Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence
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Published version
Author(s)
Type
Journal Article
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.
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.
Date Issued
2023-08-14
Date Acceptance
2023-07-31
Citation
Research Involvement and Engagement, 2023, 9, pp.1-12
ISSN
2056-7529
Publisher
BMC
Start Page
1
End Page
12
Journal / Book Title
Research Involvement and Engagement
Volume
9
Copyright Statement
© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco
mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco
mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
License URL
Identifier
https://researchinvolvement.biomedcentral.com/articles/10.1186/s40900-023-00480-z
Publication Status
Published
Article Number
67
Date Publish Online
2023-08-14