MétaCan
Menu
Back to cohort
Record W4401707212 · doi:10.34172/ijhpm.8578

Political Prioritization of Access to Medicines and Right to Health: Need for an Effective Global Health Governance Through Global Health Diplomacy Comment on "More Pain, More Gain! The Delivery of COVID-19 Vaccines and the Pharmaceutical Industry’s Role in Widening the Access Gap"

2024· article· en· W4401707212 on OpenAlexaff
Vijay Kumar Chattu, Anjali Pushkaran, Prakash Narayanan

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsCentre for Global Health ResearchUniversity of Toronto
Fundersnot available
KeywordsGlobal healthDiplomacyEssential medicinesBusinessCorporate governanceEquity (law)Global governanceIntellectual propertyRight to healthPoliticsHealth policyPublic relationsEconomic growthPolitical scienceHealth careEconomicsLawFinance

Abstract

fetched live from OpenAlex

Borges and colleagues' article entitled "More Pain, More Gain! The Delivery of COVID-19 Vaccines and the Pharmaceutical Industry's Role in Widening the Access Gap," analyzes the role of pharmaceutical companies in providing equitable access to COVID-19 vaccines. They concluded that with the failure of COVID-19 Vaccine Global Access (COVAX), the health gaps have widened due to the profit-driven pharmaceutical sector. In this commentary, we highlight the role of COVAX and its attempt to bridge some access gaps since its inception and the need for reforms in policy-making and global health governance. The commentary highlights the role of global health diplomacy in promoting equity and negotiating the Trade-Related Aspects of Intellectual Property Rights (TRIPS) waiver for COVID-19 vaccines at the World Trade Organization (WTO) thereby promoting global solidarity, global partnerships, access to medicine and health products, and the right to health. We conclude that political prioritization is the key to balance the impact of profit-driven pharma industry and addressing the needs of low- and middle-income countries (LMICs).

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)0.019
metaresearch head score (Gemma)0.053
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0060.018
Scholarly communication0.0110.012
Open science0.0040.005
Research integrity0.0490.046
Insufficient payload (model declined to judge)0.0070.002

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 head0.099
GPT teacher head0.492
Teacher spread0.393 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Health Policy and ManagementSame topicPharmaceutical Economics and PolicyFrench-language works237,207