Towards the next generation of public health research in India: a call for a health equity lens
Bibliographic record
Abstract
BACKGROUND: Public health research is at a cross road in India. Despite a high level of health needs and new public health challenges arising in the context of rapid economic growth and social change, public health research is not keeping up with the needs of Indian society. There are, however, new initiatives creating opportunities to increase public health research, thereby raising debates about public health research priorities. OBJECTIVE: In this paper, the authors offer their own view on an agenda for the next generation of public health research in India. FINDINGS: The authors first outline the main reasons why they believe that public health research has been sidelined in India. Then, the authors argue that health equity should be the overarching principle guiding a public health research agenda. The authors suggest how to integrate equity-oriented strategies into the public health research agenda and propose some key research questions that require urgent attention from their respective disciplines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.213 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.011 | 0.069 |
| Scholarly communication | 0.047 | 0.043 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".