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Record W2070591456 · doi:10.5195/hcs.2014.163

The Best Laid Plans: Access to the Rajiv Aarogyasri community health insurance scheme of Andhra Pradesh

2014· article· en· W2070591456 on OpenAlexfundno aff
Haripriya Narasimhan, Venkatesh Boddu, Priya Singh, Anuradha Katyal, Sofi Bergkvist, Mala Rao

Bibliographic record

VenueHealth Culture and Society · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
FundersDepartment for International DevelopmentWorld Bank GroupInternational Development Research CentreWellcome TrustRockefeller Foundation
KeywordsScheme (mathematics)Health careBusinessHealth insuranceSelf-insuranceState (computer science)Qualitative researchPublic healthNarrativeEconomic growthActuarial scienceMedicineNursingEconomicsSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

This paper is a qualitative assessment of a public health insurance scheme in the state of Andhra Pradesh, south India, called the Rajiv Aarogyasri Community Health Insurance Scheme (or Aarogyasri), using the case-study method. Focusing on inpatient hospital care and especially on surgical treatments leaves the scheme wanting in meeting the health care needs of and addressing the impoverishing health expenditure incurred by the poor, especially those living in rural areas. Though well-intentioned, people from vulnerable sections of society may find the scheme ultimately unhelpful for their needs. Through an in-depth qualitative approach, the paper highlights not just financial difficulties but also the non-financial barriers to accessing health care, despite the existence of a scheme such as Aarogyasri. Narrative evidence from poor households offers powerful insights into why even the most innovative state health insurance schemes may not achieve their goals and systemic corrections needed to address barriers to health care.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.318
Teacher spread0.255 · 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.

Study designNot applicable
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

Citations7
Published2014
Admission routes1
Has abstractyes

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