North–south collaboration and capacity development in global health research in low- and middle-income countries – the ARCADE projects
Bibliographic record
Abstract
BACKGROUND: Research capacity enhancement is needed in low- and middle-income countries (LMICs) for improved health, wellbeing, and health systems' development. In this article, we discuss two capacity-building projects, the African/Asian Regional Capacity Development (ARCADE) in Health Systems and Services Research (HSSR) and Research on Social Determinants of Health (RSDH), implemented from 2011 to 2015. The two projects focussed on providing courses in HSSR and social determinants of health research, and on developing collaborations between universities, along with capacity in LMIC universities to manage research grant submissions, financing, and reporting. Both face-to-face and sustainable online teaching and learning resources were used in training at higher postgraduate levels (Masters and Doctoral level). DESIGN: We collated project meeting and discussion minutes along with project periodic reports and deliverables. We extracted key outcomes from these, reflected on these in discussions, and summarised them for this paper. RESULTS: Nearly 55 courses and modules were developed that were delivered to over 920 postgraduate students in Africa, Asia, and Europe. Junior researchers were mentored in presenting, developing, and delivering courses, and in preparing research proposals. In total, 60 collaborative funding proposals were prepared. The consortia also developed institutional capacity in research dissemination and grants management through webinars and workshops. DISCUSSION: ARCADE HSSR and ARCADE RSDH were comprehensive programmes, focussing on developing the research skills, knowledge, and capabilities of junior researchers. One of the main strengths of these programmes was the focus on network building amongst the partner institutions, where each partner brought skills, expertise, and diverse work cultures into the consortium. Through these efforts, the projects improved both the capacity of junior researchers and the research environment in Africa, Asia, and Europe.
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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 | 0.132 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".