Status of “African research and its contribution to global health research: a review and an opinion”
Bibliographic record
Abstract
Abstract Aim Research is key to the development of any nation, with health research being of high value to society. Research provides data that is useful for the generation and provision of knowledge needed for policy making, planning and strategic management to develop nations. Despite the African population accounting for nearly 14% of the world’s population, little published research originates from African scholars. Purpose The purpose of this article is to highlight the reasons for the lack of robust research by scientists from African low-and-middle-income countries and to emphasize the inequity experienced by African scientists in global health research. Possible solutions to the dearth in African global health research are equally explored. Findings Insufficient research has led to poor growth, development and advancements in health in Africa. A significant gap in African-led and published research, lack of mentorship, inequitable access to research funding and grant eligibility, and increased dependency on foreign organizations have contributed to the lack of sustainability and failure of African research. Conclusion Research is crucial for national development, especially in health. However, African researchers are underrepresented in global publications. To optimize research, local needs and African researchers must be prioritized, and research systems in African institutions must be strengthened. Additionally, the international community must respect, be transparent, and support research development in Africa. These efforts are essential for fostering a robust research environment, addressing global health challenges, and promoting sustainable development across Africa and LMICs.
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 | 0.026 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".