Praktijkonderzoek over integratie van practice based evidence en evidence based practice, nderzoeksresultaten toepassen in de praktijk,verbeteren in en door de praktijk.
DOCUMENT
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.
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Om tegemoet te komen aan de eisen die gesteld worden aan werknemers in de huidig snel veranderende samenleving heeft de NHL Stenden Hogeschool gekozen voor een nieuw onderwijsconcept, namelijk Design Based Education (DBE). DBE is gebaseerd op het gedachtegoed van Design Thinking en stimuleert iteratieve en creatieve denkprocessen. DBE is een student-georiënteerde leeromgeving, gebaseerd op praktijk-, dialoog-, en vraaggestuurde onderwijsprincipes en op zelfsturend, constructief, contextueel en samenwerkend leren. Studenten construeren gezamenlijk kennis en ontwikkelen een prototype voor een praktijkvraagstuk. Student-georiënteerde leeromgevingen vragen andere begeleidingsstrategieën van docenten dan zij gewend zijn. Van docenten wordt verwacht dat zij studenten activeren gezamenlijk kennis te construeren en dat zij nauw samenwerken met werkveldprofessionals. Eerder onderzoek toont aan dat docenten, zelfs in een student-georiënteerde leeromgeving, geneigd zijn terug te vallen op conventionele strategieën. De overstap naar een ander onderwijsconcept gaat dus blijkbaar niet vanzelf. Collectief leren stimuleert docenten de dialoog aan te gaan met andere docenten en werkveldprofessionals met als doel gezamenlijk te experimenteren en collectief te handelen. De centrale vraag van het postdoc-onderzoek is het ontwerpen en ontwikkelen van (karakteristieken van) interventies die collectief leren van docenten en werkveldprofessionals stimuleren. Het doel van het postdoconderzoek is om de overstap naar DBE zo probleemloos mogelijk te laten verlopen door docenten te ondersteunen DBE leeromgevingen te ontwikkelen in samenwerking met werkveldprofessionals en DBE te integreren in hun docentactiviteiten. De onderzoeksmethode is Educational Design Research en bestaat uit vier fasen: preliminair onderzoek, ontwikkelen van prototypes, evaluatie en bijdrage aan de praktijk. Het onderzoek is verbonden aan het lectoraat Sustainable Educational Concepts in Higher Education en wordt hiërarchisch en inhoudelijk aangestuurd door de lector. Docenten, experts, werkveldprofessionals en studenten worden betrokken bij het onderzoek. Dit onderzoek kan zowel binnen als buiten de hogeschool een bijdrage leveren omdat steeds meer hogescholen kiezen voor een ander onderwijsconcept.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
Youth care is under increasing pressure, with rising demand, longer waiting lists, and growing staff shortages. In the Netherlands, one in seven children and adolescents is currently receiving youth care. At the same time, professionals face high workloads, burnout risks, and significant administrative burdens. This combination threatens both the accessibility and quality of care, leading to escalating problems for young people and families. Artificial intelligence (AI) offers promising opportunities to relieve these pressures by supporting professionals in their daily work. However, many AI initiatives in youth care fail to move beyond pilot stages, due to barriers such as lack of user acceptance, ethical concerns, limited professional ownership, and insufficient integration into daily practice. Empirical research on how AI can be responsibly and sustainably embedded in youth care is still scarce. This PD project aims to develop practice-based insights and strategies that strengthen the acceptance and long-term adoption of AI in youth care, in ways that support professional practice and contribute to appropriate care. The focus lies not on the technology itself, but on how professionals can work with AI within complex, high-pressure contexts. The research follows a cyclical, participatory approach, combining three complementary implementation frameworks: the Implementation Guide (Kaptein), the CFIR model (Damschroder), and the NASSS-CAT framework (Greenhalgh). Three case studies serve as core learning environments: (1) a speech-to-text AI tool to support clinical documentation, (2) Microsoft Copilot 365 for organization-wide adoption in support teams, and (3) an AI chatbot for parents in high-conflict divorces. Throughout the project, professionals, clients, ethical experts, and organizational stakeholders collaborate to explore the practical, ethical, and organizational conditions under which AI can responsibly strengthen youth care services.
