Willemine Willems

Citizen engagement is often seen as a cornerstone of responsible innovation, intended to make innovation systems more responsive to diverse societal perspectives (Bauer, Bogner, and Fuchs 2021; Bra...

Complex and global emergencies such as the COVID-19 pandemic and the climate crisis have made scientific knowledge more relevant and at the same time more contested. Such contestation is amplified by widespread misinformation and a growing scepticism among certain groups in society towards science and policies that seem to be based solely on scientific research. Many have argued that the complexity of such emergencies and the contestation regarding scientifically developed solutions and policies require transdisciplinary research (TDR). In this chapter we present three major pitfalls and potential remedies to help TDR involve citizens (and other stakeholders) in a successful and meaningful way.

Background: Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish. Objective: This proof-of-concept study explored the potential of artificial intelligence (AI)–based methods to capture experiential knowledge and value considerations from existing data channels to make these insights available for public health guideline development. Methods: We developed and examined AI-based methods in relation to the COVID-19 vaccination guideline development in the Netherlands. We analyzed Dutch messages shared between December 2020 and June 2021 on social media and on 2 databases from the Dutch National Institute for Public Health and the Environment (RIVM), where experiences and questions regarding COVID-19 vaccination are reported. First, natural language processing (NLP) filtering techniques and an initial supervised machine learning model were developed to identify this type of knowledge in a large data set. Subsequently, structural topic modeling was performed to discern thematic patterns related to experiences with COVID-19 vaccination. Results: NLP methods proved to be able to identify and analyze experience-based knowledge and value considerations in large data sets. They provide insights into a variety of experiential knowledge that is difficult to obtain otherwise for rapid guideline development. Some topics addressed by citizens, patients, and professionals can serve as direct feedback to recommendations in the guideline. For example, a topic pointed out that although travel was not considered as a reason warranting prioritization for vaccination in the national vaccination campaign, there was a considerable need for vaccines for indispensable travel, such as cross-border informal caregiving, work or study, or accessing specialized care abroad. Another example is the ambiguity regarding the definition of medical risk groups prioritized for vaccination, with many citizens not meeting the formal priority criteria while being equally at risk. Such experiential knowledge may help the early identification of problems with the guideline’s application and point to frequently occurring exceptions that might initiate a revision of the guideline text. Conclusions: This proof-of-concept study presents NLP methods as viable tools to access and use experience-based knowledge and value considerations, possibly contributing to robust, equitable, and applicable guidelines. They offer a way for guideline developers to gain insights into health professionals, citizens, and patients’ experience-based knowledge, especially when conventional methods are difficult to implement. AI-based methods can thus broaden the evidence and knowledge base available for rapid guideline development and may therefore be considered as an important addition to the toolbox of pandemic preparedness.

The evolving field of reproductive technologies greatly alters our practices of conception and pregnancy. It is thus crucial to develop such innovations in a democratic and sustainable manner through public participation. To date, participation has mostly focused on patients or health professionals deliberating on technological risks, benefits, costs, and healthcare experiences. Thereby, the opportunity is missed to consider broader social and moral implications. Speculative design has been presented as a promising way to open up deliberation on the impacts of new technologies. In this study, speculative design was used to gain insight into citizens’ views and concerns about the social and moral implications of the new reproductive technologies. Six themes of concern were found: the rights of the unborn; access and equality; social implications of individual choices; society as a community; ecology; and the value of wonder. Notably, the latter two issues are not commonly described in the participatory literature on reproductive technologies, indicating that speculative design is suitable for broadening the debate by including issues that have not been addressed yet. Furthermore, the study brought insight into the motivations and complex values that lie behind arguments in which the naturalness of reproductive practices is emphasized. A point of critical reflection is that to broaden the range of reflections probed by speculative design even more, both the designers and the audiences should become more diverse. If this is achieved, it is suggested using speculative design in the context of public deliberation with a more direct influence on innovation trajectories and as a means for the public to become more skilled in critically engaging with imagined futures.

