An integrated nutrition‐sensitive health and agriculture intervention to increase egg consumption among infants and young children in Upper Manya Krobo, Ghana
Bibliographic record
Abstract
Poor dietary quality and nutritional status among young children is common in rural low‐income households in Upper Manya Krobo District (UMKD), Ghana. Consumption of animal source foods can improve diet and growth outcomes. The Nutrition Links Project is a 5‐y capacity‐building project to improve nutrition and well‐being of vulnerable populations in UMKD. The project includes a small cluster randomized controlled trial that provides home gardening, health, nutrition education, and poultry entrepreneurial activities for caregivers who had a 0‐to 12‐mo‐old infant and were living in the intervention communities at enrollment (IN; n=102). Caregivers in the control (CT) communities (n=228) receive only the standard‐of‐care for nutrition, health, and agricultural extension provided by government staff. This on‐going study seeks to improve the diets and growth outcomes of young children, partly through increased egg consumption. For this analysis, longitudinal data comparing children's egg consumption during the previous day, were collected from caregivers at baseline and at the first follow‐up [IN (n=102); CT (n=228)], approximately 7 months after the intervention was implemented. At baseline, when infants were 8.7±4.1 mo old, few consumed eggs [IN (15.5%) and CT (20.8%)]. By the first follow‐up, there was a statistically significant difference in the percent of children consuming eggs (IN (28.4%) vs. CT (52.6%); P<0.001). The lower intake of eggs among IN children occurred despite the fact that IN caregivers had an increased access to eggs from the poultry enterprise. The negative effect during the early months of the intervention may reflect caregivers placing greater emphasis on egg sales for income and success of their poultry enterprise rather than home consumption. In‐depth evaluation is needed to better understand caregivers’ behaviours and to determine the project effect on the total diet. Nutrition education activities continue to encourage use of eggs as well as diverse nutrient‐rich foods in UMKD children's diets to ensure the nutrition‐sensitive agriculture intervention achieves the expected objective of improving diets and nutritional status of project participants. Support or Funding Information Funded by the Department of Foreign Affairs, Trade, and Development (DFATD) of the Government of Canada
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".