Advancing the use of evidence in bouillon fortification policy discussions: Burkina Faso, Nigeria, and Senegal
Bibliographic record
Abstract
Policy changes require stakeholder buy-in, therefore, the timely delivery of tailored evidence provided to all stakeholders involved in policy discussions is important. This paper reports the evidence generation and delivery processes undertaken in Burkina Faso, Nigeria, and Senegal in support of bouillon fortification discussions. We identified stakeholder-specific evidence needs, tapped existing data and new data to generate that evidence, and packaged and delivered it to stakeholders. Evidence needs included the levels of micronutrient inadequacy (with/without existing and other hypothetical fortification programs), the potential contributions of bouillon fortification to reduce micronutrient inadequacy and (for some micronutrients) child mortality, the cost and cost-effectiveness of bouillon fortification programs, and the contribution of bouillon to total sodium intake. Evidence on technical and commercial issues was also required. Stakeholder-specific understanding and ownership of evidence was essential; achieving both required continual interaction and trust. New bouillon evidence delivery channels were developed in each country and linked to existing decision-making bodies. A shared vocabulary and understanding of issues and evidence, and the continual innovative redelivery of evidence, were critical to success; persistence and innovations in evidence delivery paid dividends. Concrete policy changes regarding bouillon fortification were secured in Nigeria; policy discussions continue in Burkina Faso and Senegal.
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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 | 0.124 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".