Deteriorated functioning is a part of the clinical-high risk (CHR) criteria for psychosis. Diminished social, educational and occupational functioning in the phase of late adolescence and early adulthood are associated with long-term social, economic and health consequences, which stresses the importance of early intervention to stimulate functioning. This pilot study examines the effectiveness and feasibility of the choose-get-keep model of Supported Education and Supported Employment (SEE) to improve educational and occupational functioning of individuals at CHR for a psychosis. A single blind randomized controlled pilot study combined SEE with treatment as usual (TAU) versus TAU among adolescents and early adults at CHR. School performance and job status as well as global functioning scales were assessed at twelve months. Of the 78 eligible participants, 20 individuals consented to participate in this study. At follow-up, participants in the intervention condition (n = 9) did not start an education more often than the participants in the control condition (n = 11) and the school results for both conditions were similar. However, in the intervention condition there were no school dropouts, more participants gained a job and worked longer hours. Two participants quit the intervention. This pilot study provides preliminary evidence that a SEE intervention is effective and feasible in sustaining and improving the level of both educational and occupational functioning of individuals at CHR for psychosis by supporting them in attaining, keeping and elaborating of their education or employment.
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Background: Multimodal prehabilitation programs are effective at reducing complications after colorectal surgery in patients with a high risk of postoperative complications due to low aerobic capacity and/or malnutrition. However, high implementation fidelity is needed to achieve these effects in real-life practice. This study aimed to investigate the implementation fidelity of an evidence-based prehabilitation program in the real-life context of a Dutch regional hospital.Methods: In this observational cohort study with multiple case analyses, all patients who underwent colorectal surgery from January 2023 to June 2023 were enrolled. Patients meeting the criteria for low aerobic capacity or malnutrition were advised to participate in a prehabilitation program. According to recent scientific insights and the local care context, this program consisted of four exercise modalities and three nutrition modalities. Implementation fidelity was investigated by evaluating: (1) coverage (participation rate), (2) duration (number of days between the start of prehabilitation and surgery), (3) content (delivery of prescribed intervention modalities), and (4) frequency (attendance of sessions and compliance with prescribed parameters). An aggregated percentage of content and frequency was calculated to determine overall adherence.Results: Fifty-eight patients intended to follow the prehabilitation care pathway, of which 41 performed a preoperative risk assessment (coverage 80%). Ten patients (24%) were identified as high-risk and participated in the prehabilitation program (duration of 33-84 days). Adherence was high (84-100%) in five and moderate (72-73%) in two patients. Adherence was remarkably low (25%, 53%, 54%) in three patients who struggled to execute the prehabilitation program due to multiple physical and cognitive impairments.Conclusion: Implementation fidelity of an evidence-based multimodal prehabilitation program for high-risk patients preparing for colorectal surgery in real-life practice was moderate because adherence was high for most patients, but low for some patients. Patients with low adherence had multiple impairments, with consequences for their preparation for surgery. For healthcare professionals, it is recommended to pay attention to high-risk patients with multiple impairments and further personalize the prehabilitation program. More knowledge about identifying and treating high-risk patients is needed to provide evidence-based recommendations and to obtain higher effectiveness.
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There is emerging evidence that the performance of risk assessment instruments is weaker when used for clinical decision‐making than for research purposes. For instance, research has found lower agreement between evaluators when the risk assessments are conducted during routine practice. We examined the field interrater reliability of the Short‐Term Assessment of Risk and Treatability: Adolescent Version (START:AV). Clinicians in a Dutch secure youth care facility completed START:AV assessments as part of the treatment routine. Consistent with previous literature, interrater reliability of the items and total scores was lower than previously reported in non‐field studies. Nevertheless, moderate to good interrater reliability was found for final risk judgments on most adverse outcomes. Field studies provide insights into the actual performance of structured risk assessment in real‐world settings, exposing factors that affect reliability. This information is relevant for those who wish to implement structured risk assessment with a level of reliability that is defensible considering the high stakes.
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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.
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.
Over a million people in the Netherlands have type 2 diabetes (T2D), which is strongly related to overweight, and many more people are at-risk. A carbohydrate-rich diet and insufficient physical activity play a crucial role in these developments. It is essential to prevent T2D, because this condition is associated with a reduced quality of life, high healthcare costs and premature death due to cardiovascular diseases. The hormone insulin plays a major role in this. This hormone lowers the blood glucose concentration through uptake in body cells. If an excess of glucose is constantly offered, initially the body maintains blood glucose concentration within normal range by releasing higher concentrations of insulin into the blood, a condition that is described as “prediabetes”. In a process of several years, this compensating mechanism will eventually fail: the blood glucose concentration increases resulting in T2D. In the current healthcare practice, T2D is actually diagnosed by recognizing only elevated blood glucose concentrations, being insufficient for identification of people who have prediabetes and are at-risk to develop T2D. Although the increased insulin concentrations at normal glucose concentrations offer an opportunity for early identification/screening of people with prediabetes, there is a lack of effective and reliable methods/devices to adequately measure insulin concentrations. An integrated approach has been chosen for identification of people at-risk by using a prediabetes screening method based on insulin detection. Users and other stakeholders will be involved in the development and implementation process from the start of the project. A portable and easy-to-use demonstrator will be realised, based on rapid lateral flow tests (LFTs), which is able to measure insulin in clinically relevant samples (serum/blood) quickly and reliably. Furthermore, in collaboration with healthcare professionals, we will investigate how this screening method can be implemented in practice to contribute to a healthier lifestyle and prevent T2D.
