Diabetes and obesity prevention: changing the food environment in low-income settings
Bibliographic record
Abstract
Innovative approaches are needed to impact obesity and other diet-related chronic diseases, including interventions at the environmental and policy levels. Such interventions are promising due to their wide reach. This article reports on 10 multilevel community trials that the present authors either led (n = 8) or played a substantial role in developing (n = 2) in low-income minority settings in the United States and other countries that test interventions to improve the food environment, support policy, and reduce the risk for developing obesity and other diet-related chronic diseases. All studies examined change from pre- to postintervention and included a comparison group. The results show the trials had consistent positive effects on consumer psychosocial factors, food purchasing, food preparation, and diet, and, in some instances, obesity. Recently, a multilevel, multicomponent intervention was implemented in the city of Baltimore that promises to impact obesity in children, and, potentially, diabetes and related chronic diseases among adults. Based on the results of these trials, this article offers a series of recommendations to contribute to the prevention of chronic disease in Mexico. Further work is needed to disseminate, expand, and sustain these initiatives at the city, state, and federal levels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".