Urban health in Africa: a critical global public health priority
Bibliographic record
Abstract
The African continent is predicted to be home to over half of the expected global population growth between 2015 and 2050, highlighting the importance of addressing population health in Africa for improving public health globally. By 2050, nearly 60% of the population of the continent is expected to be living in urban areas and 35-40% of children and adolescents globally are projected to be living in Africa. Urgent attention is therefore required to respond to this population growth - particularly in the context of an increasingly urban and young population. To this end, the Research Initiative for Cities Health and Equity in Africa (RICHE Africa) Network aims to support the development of evidence to inform policy and programming to improve urban health across the continent. This paper highlights the importance of action in the African continent for achieving global public health targets. Specifically, we argue that a focus on urban health in Africa is urgently required in order to support progress on the Sustainable Development Goals (SDGs) and other global and regional public health targets, including Universal Health Coverage (UHC), the new Urban Agenda, and the African Union's Agenda 2063. Action on urban public health in Africa is critical for achieving global public health targets. Four key research and training priorities for improving urban health in Africa, are outlined: (1) increase intersectoral urban health literacy; (2) apply a healthy urban governance and systems approach; (3) develop a participatory and collaborative urban health planning process; and, (4) produce a new generation of urban health scholars and practitioners. We argue that acting on key priorities in urban health is critical for improving health for all and ensuring that we 'leave no-one behind' when working to achieve these regional and global agendas to improve health and wellbeing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".