Prospective cohort studies allow tackling major research questions regarding health and well-being of individuals and communities. JoinUs4Health aims to combine Responsible Research and Innovation (RRI) and crowdsourcing as converging approaches to promote inclusive innovation and citizens engagement in cohort research.
Objectives are:
1) ESTABLISH and REVIEW a conceptual framework;
2) DEVELOP, TEST and APPLY technology to engage various societal actors;
3) EXPLORE, IMPLEMENT and MONITOR institutional changes and incorporate RRI into the governance framework of three cohort institutions;
4) ADVANCE RRI and citizen science into the mainstream of public engagement, science communication and education; and
5) PROMOTE engagement and COMMUNICATE and DISSEMINATE outputs via traditional and innovative means.
The concept is based on crowdsourcing, where cohort participants, the general public and other societal actors can propose research questions or contribute to addressing such questions via working groups. Researchers are encouraged to actively engage citizens and other societal actors in dialogues during the design of their analyses and interpretation of results. Specialists will enhance this synergy by online and offline activities based on the communication strategy to boost engagement and ensure sustainability. An interactive, web-based platform facilitates and social media promotes engagement, communication and dissemination. Targeted education activities are designed and implemented at the school, university and citizen level. Six institutional changes provide the required conditions targeting the areas engagement, open access, communication and dissemination, management, implementation of RRI at cohort institutions, and education. Their implementation will be mentored by an RRI experienced partner and an international RRI advisory panel. It is hypothesized that this approach also counteracts the trend of decreasing participation in cohort studies.
The following scores were calculated using a statistically-driven machine-learning approach, a type of AI that learns to perform a task by analysing patterns in data. This is an experimental approach to citizen-science impact assessment, and the exact reasoning behind the scores is not explainable. The scores represent a best guess of the impact the project is having in each domain. Scores are recalculated and updated when “View impact report” is clicked.
Proportion of questions answered in each domain.
This is the beginning of a journey (some would say a "trajectory" ) along more than 200 questions. Don't worry; you don't have to answer them all at once. Do something, and come back some other day. You will find a lot of help along the way, and if you're generally happy to start, just click on "Ok, I got it!" below. If you're unsure how to answer a question, again, don't worry, answer it in the way that makes most sense to your project and you can always come back and change it later. If you still have doubts about life, the universe and everything, now or later, head to where to find help for advice and all the clarity you might look for.