A Tale of Two Bengals: A Comparative Analysis of 23 Indicators of Maternal, Newborn and Child Health
Bibliographic record
Abstract
Background: Bangladesh and the Indian State of West Bengal form a homogenous geographic and cultural region with distinct administrative and governance structures. Although social and economic indicators are generally better in West Bengal compared to Bangladesh, this article is the first to provide a detailed comparison of indicators of child mortality, maternal and child health, nutrition, and coverage of key interventions in the two regions. Methodology/Findings: Using data from the 2005–2006 National Family Health Survey for West Bengal and the Bangladesh Demographic and Health Survey (2007), we examined coverage, wealth and Hindu/Muslim inequalities in set of maternal, newborn and child health indicators. Performance across all indicators was broadly similar with comparatively better performance in West Bengal on coverage of antenatal care, postnatal care, and family planning while Bangladesh had higher rates of immunization coverage, vitamin A supplementation, and use of oral rehydration therapy. The nutritional status of children and rates of child mortality in the two Bengals were very similar. Access to improved sanitation was slightly higher in West Bengal with large inequalities by wealth observed in both regions. Rich–poor and educational based inequalities in stunting, wasting and underweight were higher in West Bengal. Conclusion: Our findings indicate relatively similar performance across a range of maternal and child health indicators in Bangladesh compared to West Bengal despite lower levels of income and levels of literacy in Bangladesh. The two regions share similarities in topography, demography, and culture. Further studies on programme implementation in Bangladesh may lead to the development of a replicable model for resource poor countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".