Caregivers’ nutrition knowledge and attitudes are associated with household food diversity and children’s animal source food intake across different agro-ecological zones in Ghana
Bibliographic record
Abstract
Caregivers' nutrition knowledge and attitudes may influence the variety of foods available in the household and the quality of children's diets. To test the link, this study collected data on caregivers' (n 608) nutrition knowledge and feeding attitudes as well as the diets of their household and of their 2-5-year-old children in twelve rural communities nested in the three main agro-ecological zones of Ghana. Household foods and children's animal source foods (ASF) consumed in the past 7 d were categorised into one of fourteen and ten groups, respectively. About 28 % of caregivers believed that their children needed to be fed only 2-3 times/d. Reasons for having adult supervision during child meal times, feeding diverse foods, prioritising a child to receive ASF and the perceived child benefits of ASF differed across zones (P<0·001). Households with caregivers belonging to the highest tertile of nutrition knowledge and attitude scores consumed more diverse diets compared with those of caregivers in the lowest tertile group (11·2 (sd 2·2) v. 10·0 (sd 2·4); P<0·001). After controlling for the effect of agro-ecological zone, caregivers' nutrition knowledge and feeding attitudes positively predicted household dietary diversity and the frequency and diversity of children's ASF intakes (P<0·001). The number of years of formal education of caregivers also positively predicted household dietary diversity and children's ASF diversity (P<0·001). A key component to improving child nutrition is to understand the context-specific nutrition knowledge and feeding attitudes in order to identify relevant interventions.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".