Health systems research in the time of health system reform in India: a review
Notice bibliographique
Résumé
BACKGROUND: Research on health systems is an important contributor to improving health system performance. Importantly, research on program and policy implementation can also create a culture of public accountability. In the last decade, significant health system reforms have been implemented in India. These include strengthening the public sector health system through the National Rural Health Mission (NRHM), and expansion of government-sponsored insurance schemes for the poor. This paper provides a situation analysis of health systems research during the reform period. METHODS: We reviewed 9,477 publications between 2005 and 2013 in two online databases, PubMed and IndMED. Articles were classified according to the WHO classification of health systems building blocks. RESULTS: Our findings indicate the number of publications on health systems progressively increased every year from 92 in 2006 to 314 in 2012. The majority of papers were on service delivery (40%), with fewer on information (16%), medical technology and vaccines (15%), human resources (11%), governance (5%), and financing (8%). Around 70% of articles were lead by an author based in India, the majority by authors located in only four states. Several states, particularly in eastern and northeastern India, did not have a single paper published by a lead author located in a local institution. Moreover, many of these states were not the subject of a single published paper. Further, a few select institutions produced the bulk of research. Of the foreign author lead papers, 77% came from five countries (USA, UK, Canada, Australia, and Switzerland). CONCLUSIONS: The growth of published research during the reform period in India is a positive development. However, bulk of this research is produced in a few states and by a few select institutions Further strengthening health systems research requires attention to neglected health systems domains like human resources, financing, and governance. Importantly, research capacity needs to be strengthened in states and institutions that have a scarcity of researchers, as well as states that have been the focus of little research. While more funding for health systems research is required, this funding needs to be targeted at deficient health systems domains, geographical areas, and institutions.
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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 | 0,154 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,000 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,005 |
| 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 ».