The contribution of district prioritization on maternal and newborn health interventions coverage in rural India
Bibliographic record
Abstract
The contribution of district prioritization on maternal and newborn health interventions coverage in rural IndiaBackground In 2001, India prioritized eight most socioeconomically disadvantaged states known as Empowered Action Group (EAG) states and in 2013, it prioritized 190 of the 718 as high priority districts (HPDs) to accelerate the decline in maternal and newborn mortality.This paper assesses whether the HPDs achieved a greater coverage of maternal and newborn health interventions than the non-HPDs and HPDs in EAG states achieved greater coverage than those in non-EAG states. MethodsWe used data from the Sample Registration System to assess rural neonatal mortality trends in EAG states and all India.We computed a co-coverage index based on seven maternal and newborn health interventions from the 2015/16 National Family Health Survey.Difference in differences (DID) analyses were used to examine the contribution of district prioritization, considering the HPDs and the illiterate as treatment groups and 2013 as the time cut-off for the pre-and post-treatment. ResultsNeonatal mortality declined in rural India from 36 to 27 per 1000 live births during 2010-2016 at 4.5% per year.Four EAG states experienced faster rates of decline than the national rate.From 2013, the co-coverage index increased significantly more in the HPDs compared to non-HPDs (DID = 0.11, P ≤ 0.005).The district prioritization effect on co-coverage was statistically significant in only EAG states (DID = 0.13, P ≤ 0.05).The coverage gains for illiterate mothers were greater than for literate mothers, especially in the HPDs. ConclusionsThe district prioritization in India is associated with greater improvements in the coverage of maternal and newborn health services in EAG states and the HPDs, including reductions in inequalities within those states and districts.There are however still large gaps between states and districts and within districts by the mother' s literacy status that need further prioritization to make progress towards the SDG targets by 2030.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".