Back

Exploring best practice governance for EU-funded health research consortia: A qualitative study protocol for insight and ideation

Renker-Darby, A.; Bärnighausen, T.; Neumann, C.; Preet, R.; Treskova, M.

2023-12-09 public and global health
10.1101/2023.12.07.23299676 medRxiv
Show abstract

BackgroundInternational consortia have emerged as a common model to organise and fund large-scale, multi-disciplinary research in contemporary health and biomedical science. The diversity of participants, size and complexity of these consortia necessitates effective governance to achieve their research aims and societal impact. For health research consortia funded by the European Union, certain governance structures and processes have emerged out of convention. However, there is limited scientific evidence to support their use, and little is known about consortia participants perspectives on how governance structures could be improved to better serve the implementation of research. In this paper, we present a protocol for a qualitative study to explore the perspectives of participants in European Union-funded health research consortia on the value of governance structures and how they might be improved. Methods and analysisWe will conduct a qualitative study using in-depth interviews with participants in health research consortia funded by the European Union. We will recruit participants following a purposeful sampling approach, and recruitment will continue until saturation is reached. During in-depth interviews, we will ask participants about how the governance of consortia is structured and what is working well or poorly about those governance structures from their perspective. We will draw on design thinking methods to help participants to ideate improvements in governance structures. Data will be analysed using a thematic analysis approach. DiscussionFindings from this study will provide valuable evidence for developing and formulating governance structures for health research consortia. The findings of this work may also contribute to guidelines for consortium proposal submissions.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.