Breastfeeding Practices and Dietary Diversity among Infants and Young Children in Rural and Urban-Slum Populations in India: An Observational Study
Bibliographic record
Abstract
Background: Nutritional exposures and growth in early life are linked to immediate and also to long term health outcomes. Objective: To assess infant and young child feeding (IYCF) practices using WHO-UNICEF defined indicators in rural and urban-slum populations in India. Methods: A community-based, cross-sectional study was conducted in mothers and infants up to age 24 months. Data on socio-demographics, birth history, feeding practices (WHO-UNICEF IYCF indicators), maternal weight, height, and infant’s weight, length, mid-arm, and head circumferences were collected. Results: Five hundred and two (252 rural and 250 urban-slum) mother-infant dyads were studied. Proportions of IYCF indicators in rural and urban-slum infants were: Early initiation of breastfeeding 71 and 64%; Exclusive breastfeeding under six months, 59 and 25%; Minimum acceptable diet 11 and 27% respectively. Consumption of animal-source food (other than dairy products) and vitamin-A rich fruits and vegetables was below 15%. Cesarean section [aOR, 95% CI: 2.94 (1.53, 5.65)], hospitalization of newborn [aOR, 95% CI: 6.21 (2.95, 13.16)], pre-lacteal feeding [aOR, 95% CI: 3.38 (1.77, 6.45)], needing help in breastfeeding [aOR, 95% CI: 2.15 (1.04, 4.17)], and male gender [aOR, 95% CI: 2.13 (1.15, 4.25); p<0.05 for all] were associated with delayed initiation of breastfeeding, whereas lower monthly household income [aOR, 95% CI: 2.62 (1.10, 6.25)], and younger age [aOR, 95% CI: 1.24 (1.11, 1.38); p<0.05 for both] were associated with poor dietary diversity. Conclusions: Education of optimum IYCF practices, targeting early initiation of breastfeeding, increasing meal frequency and intake of vitamin-A rich and animal-source foods need urgent attention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".