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

Autor: Teodorowski P; University of Liverpool, Liverpool, UK. p.teodorowski@liverpool.ac.uk., Gleason K; Imperial Cancer Research UK Lead Nurse, Department of Surgery and Cancer, Imperial College London, London, UK., Gregory JJ; Computational Oncology Group, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK., Martin M; School of Primary Care and Public Health, Imperial College London, London, UK., Punjabi R; Imperial College London, London, UK., Steer S; Imperial College London, London, UK., Savasir S; Imperial College London, London, UK., Vema P; Imperial College London, London, UK., Murray K; School of Public Health, Imperial College London, London, UK.; NIHR Applied Research Collaboration Northwest London, Imperial College London, London, UK., Ward H; School of Public Health, Imperial College London, London, UK.; NIHR Applied Research Collaboration Northwest London, Imperial College London, London, UK.; National Institute for Health Research Imperial Biomedical Research Centre, London, UK., Chapko D; School of Public Health, Imperial College London, London, UK.; NIHR Applied Research Collaboration Northwest London, Imperial College London, London, UK.
Jazyk: angličtina
Zdroj: Research involvement and engagement [Res Involv Engagem] 2023 Aug 14; Vol. 9 (1), pp. 67. Date of Electronic Publication: 2023 Aug 14.
DOI: 10.1186/s40900-023-00480-z
Abstrakt: 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.
(© 2023. BioMed Central Ltd., part of Springer Nature.)
Databáze: MEDLINE
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