Background Clients facing decision-making for long-term care are in need of support and accessible information. Construction of preferences, including context and calculations, for clients in long-term care is challenging because of the variability in supply and demand. This study considers clients in four different sectors of long-term care: the nursing and care of the elderly, mental health care, care of people with disabilities, and social care. The aim is to understand the construction of preferences in real-life situations. Method Client choices were investigated by qualitative descriptive research. Data were collected from 16 in-depth interviews and 79 client records. Interviews were conducted with clients and relatives or informal caregivers from different care sectors. The original client records were explored, containing texts, letters, and comments of clients and caregivers. All data were analyzed using thematic analysis. Results Four cases showed how preferences were constructed during the decision-making process. Clients discussed a wide range of challenging aspects that have an impact on the construction of preferences, e.g. previous experiences, current treatment or family situation. This study describes two main characteristics of the construction of preferences: context and calculation. Conclusion Clients face diverse challenges during the decision-making process on long-term care and their construction of preferences is variable. A well-designed tool to support the elicitation of preferences seems beneficial.
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Purpose: Collaborative deliberation comprises personal engagement, recognition of alternative actions, comparative learning, preference elicitation, and preference integration. Collaborative deliberation may be improved by assisting preference elicitation during shared decision-making. This study proposes a framework for preference elicitation to facilitate collaborative deliberation in long-term care consultations. Methods: First, a literature overview was conducted comprising current models for the elicitation of preferences in health and social care settings. The models were reviewed and compared. Second, qualitative research was applied to explore those issues that matter most to clients in long-term care. Data were collected from clients in long-term care, comprising 16 interviews, 3 focus groups, 79 client records, and 200 online client reports. The qualitative analysis followed a deductive approach. The results of the literature overview and qualitative research were combined. Results: Based on the literature overview, five overarching domains of preferences were described: “Health”, “Daily life”, “Family and friends”, ”Living conditions”, and “Finances”. The credibility of these domains was confirmed by qualitative data analysis. During interviews, clients addressed issues that matter in their lives, including a “click” with their care professional, safety, contact with loved ones, and assistance with daily structure and activities. These data were used to determine the content of the domains. Conclusion: A framework for preference elicitation in long-term care is proposed. This framework could be useful for clients and professionals in preference elicitation during collaborative deliberation.
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In the Netherlands, client and family participation in care for people with intellectual disabilities has been in vogue for a long time, and increasingly receives attention (KPMG and Vilans 2017). However, the perspective and experiential knowledge of service users and relatives is often still insuBiciently used for the co-creation of care. The professional perspective is often still dominant. In addition, professionals mainly focus on clients and less on relatives, even though relatives often play an important role in the client’s (quality of) life (Wiersma 2017). The project ‘Inclusive Collaboration in Disability Care’[1] (ICDC) focusses on enhancing equal communication between people with intellectual disabilities, their relatives, and professional caregivers, and hence contributes to redressing power imbalances in longterm care. It investigates the question: “How can the triangle of client, relative and professional caregiver together co-create better care and support?”.
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In this project, we explore how healthcare providers and the creative industry can collaborate to develop effective digital mental health interventions, particularly for survivors of sexual assault. Sexual assault victims face significant barriers to seeking professional help, including shame, self-blame, and fear of judgment. With over 100,000 cases reported annually in the Netherlands the need for accessible, stigma-free support is urgent. Digital interventions, such as chatbots, offer a promising solution by providing a safe, confidential, and cost-effective space for victims to share their experiences before seeking professional care. However, existing commercial AI chatbots remain unsuitable for complex mental health support. While widely used for general health inquiries and basic therapy, they lack the human qualities essential for empathetic conversations. Additionally, training AI for this sensitive context is challenging due to limited caregiver-patient conversation data. A key concern raised by professionals worldwide is the risk of AI-driven chatbots being misused as therapy substitutes. Without proper safeguards, they may offer inappropriate responses, potentially harming users. This highlights the urgent need for strict design guidelines, robust safety measures, and comprehensive oversight in AI-based mental health solutions. To address these challenges, this project brings together experts from healthcare and design fields—especially conversation designers—to explore the power of design in developing a trustworthy, user-centered chatbot experience tailored to survivors' needs. Through an iterative process of research, co-creation, prototyping, and evaluation, we aim to integrate safe and effective digital support into mental healthcare. Our overarching goal is to bridge the gap between digital healthcare and the creative sector, fostering long-term collaboration. By combining clinical expertise with design innovation, we seek to develop personalized tools that ethically and effectively support individuals with mental health problems.
