探索如何提高波兰和南亚社区对大数据研究的参与度。采用COM-B模型进行定性研究。

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES
Piotr Teodorowski, Sarah E Rodgers, Kate Fleming, Naheed Tahir, Saiqa Ahmed, Lucy Frith
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引用次数: 0

摘要

简介:让公众参与有助研究人员确保在设计和规划研究时考虑公众意见,使研究以人为本,与公众息息相关。本文将考虑公众对大数据研究的参与。需要包容不同的社区,以确保每个人的声音都能被听到。然而,关于如何提高鲜为人知的社区在大数据研究中的参与度,证据仍然有限。目的:本研究旨在了解如何鼓励英国的南亚和波兰社区参与大数据研究的公众参与计划。方法:在Zoom上对波兰(n=20)和南亚(n=20)参与者进行了40次访谈。参与者居住在英国,以前没有作为公共贡献者参与。访谈记录采用反身性主题分析进行分析。结果:我们确定了八个主题。“乐于重用数据”的主题通过探讨参与者对大数据研究的看法以及他们认为在什么情况下可以使用数据来设定场景。其余的主题是在能力-机会-动机-行为(COM-B)模型下绘制的,这是由Michie和他的同事开发的。这使我们能够讨论可能影响人们成为公共贡献者意愿的多种因素。结论:我们的研究首次探索了如何使用COM-B模型来提高鲜为人知的社区在大数据研究中的参与度。研究结果有可能支持那些想要确定什么能影响公众参与的研究人员。通过使用COM-B模型,可以确定可以实施哪些措施来更好地吸引这些社区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Exploring how to improve the involvement of Polish and South Asian communities around big data research. A qualitative study using COM-B model.

Exploring how to improve the involvement of Polish and South Asian communities around big data research. A qualitative study using COM-B model.

Exploring how to improve the involvement of Polish and South Asian communities around big data research. A qualitative study using COM-B model.

Introduction: Involving 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.

Objectives: This 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.

Methods: Forty 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.

Results: We 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.

Conclusions: Our 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.

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CiteScore
2.50
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