Fifty Years of Global Cardiovascular Research in Africa: A Scientometric Analysis, 1971 to 2021
Bibliographic record
Abstract
Background To analyze the quantity and impact of cardiovascular research done in Africa or coauthored by researchers based in Africa, their determinants, and the patterns of research collaboration. Methods and Results We retrieved data from Web of Science and additional sources. We analyzed temporal trends from 1971 to 2021 and geographical distribution, research impact using country‐level h‐index, international research cooperation, and associations of research quantity and quality using linear regression. The annual volume of cardiovascular research from Africa has increased from 4 publications in 1971 to 3867 in 2020 and currently represents ~3% of the global cardiovascular research output. Authors from South Africa (28.1%) and Egypt (24.1%) accounted for more than half of all publications from African countries, and they had the highest h‐index (209 and 111, respectively). Important collaborators outside Africa included the United States, United Kingdom, France, Germany, and Australia. The country's publication count was associated with larger population size ( P <0.001), whereas the country's h‐index was associated with larger population size ( P =0.001) and higher human development index ( P =0.023). International collaboration was dominated by the United States, South Africa, United Kingdom, Egypt, and Canada. The level of collaboration between African countries was lower than their collaboration with non‐African countries. Conclusions Cardiovascular research output from African authors remains low, despite marked progress over the past 5 decades. These findings highlight the urgent need to improve the quantity and quality of cardiovascular research in Africa through increased investments, training of human resources, improved infrastructures, and expansion of collaborative research networks, particularly within Africa.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.056 | 0.101 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".