Evaluation of the implementation of governmental policies and actions to create healthy food environments in Burkina Faso
Bibliographic record
Abstract
OBJECTIVE: The creation of a healthy food environment is highly dependent on the policies that governments choose to implement. The objective of this study is to compare the level of implementation of current public policies aimed at creating healthy food environments in Burkina Faso with international good practice indicators. DESIGN: This evaluation was carried out using the Food-EPI tool. The tool has two components (policy and infrastructure support), thirteen domains and fifty-six good practice indicators adapted to the Burkina Faso context. SETTING: Burkina Faso. PARTICIPANTS: Expert evaluators divided into two groups: the group of independent experts from universities, NGO and civil society and the group of experts from various government sectors. RESULTS: Among the fifty-six indicators, it was assessed the level of implementation as 'high' for six indicators, 'medium' for twenty-four indicators, 'low' for twenty-two indicators and 'very low' for four indicators. High implementation level indicators include strong and visible political support, targets on exclusive breastfeeding and complementary feeding, strong and visible political support for actions to combat all forms of malnutrition, monitoring of exclusive breastfeeding and complementary feeding indicators, monitoring of promotion and growth surveillance programmes and coordination mechanism (national, state and local government). The indicators on menu labelling, reducing taxes on healthy foods, increasing taxes on unhealthy foods and dietary guidelines are the indicators with a 'very low' level of implementation in Burkina Faso. CONCLUSIONS: The general results showed that there is a clear need for further improvements in policy and infrastructure support to promote healthy food environments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".