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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".