A new era for African health systems: Market shaping and the African Continental Free Trade Area (AfCFTA)
Bibliographic record
Abstract
The COVID-19 pandemic has forced a reflection on the origins of supplies in African healthcare market and underscored the need for an increase in local manufacturing of medical supplies. Several African countries' health markets have been heavily reliant on imports. First, this article demonstrates how the African healthcare market has had a high import dependency and the role that the African Continental Free Trade Area (AfCFTA) could play to reverse this. It is estimated that African countries import between 80% and 94% of medical supplies, 75% of testing kits, between 70% and 95% of pharmaceuticals, and 99% of vaccines. Second, during the COVID-19 pandemic, countries imposed export restrictions which impacted the flow of medical supplies to African countries. This finding highlighted the limited production capabilities on the African continent and reiterated the need to strengthen continental value chains and local manufacturing capacity to establish the continent's New Public Health Order. Third, there was the emergence of local innovations seeking to minimize the impact of these supply chain disruptions. Using case studies on the local production of COVID-19 testing kits and personal protective equipment, the article highlights progress made toward health market reform. It calls attention to the implementation of the AfCFTA to strengthen the supply, manufacturing, and trade of medical resources. Fourth, this article highlights countries that have African-made pharmaceuticals and vaccinations and the importance of regional hubs to expand these products in African healthcare markets. It concludes by discussing investments made to expand local manufacturing of health products.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".