Evaluation and prioritisation of actions on food environments to address the double burden of malnutrition in Senegal: perspectives from a national expert panel
Bibliographic record
Abstract
OBJECTIVE: To evaluate the extent of implementation of public policies aimed at creating healthy eating environments in Senegal compared to international best practice and identity priority actions to address the double burden of malnutrition. DESIGN: The Healthy Food Environment Policy Index (Food-EPI) was used by a local expert panel to assess the level of implementation of forty-three good practice policy and infrastructure support indicators against international best practices using a Likert scale and identify priority actions to address the double burden of malnutrition in Senegal. SETTING: Senegal, West Africa. PARTICIPANTS: =16) participated in the study. RESULTS: Implementation of most indicators aimed at creating healthy eating environments were rated as 'low' compared to best practice (31 on 43, or 72 %). The Gwet AC2 inter-rater reliability was good at 0·75 (95 % CI 0·70, 0·80). In a prioritisation workshop, experts identified forty-five actions, prioritising ten as relatively most feasible and important and relatively most effective to reduce the double burden of malnutrition in Senegal (e.g. develop and implement regional school menus based on local products (expand to fourteen regions) and measure the extent of the promotion of unhealthy foods to children). CONCLUSIONS: Significant efforts remain to be made by Senegal to improve food environments. This project allowed to establish an agenda of priority actions for the government to transform food environments in Senegal to tackle the double burden 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 | 0.180 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".