Nutrition-Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities: A Systematic Review of the Literature, 2002–2013
Bibliographic record
Abstract
INTRODUCTION: Residents of rural communities in the United States are at higher risk for obesity than their urban and suburban counterparts. Policy and environmental-change strategies supporting healthier dietary intake can prevent obesity and promote health equity. Evidence in support of these strategies is based largely on urban and suburban studies; little is known about use of these strategies in rural communities. The purpose of this review was to synthesize available evidence on the adaptation, implementation, and effectiveness of policy and environmental obesity-prevention strategies in rural settings. METHODS: The review was guided by a list of Centers for Disease Control and Prevention Recommended Community Strategies and Measurements to Prevent Obesity in the United States, commonly known as the "COCOMO" strategies. We searched PubMed, Cumulative Index of Nursing and Allied Health Literature, Public Affairs Information Service, and Cochrane databases for articles published from 2002 through 2013 that reported findings from research on nutrition-related policy and environmental strategies in rural communities in the United States and Canada. Two researchers independently abstracted data from each article, and resolved discrepancies by consensus. RESULTS: Of the 663 articles retrieved, 33 met inclusion criteria. The interventions most commonly focused on increasing access to more nutritious foods and beverages or decreasing access to less nutritious options. Rural adaptations included accommodating distance to food sources, tailoring to local food cultures, and building community partnerships. CONCLUSIONS: Findings from this literature review provide guidance on adapting and implementing policy and environmental strategies in rural communities.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".