Content code | Summary of responses |
---|---|
Use cases | • Improve screening, treatment and prevention options • Nuanced risk prediction and decision-making tools • Investigate broad range of health determinants • Provide community agency via data access |
Desires and visions | • Legitimise career paths for citizen scientists and patient advocates • Improve science and health literacy • Authentic collaborations • Accessible and affordable genetic tests |
Tools and supports | • Mentorship and partnerships • Reliable and sustainable funding sources • Data tools and training • Data sharing platforms |
Barriers | • Disconnect between healthcare, research and community needs • Data capacity challenges • Competing community priorities • Unfamiliarity with Big Data and research • Fear, trauma and mistrust associated with research experiences |
Facilitators | • Collaboration frameworks and shared resources • Interoperability and centrality of data • Trust in leaders and political will |
Attitudes | • Data are valuable and personal • Big Data currently lacks bidirectionality • Superficial community engagement • Data interpretation requires cultural context |