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Promising Trends in Access to Medicines

2012· article· en· W1494421313 on OpenAlexaff
E. Richard Gold, Jean‐Frédéric Morin

Bibliographic record

VenueGlobal Policy · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsDeveloping countryBusinessContext (archaeology)Language changeAccess to medicinesIntellectual propertyPrincipal (computer security)Market accessEconomic growthEconomicsPolitical scienceComputer security

Abstract

fetched live from OpenAlex

It is a vast understatement to say that the problem of access to medicines in developing countries is complex. Access is limited by a range of factors including inability to pay, a lack of infrastructure, and corruption in some countries. Surrounding and exacerbating these structural and technological problems is the layer of legal rights created by patents and their licensing that complicate and render more expensive the preparation and delivery of needed medicines, particularly those that need to be adapted to the social, health and cultural environment of developing countries. This article provides a survey of innovative strategies that aim at maximizing the potential of patents to facilitate the development and delivery of medicines against diseases, the burden of which falls principally on developing country populations. To understand the context in which these strategies are being proposed and implemented, the article reviews the battles over access to medicines beginning in the late 1980s. It then surveys some of the principal suggestions put forward to better direct innovation systems in addressing the critical health needs of the world's majority including advance market commitments, patent buy-outs, prize funds, public-private partnerships and patent pools.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.116
GPT teacher head0.398
Teacher spread0.282 · 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 designObservational
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

Citations8
Published2012
Admission routes1
Has abstractyes

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