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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».