changes in scores showed no significant difference between among adults in comparison areas; SE (p=0.237), I (p=0.300) and K (p=0.520). The preliminary findings showed no improvements in caregivers, which may be attributable to the intervention's primary focus of impacting youths and/or to the reliability of the scales. Future interventions should focus impact on caregivers because caregivers buy and prepare many of the foods that youth consume. 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".