“It takes more than a fellowship program”: reflections on capacity strengthening for health systems research in sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) experiences an acute dearth of well-trained and skilled researchers. This dearth constrains the region's capacity to identify and address the root causes of its poor social, health, development, and other outcomes. Building sustainable research capacity in SSA requires, among other things, locally led and run initiatives that draw on existing regional capacities as well as mutually beneficial global collaborations. This paper describes a regional research capacity strengthening initiative-the African Doctoral Dissertation Research Fellowship (ADDRF) program. This Africa-based and African-led initiative has emerged as a practical and tested platform for producing and nurturing research leaders, strengthening university-wide systems for quality research training and productivity, and building a critical mass of highly-trained African scholars and researchers. The program deploys different interventions to ensure the success of fellows. These interventions include research methods and scientific writing workshops, research and reentry support grants, post-doctoral research support and placements, as well as grants for networking and scholarly conferences attendance. Across the region, ADDRF graduates are emerging as research leaders, showing signs of becoming the next generation of world-class researchers, and supporting the transformations of their home-institutions. While the contributions of the ADDRF program to research capacity strengthening in the region are significant, the sustainability of the initiative and other research and training fellowship programs on the continent requires significant investments from local sources and, especially, governments and the private sector in Africa. The ADDRF experience demonstrates that research capacity building in Africa is possible through innovative, multifaceted interventions that support graduate students to develop different critical capacities and transferable skills and build, expand, and maintain networks that can sustain them as scholars and researchers.
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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 | 0.140 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.046 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.016 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 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".