African-led health research and capacity building- is it working?
Bibliographic record
Abstract
BACKGROUND: Africa bears a disproportionately high burden of globally significant disease but has lagged in knowledge production to address its health challenges. In this contribution, we discuss the challenges and approaches to health research capacity strengthening in sub-Saharan Africa and propose that the recent shift to an African-led approach is the most optimal. METHODS AND FINDINGS: We introduce several capacity building approaches and recent achievements, explore why African-led research on the continent is a potentially paradigm-shifting and innovative approach, and discuss the advantages and challenges thereof. We reflect on the approaches used by the African Academy of Sciences (AAS)-funded Sub-Saharan African Network for TB/HIV Research Excellence (SANTHE) consortium as an example of an effective African-led science and capacity building programme. We recommend the following as crucial components of future efforts: 1. Directly empowering African-based researchers, 2. Offering quality training and career development opportunities to large numbers of junior African scientists and support staff, and 3. Effective information exchange and collaboration. Furthermore, we argue that long-term investment from international donors and increasing funding commitments from African governments and philanthropies will be needed to realise a critical mass of local capacity and to create and sustain world-class research hubs that will be conducive to address Africa's intractable health challenges. CONCLUSIONS: Our experiences so far suggest that African-led research has the potential to overcome the vicious cycle of brain-drain and may ultimately lead to improvement of health and science-led economic transformation of Africa into a prosperous continent.
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 | 0.132 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".