Evaluation of Public Involvement in Doctoral Research Using a Four-Dimensional Theoretical Framework.

BACKGROUND: Working together and co-production with public advisors have become popular among health researchers. This practice extends to doctoral researchers who involve public advisors at different stages of their research or throughout their doctoral journey. OBJECTIVE: A doctoral researcher and two public advisors jointly evaluated public involvement in doctoral research. METHODS: Using the established public involvement evaluation framework by Gibson and colleagues, public advisors and a doctoral researcher mapped and evaluated their experiences of public involvement in doctoral research. The four-dimensional framework allowed the authors to reflect on (1) the strength of the public voice, (2) the number of ways in which public advisors had an opportunity to get involved, (3) whether the discussion was about the public or organisation's (doctoral researcher, university or funder) concerns and (4) if the organisation changed or resisted feedback. Results are presented in a diagrammatic and narrative way. RESULTS: Public advisors saw themselves as having a stronger voice in doctoral research than the doctoral researcher perceived. All agreed that there existed multiple ways for public advisors to be involved. Public advisors' feedback was taken on board, but it was also limited due to restrictions of what the doctoral programme allowed. CONCLUSION: Public advisors ensured that the doctoral research was more relevant to the public. The ongoing involvement also shaped the doctoral researcher's thinking and views. PATIENT AND PUBLIC INVOLVEMENT: Two public advisors were involved throughout the 3 years of this doctoral research. They co-evaluated this involvement and are co-authors of this paper.

Test, evidence, transition projects in Scotland: developing the evidence needed for transition of effective interventions in cancer care from innovation into mainstream practice.

BACKGROUND: A robust evidence base is required to assist healthcare commissioners and providers in selecting effective and sustainable approaches to improve cancer diagnosis and treatment. Such evidence can be difficult to build, given the fast-paced and highly pressured nature of healthcare delivery, the absence of incentives, and the presence of barriers in conducting pragmatic yet robust research evaluations. Cancer Research UK (CRUK) has played an active part in building the evidence base through its funding of programmes to identify, evaluate and scale-up innovative approaches across the UK. The aim of this paper is to describe and explain the research design and intended approach and activities for two cancer services improvement projects in Scotland funded by CRUK. METHODS: A hybrid effectiveness-implementation study design will assess both the efficiency of the new pathways and their implementation strategies, with the aim of generating knowledge for scale-up. A range of implementation, service and clinical outcomes will be assessed as determined by the projects' Theories of Change (ToCs). A naturalistic case study approach will enable in-depth exploration of context and process, and the collection and synthesis of data from multiple sources including routine datasets, patient and staff surveys, in-depth interviews and observational and other data. The evaluations are informed throughout by a patient/public representatives' group, and by small group discussions with volunteer cancer patients. DISCUSSION: Our approach has been designed to provide a holistic understanding of how (well) the improvement projects work (in relation to their anticipated outcomes), and how they interact with their wider contexts. The evaluations will help identify barriers, facilitators, and unanticipated consequences that can impact scalability, sustainability and spread. By opting for a pragmatic, participatory evaluation research design, we hope to inform strategies for scaling up successful innovations while addressing challenges in a targeted manner.

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.

Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence.

BACKGROUND: The growth of data science and artificial intelligence offers novel healthcare applications and research possibilities. Patients should be able to make informed choices about using healthcare. Therefore, they must be provided with lay information about new technology. A team consisting of academic researchers, health professionals, and public contributors collaboratively co-designed and co-developed the new resource offering that information. In this paper, we evaluate this novel approach to co-production. METHODS: We used participatory evaluation to understand the co-production process. This consisted of creative approaches and reflexivity over three stages. Firstly, everyone had an opportunity to participate in three online training sessions. The first one focused on the aims of evaluation, the second on photovoice (that included practical training on using photos as metaphors), and the third on being reflective (recognising one's biases and perspectives during analysis). During the second stage, using photovoice, everyone took photos that symbolised their experiences of being involved in the project. This included a session with a professional photographer. At the last stage, we met in person and, using data collected from photovoice, built the mandala as a representation of a joint experience of the project. This stage was supported by professional artists who summarised the mandala in the illustration. RESULTS: The mandala is the artistic presentation of the findings from the evaluation. It is a shared journey between everyone involved. We divided it into six related layers. Starting from inside layers present the following experiences (1) public contributors had space to build confidence in a new topic, (2) relationships between individuals and within the project, (3) working remotely during the COVID-19 pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) requirements that co-production needs to be inclusive and accessible to everyone, (6) expectations towards data science and artificial intelligence that researchers should follow to establish public support. CONCLUSIONS: The participatory evaluation suggests that co-production around data science and artificial intelligence can be a meaningful process that is co-owned by everyone involved.

Modern technology offers new treatment options for patients and novel avenues of research. However, there is limited available information in easily understandable language for the public explaining how technology relates to them and could influence their healthcare. The researchers, healthcare professionals and public members worked together collaboratively to address this problem by creating new materials for the public. Our paper explores that project through creative methods. Firstly, everyone involved was offered an opportunity to attend training sessions. Then, people took photos and described them to illustrate to others what is their experience of working together. Finally, we all met to use included photos as building blocks to present a shared experience in the project. Afterwards, the professional artist included it as one circular illustration with six interlinked layers. These layers present everyone's experiences (from inside) (1) is about the opportunity to build confidence in a new topic, (2) relationships with others, (3) working remotely during the pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) expectation that the project needs be inclusive and accessible, (6) ethical principles that researchers using new technology should follow. We showed that it is possible for researchers, healthcare professionals and members of the public to feel joint ownership of the project and that working together can be meaningful to everyone.

