A Systematic Review of the Effect of Retail Food Environment Interventions on Diet and Health with a Focus on the Enabling Role of Public Policies
Bibliographic record
Abstract
PURPOSE OF REVIEW: Update the state of evidence on the effectiveness of retail food environment interventions in influencing diet and explore the underlying role of public policy, through a systematic review of population-level interventions to promote health in the retail food environment, including community and consumer environments. Diet-related outcomes included purchasing, dietary intakes, diet quality, and health including weight. We coded studies for enabling public policy levers underpinning the intervention, using two widely used conceptual frameworks. RECENT FINDINGS: Of 86 articles (1974-2018), the majority (58 articles, 67%) showed at least one positive effect on diet. Thirteen articles (15%) discussed natural experiments, 27 articles (31%) used a design involving comparison groups including 23 articles (27%) specifically describing randomized controlled trials, and 46 (53%) were quasi-experimental (cross-sectional) evaluations. Across the "4Ps" of marketing (product, promotion, placement, and price), promotion comprised the greatest proportion of intervention strategies, especially in earlier literature (pre-2008). Few studies combined geographic access interventions with 4P strategies, and few used robust dietary intake assessments. Behavior change communication remains an intervention mainstay, but recent work has also incorporated environmental and social planning, and fiscal strategies. More recent interventions were multi-component. The retail food environment intervention literature continues to grow and has become more robust overall, with clearer evidence of the effect of interventions on diet-related outcomes, including consumer purchasing, dietary intakes, and health. There is still much scope for development in the field. Attention to enabling public policy could help to strengthen intervention implementation and evaluation in the retail food environment.
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".