Global vaccine inequities and multilateralism amid COVID-19: Reconnaissance of Global Health Diplomacy as a panacea?
Bibliographic record
Abstract
Background: The ongoing COVID-19 pandemic has shown a crystal-clear warning that nobody will be safe until everybody is safe against the pandemic. However, how everyone is safe when the pandemic’s fat tail risks have broken every nerve of the global economy and healthcare facilities, including vaccine equity. Vaccine inequity has become one of the critical factors for millions of new infections and deaths during this pandemic. Against the backdrop of exponentially growing infected cases of COVID-19 along with vaccine in-equity, this paper will examine how multilateralism could play its role in mitigating vaccine equity through Global Health Diplomacy (GHD). Second, given the most affected developing countries’ lack of participation in multilateralism, could GHD be left as an option in the worst-case scenario?. Methods: In this narrative review, a literature search was conducted in all the popular databases, such as Scopus, Web of Science, PubMed and Google search engines for the keywords in the context of developing countries and the findings are discussed in detail. Results: In this multilateral world, the global governance institutions in health have been monopolized by the global North, leading to COVID-19 vaccine inequities. GHD aids health protection and public health and improves international relations. Besides, GHD facilitates a broad range of stakeholders’ commitment to collaborate in improving healthcare, achieving fair outcomes, achieving equity, and reducing poverty. Conclusion: Vaccine inequity is a major challenge of the present scenario, and GHD has been partly successful in being a panacea for many countries in the global south.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".