Purchased ready‐to‐eat foods are positively associated with children's animal source food intakes in rural Ghana
Bibliographic record
Abstract
Purchased ready‐to‐eat (RTE) foods or street foods are a common part of children's diets in many countries. These foods may contribute to improving children's diet quality. We assessed the relationship between consumption of RTE foods and animal source food (ASF) intake by 2‐ to 5‐y‐old children in rural Ghana. Interviewers collected data on purchase of RTE foods in the past week (N=454). Children's total ASF intakes also were recorded. Among caregivers who had purchased RTE foods (N=370) in the past week, 36 % (N=133) of them had obtained the foods specifically for their 2‐ to 5‐y‐old child. There were no significant differences in sociodemographic characteristics of caregivers who purchased RTE foods for children and those who did not with respect to years of education completed, weekly income, household wealth rank, marital status, and occupation. Children who consumed RTE foods were significantly more likely to have eaten livestock meats, organ meats, chicken, and eggs in the past week compared to children who did not consume any RTE foods (P<0.05). Mean ASF diversity score was significantly higher for children who had consumed RTE foods in the last week compared to children whose diets did not include RTE foods (5.3 ± 2.2 vs. 4.7 ± 2.3; P<0.02). Purchased RTE foods may be an important source of ASF in young children's diets in rural Ghana. GL‐CRSP funded in part by USAID, Grant # PCE‐G‐00‐98‐00036‐00.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".