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Record W4228998464 · doi:10.3366/ajicl.2022.0409

Using the Right to Health to Advance Equitable Universal Health Coverage in Uganda's National Health Insurance Scheme Bill

2022· article· en· W4228998464 on OpenAlexaff
Lisa Forman, Diya Uberoi, Moses Mulumba

Bibliographic record

VenueAfrican Journal of International and Comparative Law · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsRight to healthEquity (law)National health insuranceHealth insuranceGovernment (linguistics)Health policyHealth equityHealth careEconomic growthBusinessPublic administrationPolitical scienceLawEnvironmental healthEconomicsMedicine

Abstract

fetched live from OpenAlex

In 2019, the Ugandan government issued the National Health Insurance Scheme (NHIS) Bill to promote more equitable access to health care, passing this Bill in March 2021. While the NHIS marks a significant step towards equitable reform of the health system, it also raised concerns about its impact on equity and the right to health. This article considers the extent to which the proposed 2019 Bill is in line with the right to health in international and Ugandan law, and offers recommendations to strengthen the foundation it sets for future and more equitable health policy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0080.006
Open science0.0010.009
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.076
GPT teacher head0.381
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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