Perspective: Food Environment Research Priorities for Africa—Lessons from the Africa Food Environment Research Network
Bibliographic record
Abstract
Over the last 2 decades, many African countries have undergone dietary and nutrition transitions fueled by globalization, rapid urbanization, and development. These changes have altered African food environments and, subsequently, dietary behaviors, including food acquisition and consumption. Dietary patterns associated with the nutrition transition have contributed to Africa's complex burden of malnutrition-obesity and other diet-related noncommunicable diseases (DR-NCDs)-along with persistent food insecurity and undernutrition. Available evidence links unhealthy or obesogenic food environments (including those that market and offer energy-dense, nutrient-poor foods and beverages) with suboptimal diets and associated adverse health outcomes. Elsewhere, governments have responded with policies to improve food environments. However, in Africa, the necessary research and policy action have received insufficient attention. Contextual evidence to motivate, enable, and create supportive food environments in Africa for better population health is urgently needed. In November 2020, the Measurement, Evaluation, Accountability, and Leadership Support for Noncommunicable Diseases Prevention Project (MEALS4NCDs) convened the first Africa Food Environment Research Network Meeting (FERN2020). This 3-d virtual meeting brought researchers from around the world to deliberate on future directions and research priorities related to improving food environments and nutrition across the African continent. The stakeholders shared experiences, best practices, challenges, and opportunities for improving the healthfulness of food environments and related policies in low- and middle-income countries. In this article, we summarize the proceedings and research priorities identified in the meeting to advance the food environment research agenda in Africa, and thus contribute to the promotion of healthier food environments to prevent DR-NCDs, and other forms of malnutrition.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".