Experiences and lessons learned for planning and supply of micronutrient powders interventions
Bibliographic record
Abstract
Realistic planning for a nutrition intervention is a critical component of implementation, yet effective approaches have been poorly documented. Under the auspices of "The Micronutrient Powders Consultation: Lessons Learned for Operational Guidance," 3 working groups were formed to summarize experiences and lessons across countries regarding micronutrient powders (MNP) interventions for young children. This paper focuses on programmatic experiences in the planning stages of an MNP intervention, encompassing assessment, enabling environment and adaptation, as well as considerations for supply. Methods included a review of published and grey literature, key informant interviews, and deliberations throughout the consultation process. We found that assessments helped justify adopting an MNP intervention, but these assessments were often limited by their narrow scope and inadequate data. Establishing coordinating bodies and integrating MNP into existing policies and programmes have helped foster an enabling environment and support programme stability. Formative research and pilots have been used to adapt MNP interventions to specific contexts, but they have been insufficient to inform scale-up. In terms of supply, most countries have opted to procure MNP through international suppliers, but this still requires understanding and navigating the local regulatory environment at the earliest stages of an intervention. Overall, these findings indicate that although some key planning and supply activities are generally undertaken, improvements are needed to plan for effective scale-up. Much still needs to be learned on MNP planning, and we propose a set of research questions that require further investigation.
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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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".