Balancing the personal, local, institutional, and global: multiple case study and multidimensional scaling analysis of African experiences in addressing complexity and political economy in health research capacity strengthening
Bibliographic record
Abstract
BACKGROUND: Strengthening health research capacity in low- and middle-income countries remains a major policy goal. The Health Research Capacity Strengthening (HRCS) Global Learning (HGL) program of work documented experiences of HRCS across sub-Saharan Africa. METHODS: We reviewed findings from HGL case studies and reflective papers regarding the dynamics of HRCS. Analysis was structured with respect to common challenges in such work, identified through a multi-dimensional scaling analysis of responses from 37 participants at the concluding symposium of the program of work. RESULTS: Symposium participants identified 10 distinct clusters of challenges: engaging researchers, policymakers, and donors; securing trust and cooperation; finding common interest; securing long-term funding; establishing sustainable models of capacity strengthening; ensuring Southern ownership; accommodating local health system priorities and constraints; addressing disincentives for academic engagement; establishing and retaining research teams; and sustaining mentorship and institutional support. Analysis links these challenges to three key and potentially competing drivers of the political economy of health research: an enduring model of independent researchers and research leaders, the globalization of knowledge and the linked mobility of (elite) individuals, and institutionalization of research within universities and research centres and, increasingly, national research and development agendas. CONCLUSIONS: We identify tensions between efforts to embrace the global 'Community of Science' and the promotion and protection of national and institutional agendas in an unequal global health research environment. A nuanced understanding of the dynamics and implications of the uneven global health research landscape is required, along with a willingness to explore pragmatic models that seek to balance these competing drivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".