Introduction F-ACT is a flexible version of Assertive Community Treatment to deliver care in a changing intensity depending on needs of individuals with severe mental illnesses (Van Veldhuizen, 2007). In 2016 a number of the FACT-teams in the Dutch region of Utrecht moved to locations in neighborhoods and started to work as one network team together with neighborhood based facilities in primary care (GP’s) and in the social domain (supported living, social district teams, etc.). This should create better chances on clinical, social and personal recovery of service users. Objectives This study describes the implementation, obstacles and outcomes for service users. The main question is whether this Collaborative Mental Health Care in the Community produces better outcome than regular FACT. Measures include (met/unmet) needs for care, quality of life, clinical, functional and personal recovery, and hospital admission days. Methods Data on care utilization regarding the innovation are compared to regular FACT. Qualitative interviews are conducted to gain insight in the experiences of service users, their family members and mental health care workers. Changes in outcome measures of service users in pilot areas (N=400) were compared to outcomes of users (matched on gender and level of functioning) in regular FACT teams in the period 2015-2018 (total N=800). Results Data-analyses will take place from January to March 2019. Initial analyses point at a greater feeling of holding and safety for service users in the pilot areas and less hospital admission days. Conclusions Preliminary results support the development from FACT to a community based collaborative care service.
Stel dat vijf mensen uit verschillende domeinen en met verschillende nationaliteiten samenwerken aan een praktijkopdracht in Den Haag. Eén van de groepsleden is Guido, een ICT-student uit Italië en een andere is Marie, verpleegkundedocent van de Academie voor Gezondheid van De Haagse Hogeschool (HHS). Verder zitten Jeremy, een Nederlandse student Voeding en Diëtetiek, Indy een internationale student Social Work uit India en Marja, de Finse gastdocente, in de projectgroep. Wanneer deze mensen, vanuit verschillende kennisdomeinen en met verschillende nationaliteiten samenwerken aan een echte praktijkopdracht kunnen ze niet alleen veel van elkaar leren, maar ook de beroepspraktijk een stapje verder helpen. In dit artikel wordt het ontwerp van een internationaal global health programma van De Haagse Hogeschool gepresenteerd, waarvan de pilot is afgerond. In april verscheen het artikel 'Het ontwerpen van een internationaal global health programma' in Onderwijs en gezondheidzorg, uitgave van het kennisplatform voor opleiders in de zorg, nummer 3, april 2014, zie www.onderwijsengezondheidszorg.nl
Chronic diseases represent a significant burden for the society and health systems; addressing this burden is a key goal of the European Union policy. Health and other professionals are expected to deliver behaviour change support to persons with chronic disease. A skill gap in behaviour change support has been identified, and there is room for improvement. Train4Health is a strategic partnership involving seven European Institutions in five countries, which seeks to improve behaviour change support competencies for the self-management of chronic disease. The project envisages a continuum in behaviour change support education, in which an interprofessional competency framework, relevant for those currently practising, guides the development of a learning outcomes-based curriculum and an educational package for future professionals (today’s undergraduate students).
The results will be consensus between departments of physiotherapy universities of allied health care about learning outcomes CommunicationThere is no consensus between Dutch Physiotherapy departments on learning outcome of bachelors
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.
