Pacing Forward in the Face of Fragility: Lessons From African Institutions and Governments' Response to Public Health Emergencies
Bibliographic record
Abstract
Africa is home to 54 United Nation member states, each possessing a wealth of ethno-cultural, physiographic, and economic diversity. While Africa is credited as having the youngest population in the world, it also exhibits a unique set of "unfortunate realties" ranging from famine and poverty to volatile politics, conflicts, and diseases. These unfortunate realities all converge around social inequalities in health, that are compounded by fragile healthcare systems and a lack of political will by the continent's leaders to improve smart investment and infrastructure planning for the benefit of its people. Noteworthy are the disparities in responsive approaches to crises and emergencies that exist across African governments and institutions. In this context, the present article draws attention to 3 distinct public health emergencies (PHEs) that have occurred in Africa since 2010. We focus on the 2013-2016 Ebola outbreak in Western Africa, the ongoing COVID-19 pandemic which continues to spread throughout the continent, and the destructive locust swarms that ravaged crops across East Africa in 2020. Our aim is to provide an integrated perspective on how governments and institutions handled these PHEs and how scientific and technological innovation, along with educational response played a role in the decision-making process. We conclude by touching on public health policies and strategies to address the development of sustainable health care systems with the potential to improve the health and well-being of the African people.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".