India Strive to Provide Universal Health Coverage
Bibliographic record
Abstract
<p style="text-align: justify;"><strong>Background:</strong> Universal health coverage is the reflection of the political commitment of countries towards health. This paper reviews the functioning and progress of India compared to World towards universal health coverage and identifies the bottlenecks. <strong>Methods:</strong> We searched the following electronic databases: PUBMED, BMJ, LANCET, WHO Website, Unicef Website and Google Scholar for studies related to universal health coverage. All databases were searched form inception. In addition, we checked reference lists of reviews and retrieved articles for additional studies. From the searches, we reviewed the title and abstract of each paper and retrieved potentially relevant references. <strong>Results:</strong> The poor universal health coverage was observed in Africa, Asia, and Middle East. Among south Asian countries, Bangladesh had 0.4%, India 5.7%, Nepal 0.1%, Pakistan 0%, and Sri Lanka 0.1% universal health coverage respectively. The countries like Canada, Tunisia, Egypt, Libya, Korea, Thailand, Japan, Turkey and Yemen enabled success in the path towards UHC. India have one of the highest proportions of household out-of pocket expenditures on health in the world, estimated at 71.1% in 2008–09. Any form of social or voluntary health insurance covers merely 10% of the Indian population. Community-based health insurance schemes for less than 1% of the population. <strong>Conclusion:</strong> Major challenges to achieve universal health coverage in developing countries are the limited resources. The only way to reduce dependence on direct payments is for governments to encourage payments made in advance of an illness. <strong>Key words:</strong> Universal, Health, Care, Scheme, Program, Coverage.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".