Inadequate feeding of infant and young children in India: lack of nutritional information or food affordability?
Bibliographic record
Abstract
OBJECTIVE: Despite a rapidly growing economy and rising income levels in India, improvements in child malnutrition have lagged. Data from the most recent National Family Health Survey reveal that the infant and young child feeding (IYCF) practices recommended by the WHO and the Indian Government, including the timely introduction of solid food, are not being followed by a majority of mothers in India. It is puzzling that even among rich households children are not being fed adequately. The present study analyses the socioeconomic factors that contribute to this phenomenon, including the role of nutritional information. DESIGN: IYCF practices from the latest National Family Health Survey (2005-2006) were analysed. Multivariate logistic regression analyses were performed to establish the determinants of poor feeding practices. The indicators recommended by the WHO were used to assess the IYCF practices. SETTING: India. SUBJECTS: Children (n 9241) aged 6-18 months. RESULTS: Wealth was shown to have only a small effect on feeding practices. For children aged 6-8 months, the mother's wealth status was not found to be a significant determinant of sound feeding practices. Strikingly, nutritional advice on infant feeding practices provided by health professionals (including anganwadi workers) was strongly correlated with improved practices across all age groups. Exposure to the media was also found to be a significant determinant. CONCLUSIONS: Providing appropriate information may be a crucial determinant of sound feeding practices. Efforts to eradicate malnutrition should include the broader goals of improving knowledge related to childhood nutrition and IYCF practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".