Food Environment in Burkina Faso: Review of Public Policies and Government Actions Using the Food-EPI Tool
Bibliographic record
Abstract
BACKGROUND: Governments have a central role to play in creating a food environment that will enable people to have and maintain healthy eating practices. OBJECTIVES: This study analyzes public policies and government actions related to creating healthy food environments in Burkina Faso. METHODS: The Healthy Food Environment Policy Index tool used for this study has 2 components, 13 domains, and 56 indicators of good practice adapted to the Burkina Faso context. Official policy documents collected from data sources such as government and nongovernment websites, and through interviews with government and nongovernment resource persons, provided evidence of considerations of food environment in public policy documents in Burkina Faso. RESULTS: Policies documents show a lack of revision of old texts and administrative processes for new policies and government practices are very slow. Added to this is the absence of a regulatory document for some implemented actions. The analysis of the documents collected in relation to the indicators of Food-EPI tool shows that there is no evidence of consideration of food environments for the indicators concerning the regulation of nutrition and health claims, labeling, taxes on healthy and unhealthy foods, support systems for training for private structures on healthy diets, implementation of food guidelines, and food trade and investment. CONCLUSION: This study permits a review of public policies that take into account food environments through the various indicators and constitutes a starting point from which improvements can be made by the government. PLAIN LANGUAGE TITLE: Overview of Nutrition Policies, Taking Into Account All the Dimensions That Can Influence People's Food Choices Across Government, the Food Industry and Society.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".