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.
Background: The need for effective continuing education is especially high in in-hospital geriatric care, as older patients have a higher risk of complications, such as falls. It is important that nurses are able to prevent them. However, it remains unknown which interventions change the behavior of nurses. Therefore, the aim of this study is to identify intervention options to change the behavior of hospital nurses regarding fall prevention among older hospitalized patients. Methods: This study used a mixed method design. The Behavior Change Wheel (BCW) was used to identify intervention functions and policy categories to change the behavior of nurses regarding fall prevention. This study followed the eight steps of the BCW and two methods of data collection were used: five focus groups and three Delphi rounds. The focus groups were held with hospital nurses (n = 26). Geriatric experts (n = 11), managers (n = 13) and educators (n = 13) were included in the Delphi rounds. All data were collected within ten tertiary teaching hospitals in the Netherlands. All participants were included based on predefined in- and exclusion criteria and availability. Results: In Geriatric experts’ opinions interventions targeting behavior change of nurses regarding fall prevention should aim at ‘after-care’, ‘estimating fall risk’ and ‘providing information’. However, in nurses’ opinions it should target; ‘providing information’, ‘fall prevention’ and ‘multifactorial fall risk assessment’. Nurses experience a diversity of limitations relating to capability, opportunity and motivation to prevent fall incidents among older patients. Based on these limitations educational experts identified three intervention functions: Incentivisation, modelling and enablement. Managers selected the following policy categories; communication/marketing, regulation and environmental/social planning. Conclusions: The results of this study show there is a discrepancy in opinions of nurses, geriatric experts, managers and educators. Further insight in the role and collaboration of managers, educators and nurses is necessary for the development of education programs strengthening change at the workplace that enable excellence in nursing practice. DOI: https://doi.org/10.1186/s12912-021-00598-z
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This study provides a comprehensive analysis of the AI-related skills and roles needed to bridge the AI skills gap in Europe. Using a mixed-method research approach, this study investigated the most in-demand AI expertise areas and roles by surveying 409 organizations in Europe, analyzing 2,563 AI-related job advertisements, and conducting 24 focus group sessions with 145 industry and policy experts. The findings underscore the importance of both general technical skills in AI related to big data, machine learning and deep learning, cyber and data security, large language models as well as AI soft skills such as problemsolving and effective communication. This study sets the foundation for future research directions, emphasizing the importance of upskilling initiatives and the evolving nature of AI skills demand, contributing to an EU-wide strategy for future AI skills development.
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