Setting priorities in child health research in India for 2016-2025: a CHNRI exercise undertaken by the Indian Council for Medical Research and INCLEN Trust
Bibliographic record
Abstract
BACKGROUND: Millennium Development Goal 4 (MDGs) mobilised countries to reduce child mortality by two thirds the 1990 rate in 2015. While India did not reach MDG 4, it considerably reduced child mortality in the MDG-era. Efficient and targeted interventions and adequate monitoring are necessary to further progress in improvements to child health. Looking forward to the Sustainable Development Goal (SDG)-era, the Indian Council of Medical Research and The INCLEN Trust International conducted a national research priority setting exercise for maternal, child, newborn health, and maternal and child nutrition. Here, results are reported for child health. METHODS: The Child Health and Nutrition Research Initiative (CHNRI) method for research priority setting was employed. Research ideas were crowd-sourced from a network of child health experts from across India; these were refined and consolidated into research options (ROs) which were scored against five weighted criteria to arrive weighted Research Priority Scores (wRPS). National and regional priority lists were prepared. RESULTS: 90 experts contributed 596 ideas that were consolidated into 101 research options (ROs). These were scored by 233 experts nationwide. National wRPS for ROs ranged between 0.92 and 0.51. The majority of the top research priorities related to development of cost-effective interventions and their implementation, and impact evaluations, improving data quality; and monitoring of existing programs, or improving the management of morbidities. The research priorities varied between regions, the Economic Action Group and North-Eastern states prioritised questions relating to delivering interventions at community- or household-level, whereas the North-Eastern states and Union Territories prioritised research questions involving managing and measuring malaria, and the Southern and Western states prioritised research questions involving pharmacovigilance of vaccines, impact of newly introduced vaccines, and delivery of vaccines to hard-to-reach populations. CONCLUSIONS: Research priorities varied geographically, according the stage of development of the area and mostly pertained to implementation sciences, which was expected given diversity in epidemiological profiles. Priority setting should help guide investment decisions by national and international agencies, therefore encouraging researchers to focus on priority areas. The ICMR has launched a grants programme for implementation research on maternal and child health to pursue research priorities identified by this exercise.
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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 | 0.027 | 0.001 |
| 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 | 0.002 |
| 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".