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Critical evaluation of two approaches to achieve universal health coverage in India

2018· article· en· W2884570843 on OpenAlexaboutno aff
Shyamkumar Sriram

Bibliographic record

VenueInternational Journal of Community Medicine and Public Health · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyHealth carePopulationEconomic growthBusinessQuarter (Canadian coin)MedicineActuarial scienceEnvironmental healthEconomicsGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.417
GPT teacher head0.423
Teacher spread0.006 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2018
Admission routes1
Has abstractyes

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