Planning, design and implementation of the enhancing child nutrition through animal source food management (ENAM) project
Bibliographic record
Abstract
The Global-Livestock Collaborative Research Support Program’s (GL-CRSP) Child Nutrition Project, a controlled feeding trial in rural Kenya, demonstrated the importance of Animal Source Foods (ASF) for children’s micronutrient status and cognitive development. These findings prompted research efforts to understand the constraints to ASF in children’s diets in Africa so as to design targeted interventions to improve the ASF quality of children’s diets. The Enhancing Child Nutrition through Animal Source Management (ENAM) project (2004-2009) emanated from participatory formative research that identified six principal constraints to the inclusion of Animal Source Foods (ASF) in children’s diets in Ghana, including low income of caregivers, poor producer-consumer linkages, inadequate nutrition knowledge and skills of extension staff and caregivers, cultural beliefs, and inequitable household food distribution. To address these constraints, the ENAM project undertook a multidisciplinary community development, research and capacity building initiative with the goal of augmenting caregivers’ access to and use of ASF in children’s diets. Participatory processes were used to implement an integrated microcredit, entrepreneurship and nutrition education intervention with 181 caregivers of children 2- to5-years old in six rural communities across three agro-ecological zones (Guinea Savannah, Forest-Savannah Transitional and Coastal Savannah) of Ghana. Six matched communities from the same ecological zones served as comparison sites. Quantitative methods that included surveys, child anthropometry, and dietary assessment as well as qualitative case studies were used to assess the effect of the intervention on household, caregiver and child outcomes of interest. This paper presents the key features of the planning, design and implementation of the community intervention and the research processes undertaken to assess the project’s impacts. The ENAM project model presents a unique approach for addressing caregivers’ income and knowledge barriers to improve child nutrition in rural Ghana and may be a promising intervention model for scale-up in Ghana and other African countries.
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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 | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".