Extending Healthy Ageing Narratives in Sub-Saharan Africa: Expert Viewpoint
Bibliographic record
Abstract
The nexus of rapid demographic transition and underdeveloped geriatric infrastructure poses a critical, yet understudied challenge in Sub-Saharan Africa (SSA). As global life expectancies rise, SSA's older population is projected to triple by 2050, intensifying the need for sustainable age-friendly environments (AFEs) and robust healthy ageing interventions. Informal or family caregiving structures, while vital, are under strain from rapid urbanisation and shifting social dynamics, creating a compelling gap between need and provision. This expert viewpoint draws on the authors' professional and scholarly experience regarding population ageing, AFEs, and healthy ageing to provide a comprehensive outlook on these issues in SSA. Selective literature searches were conducted in Google Scholar, Scopus and PubMed using targeted keywords and MESH terms, including "ageing in Africa", "ageing in Sub-Saharan Africa", "healthy ageing in Africa", "healthy ageing in Sub-Saharan Africa", "population ageing in Africa", "population ageing in Sub-Saharan Africa", "age-friendly environment in Africa", and "age-friendly environment in Sub-Saharan Africa." The authors argue that rapid population ageing in SSA is outpacing existing informal care arrangements, necessitating a strategic shift towards the development of age-friendly environments and more coordinated healthy ageing interventions to bridge the widening gap between demographic change and geriatric support systems. This paper underscores the necessity of proactive, evidence-based policy implementation to secure the well-being of SSA's burgeoning older population.
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 | 0.037 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| 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".