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Record W2898835450 · doi:10.1136/bmjgh-2018-001145

Global health security and universal health coverage: from a marriage of convenience to a strategic, effective partnership

2019· article· en· W2898835450 on OpenAlexaff
Clare Wenham, Rebecca Katz, Charles Birungi, Lisa Boden, Mark Eccleston-Turner, Lawrence O. Gostin, Renzo Guinto, Mark Hellowell, Kristine Husøy Onarheim, Joshua Hutton, Anuj Kapilashrami, Emily Mendenhall, Alexandra Phelan, Marlee Tichenor, Devi Sridhar

Bibliographic record

VenueBMJ Global Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsCentre for Global Health Research
FundersEconomic and Social Research CouncilWellcome Trust
KeywordsConvergence (economics)General partnershipGlobal healthArgument (complex analysis)Health securityProcess (computing)Divergence (linguistics)Health equityPublic healthPublic relationsPolitical scienceHuman rightsBusinessComputer scienceEconomic growthEconomicsMedicineHealth careLaw

Abstract

fetched live from OpenAlex

Global health security and universal health coverage have been frequently considered as "two sides of the same coin". Yet, greater analysis is required as to whether and where these two ideals converge, and what important differences exist. A consequence of ignoring their individual characteristics is to distort global and local health priorities in an effort to streamline policymaking and funding activities. This paper examines the areas of convergence and divergence between global health security and universal health coverage, both conceptually and empirically. We consider analytical concepts of risk and human rights as fundamental to both goals, but also identify differences in priorities between the two ideals. We support the argument that the process of health system strengthening provides the most promising mechanism of benefiting both goals.

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)0.031
metaresearch head score (Gemma)0.028
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 score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.070
Scholarly communication0.0160.017
Open science0.0010.030
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.027
GPT teacher head0.395
Teacher spread0.369 · 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

Citations78
Published2019
Admission routes1
Has abstractyes

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