De Regiegroep van de topsector Life Sciences & Health wil een impuls geven aan initiatieven die praktijkgericht onderzoek op het gebied van Health betreffen. De redenen hiervoor zijn de relatief bescheiden positie van Health vergeleken bij de Life Sciences in de eerdere agendering onder de topsector en de verwachting dat praktijkgericht onderzoek door hogescholen een substantiële bijdrage kan leveren aan de doelstellingen onder het topsectorenbeleid. Daarom is opdracht gegeven tot het opstellen van een agenda voor praktijkgericht onderzoek “Health”. Deze agenda moet leiden tot samenwerking met een solide economische component tussen hogescholen, eventuele andere kennisinstellingen en publieke en private partijen uit de beroepspraktijk. De Agenda Praktijkgericht Onderzoek Health is ingedeeld in vier overkoepelende thema’s (A - D) waarop het onderzoek van hogescholen zich zou moeten richten. Binnen elk thema zijn onderwerpen benoemd die op basis van deze verkenning prioriteit verdienen.
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For students who want support to continue their education.
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Binnen de nieuwe opleiding Social Work van de Hogeschool Utrecht is gehoor gegeven aan de toenemende wens van studenten om meer te doen met eigen ervaringen met psychische kwetsbaarheid. Deze wens is onder meer vertaald in een peersupportgroep voor studenten, die in de periode maart t/m juni in 2018 en 2019 liep. Veel studenten beschikken over een behoorlijk potentieel aan ervaringskennis wat door middel van peer support in een veilige setting kan worden verkend. Deelnemers worden zich bewust van dit potentieel door hierop met elkaar reflecteren en (verder) te ontwikkelen. Een peer support groep werkt taboedoorbrekend en biedt ondersteuning aan studenten met een psychische kwetsbaarheid. Peer support ondersteunt ook aankomend professionals gebruik te maken van eigen kwetsbaarheid. Voor veel (aankomend) hulpverleners was het tot voor kort ongebruikelijk om dit te doen. Intussen worden de verhoudingen tussen cliënt en hulpverlener anders gedefinieerd en richt de (herstelgerichte) zorg zich steeds nadrukkelijker op destigmatisering, de inzet van ervaringsdeskundigheid, gelijkwaardigheid en openheid in de begeleidingsrelatie. Peer support-programma’s worden steeds vaker geïmplementeerd in (zorg)organisaties om mensen te helpen omgaan met problemen, maar spelen ook in de beroepsontwikkeling van aankomend sociaal werkers een belangrijke rol. Deze rapportage is een samenvoeging van een eerdere interne rapportage van de peer supportgroep uit 2018 (Leunen; Lamers & Van Slagmaat, 2018) en een evaluatie van de peer supportgroep in 2019.
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As the Dutch population is aging, the field of music-in-healthcare keeps expanding. Healthcare, institutionally and at home, is multiprofessional and demands interprofessional collaboration. Musicians are sought-after collaborators in social and healthcare fields, yet lesser-known agents of this multiprofessional group. Although live music supports social-emotional wellbeing and vitality, and nurtures compassionate care delivery, interprofessional collaboration between musicians, social work, and healthcare professionals remains marginal. This limits optimising and integrating music-making in the care. A significant part of this problem is a lack of collaborative transdisciplinary education for music, social, and healthcare students that deep-dives into the development of interprofessional skills. To meet the growing demand for musical collaborations by particularly elderly care organisations, and to innovate musical contributions to the quality of social and healthcare in Northern Netherlands, a transdisciplinary education for music, physiotherapy, and social work studies is needed. This project aims to equip multiprofessional student groups of Hanze with interprofessional skills through co-creative transdisciplinary learning aimed at innovating and improving musical collaborative approaches for working with vulnerable, often older people. The education builds upon experiential learning in Learning LABs, and collaborative project work in real-life care settings, supported by transdisciplinary community forming.The expected outcomes include a new concept of a transdisciplinary education for HBO-curricula, concrete building blocks for a transdisciplinary arts-in-health minor study, innovative student-led approaches for supporting the care and wellbeing of (older) vulnerable people, enhanced integration of musicians in interprofessional care teams, and new interprofessional structures for educational collaboration between music, social work and healthcare faculties.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
