Teodorowski Piotr, Gleason Kelly, Gregory Jonathan J, Martin Martha, Punjabi Reshma, Steer Suzanne, Savasir Serdar, Vema Pournamy, Murray Kabelo, Ward Helen, Chapko Dorota
University of Liverpool, Liverpool, UK.
Imperial Cancer Research UK Lead Nurse, Department of Surgery and Cancer, Imperial College London, London, UK.
Res Involv Engagem. 2023 Aug 14;9(1):67. doi: 10.1186/s40900-023-00480-z.
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.
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.
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.
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.
数据科学和人工智能的发展为医疗保健带来了新的应用和研究可能性。患者应该能够就使用医疗保健做出明智的选择。因此，必须向他们提供有关新技术的通俗易懂的信息。一个由学术研究人员、卫生专业人员和公众贡献者组成的团队合作共同设计并开发了提供此类信息的新资源。在本文中，我们评估了这种新的共同生产方法。
我们采用参与式评估来了解共同生产过程。这包括三个阶段的创造性方法和反思性。首先，每个人都有机会参加三次在线培训课程。第一次聚焦于评估的目的，第二次聚焦于照片语音法（包括使用照片作为隐喻的实践培训），第三次聚焦于反思（在分析过程中认识到自己的偏见和观点）。在第二阶段，使用照片语音法，每个人拍摄象征他们参与项目经历的照片。这包括与专业摄影师的一次会面。在最后阶段，我们亲自见面，并利用从照片语音法收集的数据，构建曼陀罗作为项目共同经历的一种呈现。这一阶段得到了专业艺术家的支持，他们在插图中总结了曼陀罗。
曼陀罗是评估结果的艺术呈现。它是每个参与者共同的历程。我们将其分为六个相关层次。从内层开始呈现以下经历：（1）公众贡献者有空间在新主题上建立信心；（2）个人之间以及项目内部的关系；（3）在新冠疫情期间远程工作；（4）促使人们参与这项特定工作的动机；（5）共同生产需要具有包容性且让每个人都能参与的要求；（6）对研究人员应遵循以获得公众支持的数据科学和人工智能的期望。
参与式评估表明，围绕数据科学和人工智能的共同生产可以是一个由每个参与者共同拥有的有意义的过程。
