Strengthening health research capacity in sub-Saharan Africa: mapping the 2012–2017 landscape of externally funded international postgraduate training at institutions in the region
Bibliographic record
Abstract
BACKGROUND: The objective was to guide key stakeholders on future directions of external funding of international postgraduate training (Master's and PhD) of health research students at institutions in sub-Saharan Africa by mapping the numbers and characteristics of students, the location of institutions, and sources of external support. A cross-sectional survey of eligible external funding organizations and programmes was conducted in 2017. Information was gathered from funders' websites or through the assistance of institutional contacts. The information requested included the number of Master's and PhD grantees supported from January 2012 to June 2017, as well as each grantee's institution of study, gender, country of origin and research area. RESULTS: Of 72 organizations contacted, there were 44 responses. Of the 44, 30 funders reported programmes within the inclusion criteria, and 19 funders provided data on relevant programmes. The Wellcome Trust, the International Development Research Centre and the Norwegian Agency for Development Cooperation supported the greatest number of grantees. There was concentrated support for grantees in eastern and southern Africa, countries with developed research capacity, and highly-developed research and training centres. More support was provided for PhD than Master's degree programmes and for research areas more upstream along the research spectrum. Challenges were identified in recognizing relevant funding organizations and obtaining responses. Information was presented inconsistently across organizations, which were often unable to provide relevant and complete data within the survey timeframe. CONCLUSIONS: External funders should collect, analyse and report data at regular intervals on their support for strengthening postgraduate health research capacity in sub-Saharan Africa. Standardization of this process and development of an online database would not only help to avoid overlap between programmes and promote synergy between funders, but also inform dialogue between external funders and key stakeholders on strategic issues. These issues include how external funders can a) optimise their support for research capacity strengthening to maximise the benefits of research for health and development on an equitable basis, and b) optimise the distribution of support for researchers at different career stages and for research on different parts of the research spectrum to maximise the health benefits of research.
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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".