Complementary feeding messages that target cultural barriers enhance both the use of lipid‐based nutrient supplements and underlying feeding practices to improve infant diets in rural Zimbabwe
Bibliographic record
Abstract
Supplementation with lipid-based nutrient supplements (LiNS) is promoted as an approach to prevent child undernutrition and growth faltering. Previous LiNS studies have not tested the effects of improving the underlying diet prior to providing LiNS. Formative research was conducted in rural Zimbabwe to develop feeding messages to improve complementary feeding with and without LiNS. Two rounds of Trials of Improved Practices were conducted with mothers of infants aged 6-12 months to assess the feasibility of improving infant diets using (1) only locally available resources and (2) locally available resources plus 20 g of LiNS as Nutributter®/day. Common feeding problems were poor dietary diversity and low energy density. Popular improved practices were to process locally available foods so that infants could swallow them and add processed local foods to enrich porridges. Consumption of beans, fruits, green leafy vegetables, and peanut/seed butters increased after counselling (P < 0.05). Intakes of energy, protein, vitamin A, folate, calcium, iron and zinc from complementary foods increased significantly after counselling with or without the provision of Nutributter (P < 0.05). Intakes of fat, folate, iron, and zinc increased only (fat) or more so (folate, iron, and zinc) with the provision of Nutributter (P < 0.05). While provision of LiNS was crucial to ensure adequate intakes of iron and zinc, educational messages that were barrier-specific and delivered directly to mothers were crucial to improving the underlying diet.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".