Policy overview of the multisectoral nutrition planning process: The progress, challenges, and lessons learned from Burkina Faso
Bibliographic record
Abstract
INTRODUCTION: Malnutrition is a multifactorial problem, and multisectoral planning is an indispensable tool. The objective of this study was (a) to evaluate the extent to which nutrition is integrated into policies and (b) to describe the process used by the government of Burkina Faso to reform its policy frameworks and multisectoral nutrition planning. METHODS: This was a qualitative study, and data were collected in two key steps: first, through a policy overview conducted in 2015 and, second, in November 2017, through a document review and individual stakeholder interviews with 32 key actors involved in national nutrition planning. RESULTS: The extent to which nutrition is integrated into development policies varied from one sector to another. Since 2014, Burkina Faso has initiated nutrition planning through a multisectoral approach involving six sectors. This process was implemented in three key stages. Progress includes revision of national nutrition policy towards multisectoral perspective, formulation of a consensual and quality multisectoral nutrition strategic plan, creation of nutrition budget line, and establishment of nutrition technical secretariat. CONCLUSION: To improve the anchoring of multisectoral coordination bodies at the supra-ministerial level, mobilizing resources and promoting sector accountability are key next steps that would contribute to the success of the implementation.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".