Advancing African Medicines Agency through Global Health Diplomacy for an Equitable Pan-African Universal Health Coverage: A Scoping Review
Bibliographic record
Abstract
The African continent is home to 15% of the world's population and suffers from a disease burden of more than 25% globally. In this COVID-19 era, the high burden and mortality are further worsened due to inequities, inequalities such as inadequate health systems, scarce financial and human resources, as well as unavailability of inexpensive medicines of good quality, safety, and efficacy. The Universal Health Coverage ensures that people have access to high-quality essential health services, secure, reliable, and affordable essential medicines and vaccines, as well as financial security. This paper aimed at addressing the critical need for a continental African Medicines Agency (AMA) in addressing the inequities and the role of global health diplomacy in building consensus to support the ratification of the Treaty of AMA. A literature review was done in Scopus, Web of Science, MEDLINE/PubMed, and Google Scholar search engine to identify the critical literature in the context of study objectives. All the articles published after 2015 till 2021 in the context of AMA were included. African Health Strategy 2016-2030 highlighted the importance of an African regulatory mechanism for medicines and medical products. Through global health diplomacy (GHD), the African Union and its partners can negotiate and cooperate in providing infrastructural, administrative, and regulatory support for establishing the AMA. The paper emphasizes the South-South cooperation and highlights the contributions of India and China in the supply of medicines and vaccines to Africa. A strong AMA created through GHD can be a vital instrument in utilizing Trade-Related Aspects of Intellectual Property Rights (TRIPS) flexibilities extension and an ideal partner for European and other regional regulatory authorities seeking to stem the tide of counterfeit, sub-standard, or fake products.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".