The number of social enterprises in the Netherlands has increased rapidly. Social enterprises are looking for new, innovative and economically sustainable ways to tackle structural societal challenges that generally fall outside the direct focus and objectives of the public and private sector. Social enterprises are primarily mission-driven, where profit is not a goal in itself but a means of creating societal impact with regard to a specific social problem. Many social enterprises aim to increase their societal impact by growing their organization. However, despite their ambition, scaling up and expanding their impact remains challenging in practice. This research aimed to identify the main constraining factors in scaling up social enterprises and to develop effective methods to tackle these barriers in order to achieve more societal impact. The research was conducted among twenty social enterprises in the Netherlands, all of which aim to stimulate the labor market participation of people who are at a distance from the labor market, generally referred to as work-integration social enterprises. The results show that the majority of the participating social enterprises succeeded in achieving growth in the past two years with regard to specific indicators, but generally not in the way they had originally planned.
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We need educators to be constantly updating their skills and knowledge, and evidenced-informed practice is central to this; yet it is far from universal in our schools. Kristin Vanlommel and Chris Brown draw on their international research to show how EIP can be achieved based on three core principles. With this article, we consider the engagement by teachers and school leaders in educational practices that are ‘evidence-informed’ - across school systems and world-wide. There is a growing consensus that effective teaching and leadership is based on evidence-informed practice (or EIP), and that EIP results in improving student learning and achievement.
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Om goede en verantwoorde zorg te kunnen bieden, streeft de paramedicus naar evidence-based practice. Evidence-based practice is het zorgvuldig, expliciet en oordeelkundig gebruik van het huidige beste bewijsmateriaal en evidence om beslissingen te nemen met individuele patiënten om de zorgverlening te verbeteren. De praktijk van evidence-based practice impliceert het integreren van individuele professionele kennis van de paramedicus met de wens en voorkeur van de patiënt en het beste externe bewijsmateriaal dat vanuit systematisch onderzoek beschikbaar is. De voorkeuren, wensen en verwachtingen van de patiënt spelen bij de besluitvorming een centrale rol.
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.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