BACKGROUND Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish. OBJECTIVE This proof-of-concept study explored the potential of artificial intelligence (AI)–based methods to capture experiential knowledge and value considerations from existing data channels to make these insights available for public health guideline development. METHODS We developed and examined AI-based methods in relation to the COVID-19 vaccination guideline development in the Netherlands. We analyzed Dutch messages shared between December 2020 and June 2021 on social media and on 2 databases from the Dutch National Institute for Public Health and the Environment (RIVM), where experiences and questions regarding COVID-19 vaccination are reported. First, natural language processing (NLP) filtering techniques and an initial supervised machine learning model were developed to identify this type of knowledge in a large data set. Subsequently, structural topic modeling was performed to discern thematic patterns related to experiences with COVID-19 vaccination. RESULTS NLP methods proved to be able to identify and analyze experience-based knowledge and value considerations in large data sets. They provide insights into a variety of experiential knowledge that is difficult to obtain otherwise for rapid guideline development. Some topics addressed by citizens, patients, and professionals can serve as direct feedback to recommendations in the guideline. For example, a topic pointed out that although travel was not considered as a reason warranting prioritization for vaccination in the national vaccination campaign, there was a considerable need for vaccines for indispensable travel, such as cross-border informal caregiving, work or study, or accessing specialized care abroad. Another example is the ambiguity regarding the definition of medical risk groups prioritized for vaccination, with many citizens not meeting the formal priority criteria while being equally at risk. Such experiential knowledge may help the early identification of problems with the guideline’s application and point to frequently occurring exceptions that might initiate a revision of the guideline text. CONCLUSIONS This proof-of-concept study presents NLP methods as viable tools to access and use experience-based knowledge and value considerations, possibly contributing to robust, equitable, and applicable guidelines. They offer a way for guideline developers to gain insights into health professionals, citizens, and patients’ experience-based knowledge, especially when conventional methods are difficult to implement. AI-based methods can thus broaden the evidence and knowledge base available for rapid guideline development and may therefore be considered as an important addition to the toolbox of pandemic preparedness.

A major challenge for interdisciplinary teamwork on complex sustainability issues is the often-conflicting disciplinary perspectives and underlying values and assumptions among collaborators. Interdisciplinary collaboration among researchers and practitioners therefore requires interdisciplinary consciousness (IC): epistemological and metaphysical understanding and appreciation of one’s own and others’ disciplinary views and their differences. Since it cannot be assumed that professionals have IC, there is a need for explicit training, for instance in higher education. We developed Frame Reflection Lab (FRL)—a playful intervention to stimulate the development of IC through frame reflection—and investigated its application among cross-disciplinary student teams collaborating on sustainability issues. We aimed to understand how frame reflection can contribute to enhancing IC, by analyzing the written and oral reflections of 23 Master’s degree students. We found that the FRL intervention contributed to the development of IC as it sparked cognitive, affective, and critical reflection; created a safe space for reflection; helped participants to articulate values and assumptions; and balanced structure and freedom. Our findings demonstrate that to prepare sustainability professionals for interdisciplinary collaboration, deeply rooted and possibly unconscious preconceptions have to be challenged to build awareness and appreciation of disciplinary differences. This calls for explicitly facilitating affective processes, for instance using playfulness, whereas training in reflection and interdisciplinarity usually focuses on cognitive processes. These findings are promising in terms of informing and inspiring future efforts to use playful frame reflection in education, research, and practice to support interdisciplinary collaboration to address complex sustainability issues.

In the past decades, controversies in science and technology have fuelled the call for a research and innovation system that is open to public scrutiny and sensitive to public needs and values. These calls have been translated into a framework for responsible research and innovation (RRI). Despite its crucial role in RRI, the dimension of responsiveness is still considered to be the least conceptualized. In this paper, we analyse the mutual search for the meaning of `responsiveness' during a multi-stakeholder dialogue on responsiveness in nanotechnology innovation. We reconstructed five frames of responsiveness: science and technology, market, expert, network, and society-driven responsiveness. We highlighted and reflected on the three main strategies for dealing with differences: avoidance, polarization, and reframing. On the basis of our findings, we argue that in order to generate a collective meaning of responsiveness with a clear direction, conversations are needed in which fundamental assumptions can be questioned.

In the health sciences and policy, it is common to view rising health care costs as a tragedy of the commons, i.e., a situation in which the unhampered use of a resource by rational individuals leads to its depletion. By monitoring a set of outcomes, not only the costs but also patient experience and population health, simultaneously, it is claimed that the “triple aim” approach changes what is rational for health care stakeholders and, thus, can counter the rapidly rising health care costs. This approach has an important limitation: it reduces the monitored innovations to merely their outcomes; yet, how health care professionals and patients give shape to care delivery remains invisible. To get a more in-depth understanding of the consequences of adopting such an approach, in this article I use the method of exnovation instead. Exnovation foregrounds the everyday accomplishments of health care practices to enable reflection and learning. I draw on an ethnographic study into an innovation in care delivery aimed at rendering it more sustainable: Primary Care Plus. I reflected with both professionals and patients on what happened during 40 Primary Care Plus consultations. By presenting and analyzing three of these consultations, I foreground what is rendered invisible with the triple aim: improvisations, surprises and habits unfolding in practice. With exnovation, health care innovations can provide fertile soil for creating new forms of sustainable care that can help prevent the impending exhaustion of health care systems.