The magnitude and pattern of purchased ready‐to‐eat foods in the diets of rural Ghanaian children
Bibliographic record
Abstract
Purchase of ready‐to‐eat foods (RTEF) for children is common in urban Ghana but little is known about this practice in rural areas. We collected data on the purchase of RTEF in the past week by caregivers (N=530) of preschoolers living in 6 rural communities in 3 regions of Ghana. About 82% (N=433) of caregivers purchased RTEF in the past week. RTEF were purchased less frequently in northern (2.3 ± 0.1 times/wk) than in forest and coastal communities (3.0 ± 0.1 and 3.1 ± 0.2 times/wk, respectively; p<0.0001). RTEF was purchased most frequently for children in forest communities (56.9 ± 3.3%) and least frequently in northern communities (34.1 ± 3.4%; p<0.05). At least 60% of RTEF purchased for children were obtained in the morning. RTEF for children in forest communities was more likely to contain animal foods than in northern communities (34% vs 16% of the time, respectively; p<0.05). There were regional differences in the time of day when ASF‐based RTEF were given to children (p<0.05). The magnitude and patterns of purchased RTEF have implications for nutrition interventions in rural communities. This was supported through the 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".