Egg consumption of children under two years of age through a child-owned poultry and nutrition intervention in rural Ethiopia: A community-based randomized controlled trial
Bibliographic record
Abstract
Egg consumption is associated with better child health and nutrition. Though a relatively cheap animal source food, under-two years aged Ethiopian children rarely eat egg. This study tested effects of a child-owned poultry intervention integrated with nutrition education on egg intake. Targeting 6–18 months children, the trial was conducted in Southern Ethiopia from May to November 2018. Clusters were randomly selected and allocated to intervention and control arms. Children in the intervention group (N = 127) received two egg-laying local hens and caging materials in a cultural and religious gifting ceremony. Parents promised to not sell nor share the chickens and eggs, present two more hens, replace those that died, and feed all eggs produced to the chicken-owner child on the basis of one-egg-a-day. Cage utilization, proper poultry husbandry and environmental sanitation, as well as egg feeding, were promoted. Controls (N = 126) received the regular community-based nutrition and agriculture education. Child-owned poultry increased significantly in intervention (p < 0.001) (β = 3.856; 95% CI of 3.553–4.159). Egg intake was 72% vs 20.7% among intervention and control children, respectively, at end line (p < 0.001; Odds ratio = 3.841; 95% CI = 2.640–5.589). Mean eggs [SD] consumed by the week before end line significantly increased in intervention (4.85 [2.41]) compared to control (0.4 [0.61]) (p < 0.001; β = 2.202; 95% CI = 1.971–2.433) children. Child-owned hen flock size was strongly associated with egg intake (r = 0.975; p < 0.001). Nearly one-third of children in intervention met the minimum dietary diversity (p = 0.016; Odds ratio = 1.857; 95% CI = 1.120–3.078). Vitamin-A rich fruits and vegetables consumption (p = 0.027) increased in intervention, as a result of sales of excess eggs. Enabling children to be owners of chickens together with nutrition education significantly increased chicken production and egg consumption. We recommend integration of child-owned poultry into ongoing malnutrition prevention activities particularly in resource-poor settings where undernutrition is high and animal source food intake is low. (Trial registration = NCT03355222).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".