Suggested health services research action to achieve reduction of neonatal mortality in India
Bibliographic record
Abstract
Despite several national programs to reduce infant mortality, India had repeatedly failed to achieve its set targets for infant mortality. There are approximately one million neonatal deaths in India each year which accounts for nearly two-thirds of the infant deaths in India. India’s current trajectories of neonatal and infant mortality rates make it unlikely that it will achieve its targets for infant mortality rate for 2015 set under the Millennium Development Goals. Since two-thirds of infant deaths in India are neonatal deaths, implementation of effective neonatal care strategies would be essential to reduce infant mortality considerably. The history of child health services in India suggests an inattention to qualitative parameters, hindering a reversal of its failures. We discuss a format of mixed-methods participatory research, integrated with routine district level household surveys (DLHS), as a model of health services research which would better delineate the problems encountered in delivering effective newborn care at the primary care level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".