Public involvement and engagement in big data research: A scoping review

Piotr Teodorowski, Elisa Jones, Saiqa Ahmed, Naheed Tahir, Lucy Frith
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 MethodsFollowing Arksey and O’Malley’s methodology, we systematically searched the following databases: CINAHL, Health Research Premium Collection, PubMed, Scopus and Web of Science for papers published between 2010-2021. Additional manual searches took place. These included the first 100 hits in Google search, journals (BMC Research Involvement and Engagement, International Journal of Population Data Science and Health Expectations) and grey literature (Patient Outcome Research Institute database, first 100 hits were screened). We extracted data using a standardised form. We then organised it in a descriptive and narrative way. A system logic model was developed to understand the complexity of this topic.
 ResultsFifty-three papers were identified as eligible for inclusion in our review. The findings indicate that public involvement and engagement have the potential to improve public trust and accountability for data resharing for research. However, there is limited literature actually evaluating these activities. The findings suggest that the public can be meaningfully involved and engaged in big data research, both in terms of individual research projects and data governance, but there is no one standardised approach to do it. Therefore, we developed an initial system logic model to map relevant aspects of the involvement and engagement activities. These include which communities to reach, the context (e.g. ethical, legal aspects or public views), the design and delivery of activities, and outcomes.
 ConclusionDespite the growing literature on public involvement and engagement in big data research, more research is needed as there are few primary empirical studies exploring involvement and engagement. We suggest using the system logic model we developed when reflecting on issues that might be relevant in organising these activities.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Population Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23889/ijpds.v8i2.2247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

ObjectivesPublic involvement and engagement have been suggested as a way to establish public support for big data research, yet there has been no review exploring how these activities could facilitate this. Therefore, this scoping review aimed to explore public involvement and engagement in big data research. MethodsFollowing Arksey and O’Malley’s methodology, we systematically searched the following databases: CINAHL, Health Research Premium Collection, PubMed, Scopus and Web of Science for papers published between 2010-2021. Additional manual searches took place. These included the first 100 hits in Google search, journals (BMC Research Involvement and Engagement, International Journal of Population Data Science and Health Expectations) and grey literature (Patient Outcome Research Institute database, first 100 hits were screened). We extracted data using a standardised form. We then organised it in a descriptive and narrative way. A system logic model was developed to understand the complexity of this topic. ResultsFifty-three papers were identified as eligible for inclusion in our review. The findings indicate that public involvement and engagement have the potential to improve public trust and accountability for data resharing for research. However, there is limited literature actually evaluating these activities. The findings suggest that the public can be meaningfully involved and engaged in big data research, both in terms of individual research projects and data governance, but there is no one standardised approach to do it. Therefore, we developed an initial system logic model to map relevant aspects of the involvement and engagement activities. These include which communities to reach, the context (e.g. ethical, legal aspects or public views), the design and delivery of activities, and outcomes. ConclusionDespite the growing literature on public involvement and engagement in big data research, more research is needed as there are few primary empirical studies exploring involvement and engagement. We suggest using the system logic model we developed when reflecting on issues that might be relevant in organising these activities.
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公众参与和参与大数据研究:范围审查
公众参与和参与已被建议作为建立公众对大数据研究支持的一种方式,但尚未有评论探讨这些活动如何促进这一目标。因此,本综述旨在探讨公众对大数据研究的参与和参与。 方法采用Arksey和O 'Malley的方法,系统检索了2010-2021年间发表的论文:CINAHL、Health Research Premium Collection、PubMed、Scopus和Web of Science。还进行了额外的人工搜索。这些包括谷歌搜索的前100个点击,期刊(BMC研究参与和参与,国际人口数据科学和健康期望期刊)和灰色文献(患者结果研究所数据库,前100个点击被筛选)。我们使用标准化表格提取数据。然后我们以描述和叙述的方式组织它。开发了一个系统逻辑模型来理解这个主题的复杂性。 结果53篇论文被纳入我们的综述。调查结果表明,公众参与和参与有可能提高公众对研究数据再共享的信任和问责制。然而,实际评价这些活动的文献有限。研究结果表明,公众可以有意义地参与和从事大数据研究,无论是个人研究项目还是数据治理,但没有一种标准化的方法来做到这一点。因此,我们开发了一个初始的系统逻辑模型来映射参与和参与活动的相关方面。这些包括要接触哪些社区、背景(例如道德、法律方面或公众意见)、活动的设计和实施以及结果。 尽管关于公众参与和参与大数据研究的文献越来越多,但关于公众参与和参与的初步实证研究很少,还需要更多的研究。我们建议在考虑与组织这些活动相关的问题时,使用我们开发的系统逻辑模型。
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