'A journey of self-discovery and transformation': A theoretical and comprehensive evaluation of the Queen's nursing institute Scotland community development programme.

AIM: To evaluate adoption, implementation and maintenance of the Queen's Nursing Institute Scotland development programme. DESIGN: A comprehensive, longitudinal, qualitative evaluation. METHOD: Participants from the first two cohorts were interviewed at different stages to explore adoption, implementation and maintenance. Managers of participants engaged in interviews to explore service changes. Facilitators took part in a focus group exploring delivery. A member-checking event was held. Data collection was between March 2017 and October 2019. Data analysis was thematically followed by the application of Normalization Process Theory. RESULT: Ninety-four interviews, two focus groups and a member-checking event were conducted. Prior to the programme most participants were burnt-out and considering leaving. Engaging led to a journey of self-discovery and transformation. The programme was perceived to change their way of thinking, personally and professionally, unlike any training and development previously experienced. Participants were rejuvenated and reinvigorated, sharing their learning with colleagues, service users and family, implementing new working practices and furthering their careers. They developed communities of practice amongst their cohorts with strong bonds; enabling them to build and sustain learnings. CONCLUSION: Participants experienced a journey of self-discovery and transformation unlike anything before due to the personal investment in them. Participants were rejuvenated and reinvigorated with many moving into new roles. The programme equipped them with a range of leadership and resilience skills. IMPACT: The Queen's Nursing Institute Scotland Development Programme had a profound impact on participants, personally and professionally, which was perceived as lifelong. These findings and programmes are transferable beyond Scotland and to different professions.

'To me, it's ones and zeros, but in reality that one is death': A qualitative study exploring researchers' experience of involving and engaging seldom-heard communities in big data research.

BACKGROUND: Big data research requires public support. It has been argued that this can be achieved by public involvement and engagement to ensure that public views are at the centre of research projects. Researchers should aim to include diverse communities, including seldom-heard voices, to ensure that a range of voices are heard and that research is meaningful to them. OBJECTIVE: We explored how researchers involve and engage seldom-heard communities around big data research. METHODS: This is a qualitative study. Researchers who had experience of involving or engaging seldom-heard communities in big data research were recruited. They were based in England (n = 5), Scotland (n = 4), Belgium (n = 2) and Canada (n = 1). Twelve semistructured interviews were conducted on Zoom. All interviews were audio-recorded and transcribed, and we used reflexive thematic analysis to analyse participants' experiences. RESULTS: The analysis highlighted the complexity of involving and engaging seldom-heard communities around big data research. Four themes were developed to represent participants' experiences: (1) abstraction and complexity of big data, (2) one size does not fit all, (3) working in partnership and (4) empowering the public contribution. CONCLUSION: The study offers researchers a better understanding of how to involve and engage seldom-heard communities in a meaningful way around big data research. There is no one right approach, with involvement and engagement activities required to be project-specific and dependent on the public contributors, researchers' needs, resources and time available. PATIENT AND PUBLIC INVOLVEMENT: Two public contributors are authors of the paper and they were involved in the study design, analysis and writing.

Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis.

BACKGROUND: "Data Saves Lives" is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area. OBJECTIVE: This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users' medical data. METHODS: Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory. RESULTS: Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag. CONCLUSIONS: The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to "Twitter bubbles": groups of similar-minded Twitter users.

Public involvement and engagement in big data research: protocol for a scoping review and a systematic review of delivery and effectiveness of strategies for involvement and engagement.

INTRODUCTION: Big data research has grown considerably over the last two decades. This presents new ethical challenges around consent, data storage and anonymisation. Big data research projects require public support to succeed and it has been argued that one way to achieve this is through public involvement and engagement. To better understand the role public involvement and engagement can play in big data research, we will review the current literature. This protocol describes the planned review methods. METHODS AND ANALYSIS: Our review will be conducted in two stages. In the first stage, we will conduct a scoping review using Arksey and O'Malley methodology to comprehensively map current evidence on public involvement and engagement in big data research. Databases (CINAHL, Health Research Premium Collection, PubMed, Scopus, Web of Science) and grey literature will be searched for eligible papers. We provide a narrative description of the results based on a thematic analysis. In the second stage, out of papers found in the scoping review which discuss involvement and engagement strategies, we will conduct a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, exploring the delivery and effectiveness of these strategies. We will conduct a qualitative synthesis. Relevant results from the quantitative studies will be extracted and placed under qualitative themes. Individual studies will be appraised through Mixed Methods Appraisal Tool (MMAT), we will then assess the overall confidence in each finding through Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual). Results will be reported in a thematic and narrative way. ETHICS AND DISSEMINATION: This protocol sets out how the review will be conducted to ensure rigour and transparency. Public advisors were involved in its development. Ethics approval is not required. Review findings will be presented at conferences and published in peer-reviewed journals.

Enacting evidence-based practice: pathways for community nurses.

Community nurses are expected to deliver evidence-based practice, which is challenging given the diversity and breadth of the evidence base from which they can draw. This study aimed to explore community nurses' experiences of implementing change in their practice. Qualitative semi-structured interviews (n=9) and focus groups (n=2) with community nurses (n=17) were conducted. Three pathways to introduce change in practice were identified by participants: bottom-up, top-down and collaborative pathways. These are based on the nature of the proposed change, the available evidence, 'buy in' from colleagues and issues around implementation. The findings identify approaches to implementing change in community nursing practice. Practitioners would benefit from support to navigate the complex process of change through managerial support, ongoing education, accessible online resources and support through a practice development role.