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"
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.049 | 0.046 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".