Suchergebnisse

Multimorbidity is of increasing concern for healthcare systems globally, particularly in the context of ageing population structures, such as in the European Union and the UK. Although there is growing attention on developing strategies to manage the health and healthcare burden of older patients with multimorbidity, little research or policy focus has been placed on how to best prevent the development of multimorbidity in future generations. In this research agenda piece, we argue for a shift from a sole focus on the management of multimorbidity in old age to a multimorbidity agenda that considers prevention and management throughout the life-course.

ObjectivesPublic involvement and engagement have been growing within big data research. However, seldom heard voices such as migrant and ethnic minorities communities are often underrepresented. This study explored how Polish and South Asian communities in the United Kingdom could be better included in public involvement and engagement activities.
ApproachWe conducted semi-structured interviews with Polish (n=20) and South Asians (n=20) to elicit their views on big data research, public involvement and engagement. We focused on Polish and South Asian communities as they represent some of the United Kingdom's largest migrant and ethnic minority groups. Data were analysed using inductive thematic analysis. Public advisors were involved in the analysis. They and one of the researchers come from ethnic minority and offered insider insight into participants' perspectives and thus allowing us to unpick the complexity of experiences and backgrounds.
ResultsThe majority of participants were willing to become involved or engaged in big data research. However, we found there were multiple barriers to involvement, these included: language (especially for those for whom English is the second language); use of jargon by researchers; time restrictions and unfamiliarity with big data or public involvement. Some participants questioned how much migrants could be involved when they were only in the United Kingdom on a temporary basis. The participants made recommendations for how researchers can mitigate these barriers. Awareness-raising activities would allow people to expand their understanding and build their confidence when speaking about big data research in a second language. Participants spoke of the need for researchers to work more closely with local communities, especially with local gatekeepers.
ConclusionsThe results indicate that there is no 'right' way to involve and engage seldom heard communities around big data research. Researchers need to engage with communities, establish trust and develop a long-lasting relationships. These partnerships should move beyond single projects and aim to benefit both researchers and seldom heard communities.

IntroductionInvolving public contributors helps researchers to ensure that public views are taken into consideration when designing and planning research, so that it is person-centred and relevant to the public. This paper will consider public involvement in big data research. Inclusion of different communities is needed to ensure everyone's voice is heard. However, there remains limited evidence on how to improve the involvement of seldom-heard communities in big data research.
ObjectivesThis study aims to understand how South Asians and Polish communities in the UK can be encouraged to participate in public involvement initiatives in big data research.
MethodsForty interviews were conducted with Polish (n=20) and South Asian (n=20) participants on Zoom. The participants were living in the United Kingdom and had not previously been involved as public contributors. Transcribed interviews were analysed using reflexive thematic analysis.
ResultsWe identified eight themes. The 'happy to reuse data' theme sets the scene by exploring our participants' views towards big data research and under what circumstances they thought that data could be used. The remaining themes were mapped under the capability-opportunity-motivation-behaviour (COM-B) model, as developed by Michie and colleagues. This allowed us to discuss multiple factors that could influence people's willingness to become public contributors.
ConclusionsOur study is the first to explore how to improve the involvement and engagement of seldom-heard communities in big data research using the COM-B model. The results have the potential to support researchers who want to identify what can influence members of the public to be involved. By using the COM-B model, it is possible to determine what measures could be implemented to better engage these communities.