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Record W2041840751 · doi:10.2174/1874613601004020025

Achieving a Dream: Meeting Policy Goals Related to Improving Drug Access

2010· article· en· W2041840751 on OpenAlexaff
David Zakus, Jillian Clare Köhler, Venera Zakriova, Aaron N. Yarmoshuk

Bibliographic record

VenueThe Open AIDS Journal · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsPublic Health OntarioUniversity of TorontoCanadian Society for International HealthAIDS VancouverHealth Canada
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)MedicineFace (sociological concept)Economic growthAccess to medicinesPublic relationsDeveloping countryDevelopment economicsPolitical scienceSocial scienceFamily medicineSociologyEconomics

Abstract

fetched live from OpenAlex

International experts recognize that significant inequities exist in the accessibility of life-saving medicines among poor and vulnerable populations, especially in developing countries. This article highlights that drug access even for relatively cheap medicines is out of reach for the vast numbers of global poor. This badly affects people living with HIV/AIDS who face serious obstacles in accessing ARVs. The same concerns are attributed to neglected diseases. Despite international meetings, promises from the pharmaceutical industry and a lot of media attention little has changed in the past 20 years. The accessibility gap to life-saving drugs could be reduced by the UNITAID initiative to pool patents for the many different ARVs, but the reality is that UNITAID is still a promise. To surmount this global problem of inequity requires a rethinking of traditional models of drug access and health objectives that should not be compromised by commercial interests.

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)0.017
metaresearch head score (Gemma)0.035
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.007
Scholarly communication0.0110.023
Open science0.0020.008
Research integrity0.0170.011
Insufficient payload (model declined to judge)0.0140.003

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 head0.054
GPT teacher head0.358
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
GenreCommentary

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
Published2010
Admission routes1
Has abstractyes

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