For students who want support to continue their education.
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Accessible Summary What is known on the subject? • Mentalizing is the capacity to understand both one‘s own and other people‘s behaviour in terms of mental states, such as, for example, desires, feelings and beliefs. • The mentalizing capacities of healthcare professionals help to establish effective therapeutic relationships and, in turn, lead to better patient outcomes. What this paper adds to existing knowledge? • The personal factors positively associated with the mentalizing capacities of healthcare professionals are being female, greater work experience and having a more secure attachment style. Psychosocial factors are having personal experience with psychotherapy, burnout, and in the case of female students, being able to identify with the female psychotherapist role model during training. There is limited evidence that training programmes can improve mentalizing capacities. • Although the mentalization field is gaining importance and research is expanding, the implications for mental health nursing have not been previously reviewed. Mental health nurses are underrepresented in research on the mentalizing capacities of healthcare professionals. This is significant given that mental health nurses work closest to patients and thus are more often confronted with patients‘ behaviour compared to other health care professionals, and constitute a large part of the workforce in mental healthcare for patients with mental illness. What are the implications for practice? • Given the importance of mentalizing capacity of both the patient and the nurse for a constructive working relationship, it is important that mental health nurses are trained in the basic principles of mentalization. Mental health nurses should be able to recognize situations where patients‘ lack of ability to mentalize creates difficulties in the interaction. They should also be able to recognize their own difficulties with mentalizing and be sensitive to the communicative implications this may have.
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Low heart rate variability (HRV) is related to health problems that are known reasons for sick-leave or early retirement. A 1-minute-protocol could allow large scale HRV measurement for screening of health problems and, potentially, sustained employability. Our objectives were to explore the association of HRV with measures of health. Cross-sectional design with 877 Dutch employees assessed during a Workers’ Health Assessment. Personal and job characteristics, workability, psychological and mental problems, and lifestyle were measured with questionnaires. Biometry was measured (BMI, waist circumference, blood pressure, glucose, cholesterol). HRV was assessed with a 1-minute paced deep-breathing protocol and expressed as mean heart rate range (MHRR). A low MHRR indicates a higher health risk. Groups were classified age adjusted for HRV and compared. Spearman correlations between raw MHRR and the other measures were calculated. Significant univariable correlations (p < 0.05) were entered in a linear regression model to explore the multivariable association with MHRR. Age, years of employment, BMI and waist circumference differed significantly between HRV groups. Significant correlations were found between MHRR and age, workability, BMI, waist circumference, cholesterol, systolic and diastolic blood-pressure and reported physical activity and alcohol consumption. In the multivariable analyses 21.1% of variance was explained: a low HRV correlates with aging, higher BMI and higher levels of reported physically activity. HRV was significantly associated with age, measures of obesity (BMI, waist circumference), and with reported physical activity, which provides a first glance of the utility of a 1-minute paced deep-breathing HRV protocol as part of a comprehensive preventive Workers’ Health Assessment.Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat ivecommons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate redit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Effectiveness of Supported Education for students with mental health problems, an experimental study.The onset of mental health problems generally occurs between the ages of 16 and 23 – the years in which young people follow postsecondary education, which is a major channel in ourso ciety to prepare for a career and enhance life goals. Several studies have shown that students with mental health problems have a higher chance of early school leaving. Supported Education services have been developed to support students with mental health to remain at school. The current project aims to study the effect of an individually tailored Supported Education intervention on educational and mental health outcomes of students with mental health problems at a university of applied sciences and a community college. To that end, a mixed methods design will be used. This design combines quantitative research (Randomized Controlled Trial) with qualitative research (focus groups, monitoring, interviews). 100 students recruited from the two educational institutes will be randomly allocated to either the intervention or control group.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
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.
Centre of Expertise, part of Hanze
