Prioritizing ‘equity’ in COVID-19 vaccine distribution through Global Health Diplomacy
Bibliographic record
Abstract
With over 4 million deaths worldwide, the current coronavirus disease 2019 (COVID-19)pandemic is regarded as one of the worst pandemics in history. With its wider devastating consequences, even so-called affluent countries could not provide full coverage for COVID-19vaccines and medications to all of their citizens. Against this backdrop, the main aim of this article is to examine how Global Health Diplomacy (GHD) can play a role in prioritizing vaccine equity in the global health agenda in the fight against COVID-19. The majority of developed countries' healthcare systems have been exposed and have reached a tipping point.After the completion of eighteen months of the pandemic, only five countries were able to produce vaccines for the treatment of COVID-19. This pandemic has divided the world into two blocs: those with vaccines, such as the United States, the United Kingdom, Russia, China, and India; and those without, such as the rest of the world. The greatest challenges are vaccine inequalities, inequities and distribution, which undermine the global economic recovery. Many poor countries are still waiting for the initial doses to be delivered to their citizens, while some rich nations are planning for booster doses. GHD plays a critical role in establishing successful global collaborations, funding mechanisms and ensuring international cooperation through the combined efforts of all stakeholders. Besides, global solidarity is necessary to lessen the wider gaps between the vaccination status of rich and poor nations. Therefore, through GHD, the vaccine gaps and inequities can be addressed to strengthen global health security and accelerate global economic recovery.
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
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".