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ABSTRACT 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.

BackgroundThe success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research. ObjectiveThis review aims to synthesize the evidence on public involvement and engagement in big data research. MethodsThis scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review. ResultsA total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research. ConclusionsThis review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research. International Registered Report Identifier (IRRID)RR2-https://doi.org/10.1136/bmjopen-2021-050167

Abstract 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.

Abstract 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.

Abstract 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.

Objectives Public 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. Methods Following 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. Results Fifty-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. Conclusion Despite 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.

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.

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. ObjectiveThis 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. MethodsPublic 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. ResultsOf 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. ConclusionsThe 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.

Abstract The purpose of the study was to carry out the molecular surveillance of piroplasmosis, granulocytic anaplasmosis and lyme borreliosis in horses which originated from Poland and exhibited symptoms raising the suspicion of the aforementioned disease units. The presence of Theileria equi genetic material was detected in 37 out of 512 examined horses (7.2%), and Anaplasma phagocytophilum in 9 (1.8%). The DNA of Borrelia burgdorferi was found in 11 out of 204 examined horses (5.4%). The above‐cited results indicate that the problem of tick‐borne diseases affecting horses in Poland is not as significant as in other parts of Europe, however they have to be considered in differential diagnosis of the diseases with lethargy, fever, anaemia and thrombocytopenia.

The aim of the study was to demonstrate a link between uncomplicated Babesia canis infection in dogs and blood concentrations of zinc and copper and erythrocytic antioxidant defence – activities of glutathione (GSH), superoxide dismutase (SOD) and catalase (CAT).

Objectives Public 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. Approach We 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. Results The 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. Conclusions The 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.

Abstract Background Anaplasma are obligate intracellular bacteria and aetiological agents of tick-borne diseases of both veterinary and medical interest. The genus Anaplasma comprises six species: Anaplasma marginale, Anaplasma centrale, Anaplasma ovis, Anaplasma phagocytophilum, Anaplasma bovis and Anaplasma platys. They can infect humans, carnivores, ruminants, rodents, insectivores, birds and reptiles. The aim of this study was to present the first clinical case of granulocytic anaplasmosis in a captive ring-tailed lemur in Poland. Case presentation A 4-year-old female lemur presented anorexia, epistaxis and tick infestation. The microscopic examination of a blood smear revealed morulae in neutrophils. Polymerase chain reaction test and sequencing of obtained PCR product confirmed infection by the GU183908 Anaplasma phagocytophilum strain. Therapeutic protocol included doxycycline (2.5 mg/kg p.o., b.i.d.) for 3 weeks and the lemur recovered within 24 h. Conclusions This is the first report on granulocytic anaplasmosis in a ring-tailed lemur in Europe, indicating that A. phagocytophilum infection must also be considered in differential diagnosis in this animal species, especially in individuals with thrombocytopenia associated with Ixodes ricinus parasitism.

Wild ungulates may serve as reservoirs for particular tick-borne pathogens (TBP) of importance for public and animal health. The aim of the study was to investigate, for the first time, the occurrence of Anaplasma phagocytophilum (AP) in a large population of roe deer in eastern Poland. Spleen samples from 424 roe deer ( Capreolus capreolus ) were collected during the 2018–2019 hunting season. Genetic screening for AP was carried out by PCR amplification of a fragment of the 16S rRNA and groEL genes. Twenty-six of the 424 spleen samples (6.13%) tested positive both for 16S rRNA and groEL. Phylogenetic analysis confirmed the existence of a specific roe deer ecotype of AP in eastern Poland. Despite the low prevalence of AP in roe deer populations in the study area, these animals may act as anaplasmosis reservoirs for ticks. Based on the zoonotic potential of granulocytic anaplasmosis, it seems advisable to introduce continuous monitoring of this infection among domestic, farm and wild animals.

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.

Canine babesiosis is a disease caused by infection with parasites of the genus Babesia. These protozoa are classified as either large (e.g., Babesia canis) or small (e.g., Babesia gibsoni). So far, only three small Babesia species of clinical importance, able to infect dogs, have been described: B. gibsoni, B. conradae, and B. vulpes. This review presents the current epidemiological situation of Babesia gibsoni infections in dogs in Europe. In most European countries where B. gibsoni has been reported, the percentage of infected dogs is around 1%. The higher prevalence of the B. gibsoni infection among American Pit Bull Terriers suggests breed susceptibility. An analysis of the available data makes it possible to conclude that B. gibsoni infections may appear in the future in other non-endemic regions of Europe, which may pose significant diagnostic and therapeutic challenges for veterinary practitioners.

