This chapter explores qualitative career assessment as an identity learning process where meaning-oriented learning is essential and distinguished from conditioned or semantic types of learning. In order to construct a career identity in the form of a future-oriented narrative, it is essential that learners are helped through cognitive learning stages with the help of a dialogue about concrete experiences which aims to pay attention to emotions and broadens and deepens what is expressed.
The workforce in the EU is ageing, and this requires investment in older workers so that the organisations in which they work remain competitive and viable. One such investment takes the form of organising and facilitating intergenerational learning: learning between and among generations that can lead to lifelong learning, innovation and organisational development. However, successfully implementing intergenerational learning is complex and depends on various factors at different levels within the organisation. This multidisciplinary literature review encompasses work from the fields of cognitive psychology, occupational health, educational science, human resource development and organisational science and results in a framework that organisations can use to understand how they can create the conditions needed to ensure that the potential of their ageing workforce is tapped effectively and efficiently. Although not a comprehensive review, this chapter serves as a basis for further empirical research and gives practitioners an insight into solving a growing problem.
Lifelong learning is necessary for nurses and caregivers to provide good, person-centred care. To facilitate such learning and embed it into regular working processes, learning communities of practice are considered promising. However, there is little insight into how learning networks contribute to learning exactly and what factors of success can be found. The study is part of a ZonMw-funded research project ‘LeerSaam Noord’ in the Netherlands, which aims to strengthen the professionalization of the nursing workforce and promote person-centred care. We describe what learning in learning communities looks like in four different healthcare contexts during the start-up phase of the research project. A thematic analysis of eleven patient case-discussions in these learning communities took place. In addition, quantitative measurements on learning climate, reciprocity behavior, and perceptions of professional attitude and autonomy, were used to underpin findings. Reflective questioning and discussing professional dilemma's i.e. patient cases in which conflicting interests between the patient and the professional emerge, are of importance for successful learning.
MULTIFILE
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
Our country contains a very dense and challenging transport and mobility system. National research agendas and roadmaps of multiple sectors such as HTSM, Logistics and Agri&food, promote vehicle automation as a means to increase transport safety and efficiency. SMEs applying vehicle automation require compliance to application/sector specific standards and legislation. A key aspect is the safety of the automated vehicle within its design domain, to be proven by manufacturers and assessed by authorities. The various standards and procedures show many similarities but also lead to significant differences in application experience and available safety related solutions. For example: Industrial AGVs (Automated Guided Vehicles) have been around for many years, while autonomous road vehicles are only found in limited testing environments and pilots. Companies are confronted with an increasing need to cover multiple application environments, such restricted areas and public roads, leading to complex technical choices and parallel certification/homologation procedures. SafeCLAI addresses this challenge by developing a framework for a generic safety layer in the control of autonomous vehicles that can be re-used in different applications across sectors. This is done by extensive consolidation and application of cross-sectoral knowledge and experience – including analysis of related standards and procedures. The framework promises shorter development times and enables more efficient assessment procedures. SafeCLAI will focus on low-speed applications since they are most wanted and technically best feasible. Nevertheless, higher speed aspects will be considered to allow for future extension. SafeCLAI will practically validate (parts) of the foreseen safety layer and publish the foreseen framework as a baseline for future R&D, allowing coverage of broader design domains. SafeCLAI will disseminate the results in the Dutch arena of autonomous vehicle development and application, and also integrate the project learnings into educational modules.
SHAREHOUSE is een ruimte voor bedrijven in het STC om te experimenteren met eigen technologie. De experimenten worden wetenschappelijk gestroomlijnd en gericht op dataverzameling ter verbetering van magazijnwerk. Moderne technologieën, mens-technologie interactie, technologieadoptie en sociale innovatie, veiligheid, ethiek en duurzaamheid en de benodigde skills voor (toekomstige) medewerkers in de logistiek staan hierin centraal. SHAREHOUSE creëert tevens een open leeromgeving voor studenten en (MKB-)bedrijven, zodat ze in een praktijkomgeving ervaren hoe zij automated guided vehicles, virtual/augmented reality en wearables en exoskeletten voor goederenverwerking in een magazijn kunnen implementeren en beheersen. Publiek-private learning communities zorgen voor duurzame samenwerking tussen de belangrijkste stakeholders.
