Prevalence and social determinants of breastfeeding practices in urban slums and urban non-slum areas in India: A comparative analysis
Bibliographic record
Abstract
Understanding how the prevalence and determinants of breastfeeding practices differ between urban slum areas, where living conditions are stringent, and urban non-slum areas, which have relatively more resources, is key to context-specific interventions. We conducted a comparative analysis to investigate the prevalence of breastfeeding practices in the urban slum and urban non-slum areas of India. We also used the socio-ecological framework to assess the individual, community, and policy-level correlates of breastfeeding practices. Secondary analysis of data from the National Family and Health Survey (2015-2016) in India was conducted to estimate the prevalence of early breastfeeding initiation and exclusive breastfeeding of children living in urban slum and urban non-slum areas, and the prevalence estimates were further stratified by the seven states where slums were sampled. Multilevel logistic regression analysis was used to examine the correlates of breastfeeding practices. Early breastfeeding initiation was significantly higher in the urban slum areas (50.4%) compared to urban non-slum areas (37.4%). In contrast, exclusive breastfeeding was lower in urban slums (50.1%) than in urban non-slum areas (55.8%). At the individual level in urban slum areas, preceding birth of more than 24 months was associated with early initiation of breastfeeding. At the policy level, child delivery at the health facilities was associated with early initiation of breastfeeding in the urban non-slum areas. The study showed that breastfeeding practices need to be urgently addressed in both the urban slum and urban non-slum areas, and policy-level factors such as health care facilities should be considered in designing effective 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".