Caregivers’ Income Generation Activities and Diversity of Animal Source Foods in Children’s Diets in Ghana
Bibliographic record
Abstract
Engagement in income generation activities (IGA) that involve animal source foods (ASF) may influence children’s intake of animal products through increased availability of ASF in the home and increased income that can be used to purchase ASF. This study compared the diversity of ASF in the diet of children whose caregivers’ engaged in ASF‐related IGA and those engaged in an IGA unrelated to ASF. Data on household income and sociodemographics, and diets of 2‐ to 5‐y‐old children were collected through interviews with 251 caregivers in 6 rural and semi‐rural communities in 3 regions of Ghana. About 34% (n= 83) of caregivers were engaged in an ASF‐related IGA. Rural caregivers with ASF‐related IGA had a significantly higher weekly income than those in ASF‐unrelated IGA (p<0.05); however, a similar difference was not noted among semi‐rural caregivers. Rural children tended to have higher dietary ASF diversity than semi‐rural children (P=0.05). Children’s ASF diversity was not predicted by type of IGA. Weekly income of at least US$10.90, caregiver formal education, and rural location predicted children’s ASF diversity (P<0.001). Ability to purchase ASF in the market rather than its accessibility in the home through an IGA may be a more important determinant of ASF in children’s diets in rural and semi‐rural communities in Ghana. Support was through GL‐CRSP, funded in part by US‐AID, Grant # PCE‐G‐00‐98‐00036‐00 and J Ellis fellowship to Christian.
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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 | 0.000 | 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 | 0.001 | 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".