Critical evaluation of two approaches to achieve universal health coverage in India
Bibliographic record
Abstract
The WHO report on the path to Universal Health Coverage (UHC) emphasizes that every person should receive the necessary healthcare without enduring financial hardship at the time of getting care. United Nations’ Sustainable Development agenda incorporates one goal (Goal 3) that is related to health and well-being of the population and one of the specific targets of the goal is to improve financial risk protection through the achievement of universal health coverage. More than 100 countries in the world have either started their reforms towards UHC or have already achieved it and India is one of the countries trying to achieve UHC. Out of the 1.324 billion people in India, only 11% of the population has any form of health insurance coverage. Around, 42% of India’s population is Below Poverty Line (BPL). Rashtriya Swasthya Bima Yojana is a health insurance program started in 2007 that provides a wide range of healthcare services for BPL families. Rajiv Aarogyasri Community Health Insurance is a state health insurance program started in Andhra Pradesh as one of the first programs in India to provide health insurance to poor people. In India, 39 million people are being impoverished due to OOP health expenditures each year, and a quarter of these expenditures are contributed by hospitalization Out-of-pocket expenditures even after the financial protection provided by a number of health insurance programs. This review will critically evaluate the two health insurance approaches which aim to achieve UHC in India by providing health protection to the indigent.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".