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Record W3202418590

Litigating the Right to Health: What Can We Learn from a Comparative Law and Health Care Systems Approach

2014· article· en· W3202418590 on OpenAlexaff
Colleen M. Flood, Aeyal Gross

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRight to healthHealth careHealth equityHealth policyHuman rightsSocial determinants of healthEquity (law)Political sciencePublic healthInternational healthPublic economicsBusinessLaw and economicsEconomic growthLawEconomicsMedicine
DOInot available

Abstract

fetched live from OpenAlex

This article presents research demonstrating that the right to health plays different roles in different types of health systems. In high-income countries with tax-funded health systems, we usually encounter a lack of an enforceable right to heath. In contrast, rights play a more significant role in social health insurance/managed competition systems (which are present in a mixture of high-income and middle-income countries). There is concern, for example in Colombia, that a high volume of rights litigation can challenge the very sustainability of a public health care system and distort resources away from those most in need. Finally, in middle-income countries with big gaps between a poor public health system and a rich private one, we are more likely to find an express constitutional right to health care (or one is inferred from, for example, the right to life). In some of these countries, constitutional rights were included as part of the transition to democracy and an attempt to address huge inequities within society. Here the scale of health inequities suggests that courts need to be bolder in their interpretation of health care rights. We conclude that in adjudicating health rights, courts should scrutinize decision-making through the lens of health equity and equality to better achieve the inherent values of health human rights.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
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 head0.036
GPT teacher head0.319
Teacher spread0.283 · 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 designQualitative
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

Citations1
Published2014
Admission routes1
Has abstractyes

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