Health systems research in the time of health system reform in India: a review
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.154 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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".