Benchmarking Policy Goals and Actions for Healthy Food Environments in Benin to Prevent Malnutrition
Bibliographic record
Abstract
Background and aim: Lifestyle and dietary shifts contribute to widespread or specific micronutrient deficiencies, leading to health issues such as obesity, hypertension, diabetes, cardiovascular diseases, and certain cancers. These problems are linked to unhealthy food environments, yet little is known about Benin policy responses. This study aims to assess how different aspects of the food environment are addressed in Benin's policy documents and their alignment with international best practices. Methods: The study analyzed intentions and actions to ensure a healthy food environment in Benin using various policy documents, including laws, decrees, sectoral policies, strategic and operational plans, regulations, directives, action plans, and program/project documents. It followed the Food-EPI tool (Healthy Food Environment Policy Index) of the INFORMAS network (International Network for Food and Obesity/NCDs, Research, Monitoring and Action Support) focusing on the “Policy and Infrastructure Support” components, with steps like contextual analysis, document collection, and evidence extraction. Results: Of the 98 documents collected, 61 were analyzed and classified into frameworks: 54.09% in the policy framework, 29.50% in the strategic framework, and 16.39% in the operational framework. While nine food environment domains were addressed to some extent, disparities with international best practices were noted, especially in food composition, labeling, pricing, governance, and funding/resources. Evidence gaps were identified in retail food sales, food trade and investments, and health integration in policies. Conclusions: The study reveals diverse approaches and gaps in Benin policies for healthy food environments. Despite progress in some areas, like leadership and monitoring, others, including food composition and governance, need more attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".