Successes and challenges of north–south partnerships – key lessons from the African/Asian Regional Capacity Development projects
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,087 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,015 | 0,014 |
| Communication savante | 0,014 | 0,013 |
| Science ouverte | 0,004 | 0,031 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».