Effect of enhanced nutrition services with community‐based nutrition services on the diet quality of young children in Ethiopia
Bibliographic record
Abstract
Poor diet quality related to inadequate complementary feeding is a major public health problem in low and middle-income countries including Ethiopia. Low dietary diversity has been linked to negative health outcomes in children. To provide a package of interventions to close nutritional gaps through agriculture, the Sustainable Undernutrition Reduction in Ethiopia (SURE) programme was set up as a multi-sectoral initiative and the results of combined effects of community-based and enhanced nutrition services, compared to community-based alone, on diet diversity and diet quality of complementary feeding of young children are presented. The study used pre- and post-intervention design. Baseline (n = 4980) data were collected from May to July 2016, and follow-up (n = 2419) data from December 2020 to January 2021. From 51 intervention districts having the SURE programme, 36 intervention districts were randomly selected for baseline and 31 for the follow-up survey. The primary outcome was diet quality: minimum dietary diversity (MDD), minimum meal frequency (MMF) and minimum acceptable diet (MAD). Comparing endline to baseline over the 4.5-year intervention, the use of standard community-based nutrition services of growth monitoring and promotion increased (16%-46%), as did enhanced nutrition services of infant and young child feeding counselling, and agricultural advising (62%-77%). Women involved in home gardening significantly increased (73%-93%); however, household production of food decreased yet consumption of most own-grown foods increased. Importantly, MAD and MDD increased four-fold. The SURE intervention programme was associated with improvements in complementary feeding and diet quality through enhanced nutrition services. This suggests programmes targeted at nutrition-sensitive practices can improve child feeding in young children.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".