Inequalities in Research on Food Environment Policies: An Evidence Map of Global Evidence from 2010-2020
Bibliographic record
Abstract
There has been increasing pressure to implement policies for promoting healthy food environments worldwide. We conducted an evidence map to critically explore the breadth and nature of primary research from 2010-2020 that evaluated the effectiveness, cost-effectiveness, development, and implementation of mandatory and voluntary food environment policies. Fourteen databases and 2 websites were searched for "real-world" evaluations of international, national, and state level policies promoting healthy food environments. We documented the policy and evaluation characteristics, including the World Cancer Research Fund International NOURISHING framework's policy categories and 10 equity characteristics using the PROGRESS-Plus framework. Data were synthesized using descriptive statistics and visuals. We screened 27,958 records, of which 482 were included. Although these covered 70 countries, 81% of publications focused on only 12 countries (United States, United Kingdom, Australia, Canada, Mexico, Brazil, Chile, France, Spain, Denmark, New Zealand, and South Africa). Studies from these countries employed more robust quantitative methods and included most of the evaluations of policy development, implementation, and cost-effectiveness. Few publications reported on Africa (n = 12), Central and South Asia (n = 5), and the Middle East (n = 6) regions. Few also assessed public-private partnerships (PPPs, n = 31, 6%) compared to voluntary approaches by the private sector (n = 96, 20%), the public sector (n = 90, 19%), and mandatory approaches (n = 288, 60%). Most evaluations of PPPs reported on the same 2 partnerships. Only 50% of publications assessing policy effectiveness compared outcomes between population groups stratified by an equity characteristic, and this proportion has decreased over time. There are striking inequities in the origin, scope, and design of these studies, suggesting that research capacity and funding lies in the hands of a few expert teams worldwide. The small number of studies on PPPs questions the evidence base underlying the international push for PPPs to promote health. Policy evaluations should consider impacts on equity more consistently. This study was registered at PROSPERO as CRD42020170963.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".