Successes and challenges of north–south partnerships – key lessons from the African/Asian Regional Capacity Development projects
Bibliographic record
Abstract
INTRODUCTION: Increasing efforts are being made globally on capacity building. North-south research partnerships have contributed significantly to enhancing the research capacity in low- and middle-income countries (LMICs) over the past few decades; however, a lack of skilled researchers to inform health policy development persists, particularly in LMICs. The EU FP7 funded African/Asian Regional Capacity Development (ARCADE) projects were multi-partner consortia aimed to develop a new generation of highly trained researchers from universities across the globe, focusing on global health-related subjects: health systems and services research and research on social determinants of health. This article aims to outline the successes, challenges and lessons learned from the life course of the projects, focusing on the key outputs and experiences of developing and implementing these two projects together with sub-Saharan African, Asian and European institution partners. DESIGN: Sixteen participants from 12 partner institutions were interviewed. The data were analysed using thematic content analysis, which resulted in four themes and three sub-categories. These data were complemented by a review of project reports. RESULTS: The results indicated that the ARCADE projects have been successful in developing and delivering courses, and have reached over 920 postgraduate students. Some partners thought the north-south and south-south partnerships that evolved during the project were the main achievement. However, others found there to be a 'north-south divide' in certain aspects. Challenges included technical constraints and quality assurance. Additionally, adapting new teaching and learning methods into current university systems was challenging, combined with not being able to award students with credits for their degrees. CONCLUSION: The ARCADE projects were introduced as an innovative and ambitious project idea, although not designed appropriately for all partner institutions. Some challenges were underestimated from the beginning, and for such future projects, a more structured approach needs to be adopted. ARCADE partners learned that integrating courses into current university systems and awarding students credits are essential.
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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 | 0.087 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".