Improving Infant and Young Child Feeding in Ethiopia through Community Based Grain Banks Using Local Foods
Bibliographic record
Abstract
Background Inadequate infant and young child feeding practices contribute to the concerning malnutrition situation in Ethiopia. To improve availability, accessibility and quality of complementary foods, development partners in Ethiopia supported women's groups to produce complementary food by processing local grains and legumes at community grain banks. Flour was sold in the semi‐urban communities and a subsidized barter system was created in the rural areas. Objective To assess the acceptability, perceived impact, feasibility and sustainability for local grain bank interventions to improve infant and young child nutrition, in four regions of Ethiopia. Methods A total of 51 key informant interviews and 33 focus group discussions (n=237) were conducted based on a purposive sampling framework of project stakeholders. Results The grain bank flour was valued for its quality and diverse locally grown ingredients. Mothers were motivated to give the flour because of perceived benefits to their child's health; while the community grain bank was credited as labor saving for women. The grain bank flour offered improved dietary diversity; however, further micronutrient interventions including dietary modification or fortification are needed to improve diet adequacy. The greatest risks to sustainability were dependence on external resources to subsidize the barter model and reliance on volunteer work from women's groups in the rural context. Conclusions Integrated agricultural and health interventions leveraging local crops can appeal to diverse stakeholders and may offer an acceptable approach to improving infant and young child feeding. Funding: UNICEF & Micronutrient Initiative
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".