Aging with HIV/AIDS in Sub‐Saharan Africa: A scoping review
Bibliographic record
Abstract
Abstract Older adults in Sub‐Saharan Africa (SSA) are projected to increase 4‐fold from 46 million to 165 million by 2050. Despite this fact, global health policies and programs typically neglect this particular demographic. This is illustrated by the fact that policies and programs directed towards human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) in SSA commonly overlook older adults managing HIV/AIDS, despite this demographic being projected to increase 190% by 2040. To support evidence‐informed policy and practice, this scoping review synthesizes peer‐reviewed articles focusing on older adults (50‐plus years) managing HIV/AIDS in SSA. A systematic search of peer‐reviewed articles written in English between 2000 and 2024 resulted in a total of 48,961 articles, of which 42 met the inclusion criteria. Results revealed four primary themes: knowledge of HIV/AIDS, challenges in healthcare settings (such as ageist assumptions from healthcare providers), barriers to HIV/AIDS‐related care (including transportation barriers), and experiences of those managing HIV/AIDS (such as difficulties with status disclosure). Findings underscore that to improve the health and wellbeing of older populations living with HIV/AIDS in SSA, policies and programs should address ageism and discrimination in healthcare settings and address financial insecurities.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".