Liberal states simultaneously pursue policies of encouraging and controlling (un)desired immigration. Forces of representative democracy, nationhood, constitutionalism, and capitalism - each call for a distinct migration strategy. Previous research focusing on attitudes towards migration used quantitative methods examining values and perceptions that influence people's opinions. Still, it did not explore the diversity and complexity of sentiments. This paper aims to provide a more nuanced perspective based on tweets on and around the last day of the British membership in the European Union (31 January 2020). Data were collected using NCapture - a web-browser extension that downloaded tweets with hashtags #Brexit, #BrexitDay, and #BrexitEve, and imported them directly to NVivo. Seven batches of tweets were captured on 30-31 January and 1, 7-10 February; extracting 250,095 published between 23 January and 10 February. All retweets, duplicates, non-English tweets, and spam were removed, leaving 888 tweets for the analysis. The dataset was coded twice, assigning sentiments towards Brexit as positive (n = 203), negative (n = 586), or neutral (n = 99), and using inductive thematic analysis. The findings showed the division of discourse on migration was more complicated than merely in favor and against immigration. Interestingly, they also exhibited the shift in the British debate from benefits and drawbacks of immigration to the reciprocity of migration policies in the future relations between the United Kingdom and the European Union.

Wild ungulates may serve as reservoirs for particular tick-borne pathogens (TBP) of importance for public and animal health. The aim of the study was to investigate, for the first time, the occurrence of Anaplasma phagocytophilum (AP) in a large population of roe deer in eastern Poland. Spleen samples from 424 roe deer (Capreolus capreolus) were collected during the 2018–2019 hunting season. Genetic screening for AP was carried out by PCR amplification of a fragment of the 16S rRNA and groEL genes. Twenty-six of the 424 spleen samples (6.13%) tested positive both for 16S rRNA and groEL. Phylogenetic analysis confirmed the existence of a specific roe deer ecotype of AP in eastern Poland. Despite the low prevalence of AP in roe deer populations in the study area, these animals may act as anaplasmosis reservoirs for ticks. Based on the zoonotic potential of granulocytic anaplasmosis, it seems advisable to introduce continuous monitoring of this infection among domestic, farm and wild animals. Wild ungulates may serve as reservoirs for particular tick-borne pathogens (TBP) of importance for public and animal health. The aim of the study was to investigate, for the first time, the occurrence of Anaplasma phagocytophilum (AP) in a large population of roe deer in eastern Poland. Spleen samples from 424 roe deer (Capreolus capreolus) were collected during the 2018–2019 hunting season. Genetic screening for AP was carried out by PCR amplification of a fragment of the 16S rRNA and groEL genes. Twenty-six of the 424 spleen samples (6.13%) tested positive both for 16S rRNA and groEL. Phylogenetic analysis confirmed the existence of a specific roe deer ecotype of AP in eastern Poland. Despite the low prevalence of AP in roe deer populations in the study area, these animals may act as anaplasmosis reservoirs for ticks. Based on the zoonotic potential of granulocytic anaplasmosis, it seems advisable to introduce continuous monitoring of this infection among domestic, farm and wild animals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research., Objective: This review aims to synthesize the evidence on public involvement and engagement in big data research., Methods: This scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review., Results: A total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research., Conclusions: This review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research., International Registered Report Identifier (irrid): RR2-https://doi.org/10.1136/bmjopen-2021-050167., (©Piotr Teodorowski, Elisa Jones, Naheed Tahir, Saiqa Ahmed, Sarah E Rodgers, Lucy Frith. Originally published in Journal of Participatory Medicine (https://jopm.jmir.org), 16.08.2024.)

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., (© 2024 The Author(s). Health Expectations published by John Wiley & Sons Ltd.)

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., Competing Interests: Statement on conflicts of Interest: None declared

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., (© 2023 The Authors. Journal of Advanced Nursing published by John Wiley & Sons Ltd.)

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., (© 2023 The Authors. Health Expectations published by John Wiley & Sons Ltd.)

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)