A critical analysis of COVAX alliance and corresponding global health governance and policy issues: a scoping review
Bibliographic record
Abstract
INTRODUCTION: The COVAX alliance is a novel approach to international partnership in global health intended to achieve the worthy goal of 'COVID-19 vaccine equity'. This study aimed to identify the implementation challenges and framework gaps of COVAX and to explore the interconnected global health policy and governance gaps in ensuring equity, accessibility and affordability of vaccines. METHODS: A scoping review was conducted to identify the implementation challenges and framework gaps of COVAX and related global health policy and governance gaps. A search was carried out in PubMed, Scopus, Springer Link and Embase databases. Manually searched the grey literature, such as official reports and articles. EndNote V.20 was used to manage the evidence screening, and data extraction was carried out in Microsoft Excel. RESULTS: Searches of four electronic databases and official UN, GAVI and WHO websites identified 4686 pieces of evidence. The 937 duplicates were removed, and the remaining 3749 articles were screened for the title and abstract. Most articles were eliminated as they do not address global COVAX or COVID-19 vaccine equity. The remaining 53 pieces of evidence were reviewed for full text, and ultimately 40 articles found eligible were included in the scoping review. CONCLUSIONS: The implementation challenges of COVAX were attributed mainly to the phenomenon of vaccine nationalism by rich countries. The future global health policy and governance structure must be re-examined to address the inadequacies of such novel super public-and-private partnership models.
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 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 | 0.135 | 0.351 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.028 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".