From evidence to national scale: An implementation framework for micronutrient powders in Rwanda
Bibliographic record
Abstract
Micronutrient powders (MNP) are an efficacious intervention in terms of reducing anaemia among young children, yet challenges remain regarding implementation at scale. Research that can guide effective implementation of nutrition interventions and facilitate integration into existing health care platforms is needed. This paper seeks to advance the implementation science knowledge base by presenting our multiphased strategy and findings for scaling-up MNP in Rwanda. The multiphased implementation strategy, spanning a 5-year period (2011-2016), included (a) a feasibility study involving formative research, (b) a 30-day trial of improved practices (n = 60 households), (c) a 12-month pilot that included an effectiveness study (n = 1,066 caregiver/child pairs), and (d) a staggered approach to national scale-up. At the end of Phase 4, the programme had been implemented in 19 of Rwanda's 30 districts with the scale-up in the final 11 districts completed in the following year. The caregivers of over 270,000 eligible children 6-23 months of age received a box of 30 MNP sachets in the final 3-month assessment period, representing a coverage rate of 87%. Initial problems with the supply chain and distribution and ongoing challenges to monitoring and reporting have been the largest obstacles. Continued success will be dependent on adequate resources for capacity development, refresher training, and responsive monitoring. Rwanda is one of the first countries to successfully scale-up home fortification subnationally with MNP. Lessons learned have implications for other countries.
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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 | 0.676 | 0.510 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.043 | 0.047 |
| Open science | 0.024 | 0.064 |
| Research integrity | 0.023 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 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".