Anaesthesiology residents at Leiden University Medical Center regularly undergo simulation training with a full-body manikin. This is a vital aspect of the clinical programme providing a stressful yet safe environment for effective critical resource management (CRM) training. Unfortunately, the COVID-19 pandemic made real-life simulations challenging due to organizational and preventive measures. As a result, we explored asynchronous training opportunities utilizing a multiplayer virtual reality (VR) simulation. VR simulations can create personalized scenarios, facilitating differentiated learning through enhanced sensory immersion. VR offers full immersion with a high potential for visual effects, simultaneously allowing changes in patient characteristics such as sex, weight, external trauma and age, which is impossible with regular manikin training. The three-step approach involved (1) identifying user requirements, (2) developing a prototype and (3) assessing the projectandapos;s viability and interest for expansion.
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This empirical study investigated the relationship between the learning climate and training participation in Dutch organizations and how subsidies and the sharing of investments in time and costs between employers and employees affect this relationship. Our analyses are based on a survey of a representative sample of 512 organizations with at least five employees in a Dutch region. Respondents replied to five statements to measure the learning climate, while training was measured through participation and intensity. We found that an organization’s learning climate is positively related to participation and intensity of training in terms of hours. However, we observe that the effect of learning climate on the number of hours of training decreases when the employer pays the costs for the training and when the training takes place during working hours. When organizations can use government subsidies, participation in training increases, and the number of hours per participant increases.
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Purpose - Focusing on management training, this study aimed to establish whether identical elements in a training program (i.e. aspects resembling participants' work situation) can improve training transfer and whether they do so beyond the contribution of two well-established predictors -- motivation to learn and expected utility. In an effort to establish mechanisms connecting identical elements with training transfer, we proposed and tested motivation to transfer as a mediator. Design/methodology/approach - Data were collected online from 595 general managers who participated in a management training program. Structural equations modeling was used to test the model. Findings - Identical elements, expected utility and motivation to learn each had a unique contribution to the prediction of training transfer. Whereas motivation to learn partly mediated these relationships, identical elements and expected utility also showed direct associations with training transfer. Research limitations/implications - Identical elements represent a relevant predictor of training transfer. In future research, a longitudinal analysis from different perspectives would be useful to better understand the process of training transfer. Practical implications - Participants may profit more from management training programs when the training better resembles participants' work situation. Organisations and trainers should therefore apply the concept of identical elements in their trainings, in order to increase its value and impact. Originality/value - This study contributes to the training literature by showing the relevance of identical elements for transfer, over and above established predictors.
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In recent years, disasters are increasing in numbers, location, intensity and impact; they have become more unpredictable due to climate change, raising questions about disaster preparedness and management. Attempts by government entities at limiting the impact of disasters are insufficient, awareness and action are urgently needed at the citizen level to create awareness, develop capacity, facilitate implementation of management plans and to coordinate local action at times of uncertainty. We need a cultural and behavioral change to create resilient citizens, communities, and environments. To develop and maintain new ways of thinking has to start by anticipating long-term bottom-up resilience and collaborations. We propose to develop a serious game on a physical tabletop that allows individuals and communities to work with a moderator and to simulate disasters and individual and collective action in their locality, to mimic real-world scenarios using game mechanics and to train trainers. Two companies–Stratsims, a company specialized in game development, and Society College, an organization that aims to strengthen society, combine their expertise as changemakers. They work with Professor Carola Hein (TU Delft), who has developed knowledge about questions of disaster and rebuilding worldwide and the conditions for meaningful and long-term disaster preparedness. The partners have already reached out to relevant communities in Amsterdam and the Netherlands, including UNUN, a network of Ukrainians in the Netherlands. Jaap de Goede, an experienced strategy simulation expert, will lead outreach activities in diverse communities to train trainers and moderate workshops. This game will be highly relevant for citizens to help grow awareness and capacity for preparing for and coping with disasters in a bottom-up fashion. The toolkit will be available for download and printing open access, and for purchase. The team will offer training and facilitate workshops working with local communities to initiate bottom-up change in policy making and planning.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
