Effectiveness of an educational intervention to improve child feeding practices and growth in rural <scp>C</scp>hina: updated results at 18 months of age
Bibliographic record
Abstract
Inappropriate complementary feeding practices have led to, in part, significant disparities in growth and nutritional status between rural and urban children in China. A cluster-randomised, controlled trial was implemented in Laishui, China to assess the effectiveness of an educational intervention on caregivers' feeding practices and children's growth. Eight townships were randomly assigned to the intervention or control. Five hundred ninety-nine healthy infants were enrolled at 2-4 months old, and were followed up at ages 6, 9, 12, 15 and 18 months. The intervention group received information on enhanced home-prepared recipes and food preparation and hygiene through group training, counselling and home visit. Key outcomes were children's physical growth, caregivers' knowledge and behaviours on complementary feeding, and the infant and child feeding index (ICFI). Analysis was by intention to treat. The intervention group achieved better knowledge and practices related to complementary feeding, and significantly higher ICFI scores at each follow-up point. Children in the intervention group achieved higher z-scores for weight-for-age (WAZ) and weight-for-height (WHZ) than the control (0.18 vs. 0.01 and 0.49 vs. 0.19, respectively) at 18 months old, and were less likely to have stunted growth (odds ratio = 0.71, 95% confidence interval: 0.53-0.94). Mixed model showed that the intervention group achieved significantly better linear growth over time, including WAZ (P = 0.016), WHZ (P = 0.030) and HAZ (P = 0.078). These results indicated that an educational intervention delivered through local health services can enhance caregivers' knowledge and practices of complementary feeding and ultimately improve children's growth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".