The syndemic burden of HIV/AIDS in Africa amidst the COVID‐19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) has long affected millions of individuals across the globe. Historically, the prevalence of this disease is particularly noted within the African continent. Before the coronavirus disease 2019 (COVID-19) pandemic, many African countries struggled to effectively manage the increasing burden associated with HIV/AIDS. There is now a need to reassess this in a COVID-19 pandemic context so that the impact of COVID-19 on HIV/AIDS healthcare within Africa can be adequately evaluated. METHODS: Data collection was performed on the PubMed, Ovid MEDLINE and Embase bibliographical databases with a predefined search strategy. Searches were performed in blind duplicate and all articles considering COVID-19 and HIV/AIDS within African healthcare were considered. RESULTS: The COVID-19 pandemic has severely exacerbated the many issues surrounding HIV/AIDS care within many African countries. These impacts are noticeable in medical, psychological, and socio-political contexts. CONCLUSIONS: Before efforts are made to improve the provision of HIV/AIDS and COVID-19 care within Africa, it is important that this issue is brought to the attention of the scientific and clinical community so that the continent can receive the necessary support and aid.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.011 | 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".