Community Interventions for Dietary Improvement in Ghana
Bibliographic record
Abstract
UNLABELLED: Background. Low caregiver income and poor nutrition knowledge and skills are important barriers to achieving optimal child feeding in rural Ghana. OBJECTIVE: An integrated microcredit and nutrition education intervention was implemented to address these barriers. METHODS: Using a quasi-experimental design, 134 caregivers of children 2 to 5 years of age in six intervention communities were enrolled into self-selected savings and loan groups. They received small individual loans over four 16-week cycles to support their income-generating activities. Nutrition and entrepreneurial education was provided during weekly loan repayment meetings. Another 261 caregivers in six comparison communities did not receive the intervention. Data on household sociodemographic and economic characteristics, perception of income-generating activity profits, and children's consumption of animal-source foods in the previous week were collected at baseline and at four additional time points. Differences according to group (intervention vs. control) and time (baseline vs. endline) were analyzed with chi-square and Student's t-tests. RESULTS: The intervention and comparison groups did not differ by caregivers' age and formal education; few (35) had previous experience with microcredit loans. At endline, more intervention than comparison caregivers perceived that their business profits had increased (59% vs. 23%, p < .001). In contrast to comparison children, after 16 months of intervention children consumed more livestock meat (p =.001), organ meat (p = .04), eggs (p = .001), and milk and milk products (p < .0001) in the previous week in comparison with baseline. CONCLUSIONS: Integrated food-centered strategies can improve children's diets, which will enhance their nutritional status, health, and cognitive outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".