Region and harvest season determine child feeding frequency in Ghana
Bibliographic record
Abstract
Adequate feeding frequency (FF) helps to ensure young children obtain sufficient energy and nutrients for proper growth and development. In 2008, only 50% of breastfed and 22% of non‐breastfed Ghanaian children 6–23 mo consumed the minimum FF recommendations. No similar information exists for children 2–5 y. The objectives of this study were to determine what factors influence child FF in Ghana. Weighed food intake data of 76 children 2–5 y from 3 regions (2004–2009 ENAM study) were analyzed with ANCOVA to determine predictors of FF. Interviews with 40 caregivers of children < 5 y were conducted in two regions in 2011; grounded theory guided the generation of FF themes. FF was greater in the Central (p<0.05) and Brong Ahafo (p<0.001) regions compared to Upper East, during the post‐harvest compared to pre‐harvest season (p<0.05), and if a caregiver was unmarried compared to married (p=0.01). FF tended to be higher when a caregiver worked as an artisan compared to a farmer (p=0.06). Caregivers identified lack of time and money as main constraints, whereas caregivers with social networks and established working hours felt more confident in providing adequate FF. Strategies to increase child FF must acknowledge a caregiver's environmental context such as region of residence and season, as well as resource constraints, including time and food availability. Funding: Africa Initiative.
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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 | 0.000 | 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) | 0.003 | 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".