Purpose: The aims of this study were to investigate how a variety of research methods is commonly employed to study technology and practitioner cognition. User-interface issues with infusion pumps were selected as a case because of its relevance to patient safety. Methods: Starting from a Cognitive Systems Engineering perspective, we developed an Impact Flow Diagram showing the relationship of computer technology, cognition, practitioner behavior, and system failure in the area of medical infusion devices. We subsequently conducted a systematic literature review on user-interface issues with infusion pumps, categorized the studies in terms of methods employed, and noted the usability problems found with particular methods. Next, we assigned usability problems and related methods to the levels in the Impact Flow Diagram. Results: Most study methods used to find user interface issues with infusion pumps focused on observable behavior rather than on how artifacts shape cognition and collaboration. A concerted and theorydriven application of these methods when testing infusion pumps is lacking in the literature. Detailed analysis of one case study provided an illustration of how to apply the Impact Flow Diagram, as well as how the scope of analysis may be broadened to include organizational and regulatory factors. Conclusion: Research methods to uncover use problems with technology may be used in many ways, with many different foci. We advocate the adoption of an Impact Flow Diagram perspective rather than merely focusing on usability issues in isolation. Truly advancing patient safety requires the systematic adoption of a systems perspective viewing people and technology as an ensemble, also in the design of medical device technology.
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Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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At the science - policy interface there are several reasons to combine models with the participatory process to facilitate the complex policy making process but the communication of the two sides is often too hard to generate any meaningful results. In this paper we argue that to close the communication gap the rationale of the Meta - rule of complex policy making needs to be comprehended and coped with. Gaming as a participatory method can be used to organize the combined process. Through the literature review we summarize the principles of gaming and use them to analyze an empirical case where stakeholders participated in a water policy making process. A computer model called the Planning Kit Blokkendoos (PKB, in English: Box of Blocks) was used here to support the participatory process and is claimed to have had a marked impact on the complex policy making process. We conclude that the PKB tool provided the stakeholders with significant 'room to play' with the various policy alternatives and interweaved with the policy process.
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Editorial on the Research Topic "Leveraging artificial intelligence and open science for toxicological risk assessment"
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his paper develops a new, broader, and more realistic lens to study (lacking) linkages between government policy and school practices. Drawing on recent work in organization theory, we advance notions on cluster of organization routines and the logic of complementarities underlying organizational change. This lens allows looking at how schools do (not) change a cluster of organization routines in response to multiple, simultaneous demands posed by government policies. Thirteen purposively selected Dutch secondary schools responding to three central government policies calling for concurrent change were analyzed, taking the schedule of a school as an exemplary case of a cluster of organization routines. Five distinct responses were distinguished, which can be sorted according to their impact on the whole organization. The study fnds that ten of the thirteen schools did not change anything in response to at least one of the three policies we studied. However, all schools changed their cluster of organization routines, which impacted the whole organization in response to at least one of the three government policies. Therefore, looking at combinations of responses and considering the impact of change on school organizations qualifes ideas about schools being resistant to policy or unwilling to change and improve.
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The role of expert knowledge of the environment in decision-making about urban development has been intensively debated, largely in terms of a so-called ‘science-policy gap’. Most contributions to this debate have studied the use of knowledge in the decision-making process from the knowledge providers’ point of view. In this paper, we reverse the perspective and try to unearth how decision-makers use scientific knowledge in decision-making about an urban plan. We confronted municipal administrators, responsible for local urban development, with conceptions of the use of knowledge that were derived from the literature on this issue. From the reactions obtained, we conclude that, in the context of urban redevelopment, local administrators hardly perceive a barrier between themselves as decision-makers and experts – both environmental scientists and urban designers. They do, however, acknowledge that experts and decision-makers have distinct roles: unlike experts, local administrators have to balance all interests relevant to an urban plan. It is argued, therefore, that experts should engage in providing better decision frameworks rather than more or better knowledge.
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This chapter will be part of a monograph on social inclusion, the interface between leisure and work in relation to people with intellectual disabilities.
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Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.
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Design education has a nuanced relationship with examples. Although they are considered useful teaching tools, their use is often restricted to illustrating the design theories and principles around which the curriculum is structured. In contrast, professional designers view examples as autonomous entities and use them to initiate a critical dialogue with their current problem space. Therefore, students should be facilitated in cultivating their own repertoire of solutions and learn to initiate conversations between existing solutions and design challenges to gain a better understanding of the problem space and generate new designs. This paper outlines a small-scale experiment conducted with master's students in Applied Data Science at Utrecht University who took a course on designing recommender system interfaces. The students were provided with a set of examples of recommender interface designs as their main instructional tool. They could use this set to curate their own solution repertoire. As a result, the majority of the participants' work displayed more diverse designs, and they used design patterns distilled from those examples generatively, developing innovative designs. Based on this case study, we tentatively conclude that a design curriculum built around examples, complemented by theories, could be advantageous, as long as special attention is given to helping students initiate fruitful iterations between their challenges and a set of solutions.
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In flexible education, recommender systems that support course selection, are considered a viable means to help students in making informed course selections, especially where curricula offer greater flexibility. However, these recommender systems present both potential benefits and looming risks, such as overdependence on technology, biased recommendations, and privacy issues. User control mechanisms in recommender interfaces (or algorithmic affordances) might offer options to address those risks, but they have not been systematically studied yet. This paper presents the outcomes of a design session conducted during the INTERACT23 workshop on Algorithmic Affordances in Recommender Interfaces. This design session yielded insights in how the design of an interface, and specifically the algorithmic affordances in these interfaces, may address the ethical risks and dilemmas of using a recommender in such an impactful context by potentially vulnerable users. Through design and reflection, we discovered a host of design ideas for the interface of a flexible education interface, that can serve as conversation starters for practitioners implementing flexible education. More research is needed to explore these design directions and to gain insights on how they can help to approximate more ethically operating recommender systems.
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