Tackling poverty through private sector microcredit programs in Ghana: Does infant and young child nutrition improve?
Bibliographic record
Abstract
Low income is a barrier to optimal feeding practices of infants and young children (IYC). Microcredit programs for rural Ghanaian women aim to improve incomes and may have the additional benefit of improving IYC nutrition. A cross‐sectional study was conducted in the Upper Manya Krobo District in Ghana to determine the association between a mother's participation in a rural bank microcredit program and IYC dietary quality and nutritional status. Participants included 102 active microcredit member mothers and 102 non‐microcredit member mothers and their youngest child (6–23 mo). Non‐members were matched to the microcredit group by community and child age. Information was collected on IYC feeding practices, length and weight and household socio‐demographic characteristics. IYC in the microcredit group (MC) consumed more dairy products ( p < 0.01) and iron‐fortified foods ( p < 0.05) while IYC in the non‐microcredit group (NMC) consumed more legumes and nuts ( p < 0.05). Overall, more MC than NMC children (57 % vs. 43 %; p < 0.05) met minimum dietary diversity recommendations (≥ 4 food groups). Weight‐for‐age z‐scores tended to be higher among MC vs. NMC children (−0.43 ± 1.13 vs. −0.69 ± 1.03; p = 0.09). Microcredit programs may contribute to improving IYC nutrition in Ghana; longitudinal studies are needed to understand the role that this component of the private sector plays. Funding: IDRC #1045519‐017 & McGill U.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".