Currently, implementation research in the field of forensic risk assessment is limited and consensus on “implementation success” is lacking. This study applies outcomes of success from implementation science to the implementation evaluation of the Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) in a residential youth care facility in the Netherlands. Staff perceptions on the implementation and the instrument were assessed using 5 implementation outcomes in a longitudinal multimethod design: acceptability, adoption, appropriateness, feasibility, and penetration. As anticipated, the majority of staff perceived START:AV core constructs as useful for treatment (appropriateness). However, satisfaction with the instrument decreased over time (acceptability). This was likely due to an increased workload (feasibility). Despite this dissatisfaction, the completion rate was acceptable (adoption). Lastly, staff reported a lack of integration of the START:AV findings in clinical case conferences (penetration). The implementation outcomes aid in identifying areas for improvement, which in turn can lead to an increased and more consistent uptake of structured risk assessment into routine practice
In the Netherlands approximately 2 million inhabitants have one or more disabilities. However, just like most people they like to travel and go on holiday.In this project we have explored the customer journey of people with disabilities and their families to understand their challenges and solutions (in preparing) to travel. To get an understanding what ‘all-inclusive’ tourism would mean, this included an analysis of information needs and booking behavior; traveling by train, airplane, boat or car; organizing medical care and; the design of hotels and other accommodations. The outcomes were presented to members of ANVR and NBAV to help them design tourism and hospitality experiences or all.
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.
