Purpose: To support family caregivers of persons post-stroke adequately from the start and to develop self-management interventions, we aim to gain a better understanding of family caregivers experiences at the time of acute care and therefore achieve a better understanding of how they manage their new situation. Methods and Materials: We chose a qualitative descriptive methodology using individual semi-structured interviews with eleven family caregivers of persons post-stroke. We conducted interviews retrospectively, between 2 and 10 months post-stroke, and analysed transcripts using thematic analysis. Results: The themes (1) being in survival mode, (2) feeling supported by family and friends, (3) feeling left alone by the treatment team and (4) insisting on information emerged from the data. Conclusion: During acute care, many self-management skills are required from family caregivers but are just starting to be developed. This development can first be observed as co-management with the social network and is often combined with shared decision-making. Information-sharing, foundational for developing self-management, is essential for family caregivers and should be supported proactively by health professionals from the beginning. Further, from the start, health professionals should raise awareness about role changes and imbalances of activities among family caregivers to prevent negative influences on their health.
The European policy emphasis on providing informal care at home causes caregivers and home care professionals having more contact with each other, which makes it important for them to find satisfying ways to share care. Findings from the literature show that sharing care between caregivers and professionals can be improved. This study therefore examines to what degree and why caregivers’ judgements on sharing care with home care professionals vary. To improve our understanding of social inequities in caregiving experiences, the study adopts an intersectional perspective. We investigate how personal and situational characteristics attached to care judgements are interwoven. Using data of the Netherlands Institute for Social Research, we conducted bivariate and multivariate linear regression analysis (N = 292). We combined four survey questions into a 1–4 scale on ‘caregiver judgement’ (α = 0.69) and used caregivers’ personal (such as gender and health status) and situational characteristics (such as the care recipient's impairment and type of care) as determinants to discern whether these are related to the caregivers’ judgement. Using a multiplicative approach, we also examined the relationship between mutually constituting factors of the caregivers’ judgement. Adjusted for all characteristics, caregivers who provide care to a parent or child with a mental impairment and those aged between 45 and 64 years or with a paid job providing care to someone with a mental impairment are likely to judge sharing care more negatively. Also, men providing care with help from other caregivers and caregivers providing care because they like to do so who provide domestic help seem more likely to be less satisfied about sharing care. This knowledge is vital for professionals providing home care, because it clarifies differences in caregivers’ experiences and hence induce knowledge how to pay special attention to those who may experience less satisfaction while sharing care.
Objective: Despite the increasing availability of eRehabilitation, its use remains limited. The aim of this study was to assess factors associated with willingness to use eRehabilitation. Design: Cross-sectional survey. Subjects: Stroke patients, informal caregivers, health-care professionals. Methods: The survey included personal characteristics, willingness to use eRehabilitation (yes/no) and barri-ers/facilitators influencing this willingness (4-point scale). Barriers/facilitators were merged into factors. The association between these factors and willingness to use eRehabilitation was assessed using logistic regression analyses. Results: Overall, 125 patients, 43 informal caregivers and 105 healthcare professionals participated in the study. Willingness to use eRehabilitation was positively influenced by perceived patient benefits (e.g. reduced travel time, increased motivation, better outcomes), among patients (odds ratio (OR) 2.68; 95% confidence interval (95% CI) 1.34–5.33), informal caregivers (OR 8.98; 95% CI 1.70–47.33) and healthcare professionals (OR 6.25; 95% CI 1.17–10.48). Insufficient knowledge decreased willingness to use eRehabilitation among pa-tients (OR 0.36, 95% CI 0.17–0.74). Limitations of the study include low response rates and possible response bias. Conclusion: Differences were found between patients/informal caregivers and healthcare professionals. Ho-wever, for both groups, perceived benefits of the use of eRehabilitation facilitated willingness to use eRehabili-tation. Further research is needed to determine the benefits of such programs, and inform all users about the potential benefits, and how to use eRehabilitation. Lay Abstract The use of digital eRehabilitation after stroke (e.g. in serious games, e-consultation and education) is increasing. However, the use of eRehabilitation in daily practice is limited. As a first step in increasing the use of eRehabilitation in stroke care, this study examined which factors influence the willingness of stroke patients, informal caregivers and healthcare professionals to use eRehabilitation. Beliefs about the benefits of eRehabilitation were found to have the largest positive impact on willingness to use eRehabilitation. These benefits included reduced travel time, increased adherence to therapy or motivation, and better health outcomes. The willingness to use eRehabilitation is limited by a lack of knowledge about how to use eRehabilitation.
MULTIFILE
Dutch society faces major future challenges putting populations’ health and wellbeing at risk. An ageing population, increase of chronic diseases, multimorbidity and loneliness lead to more complex healthcare demands and needs and costs are increasing rapidly. Urban areas like Amsterdam have to meet specific challenges of a growing and super divers population often with a migration background. The bachelor programs and the relating research groups of social work and occupational therapy at the Amsterdam University of Applied Sciences innovate their curricula and practice-oriented research by multidisciplinary and cross-domain approaches. Their Centres of Expertise foster interprofessional research and educational innovation on the topics of healthy ageing, participation, daily occupations, positive health, proximity, community connectedness and urban innovation in a social context. By focusing on senior citizens’ lives and by organizing care in peoples own living environment. Together with their networks, this project aims to develop an innovative health promotion program and contribute to the government missions to promote a healthy and inclusive society. Collaboration with stakeholders in practice based on their urgent needs has priority in the context of increasing responsibilities of local governments and communities. Moreover, the government has recently defined social base as being the combination of citizen initiatives, volunteer organizations , caregivers support, professional organizations and support of vulnerable groups. Kraktie Foundations is a community based ethno-cultural organization in south east Amsterdam that seeks to research and expand their informal services to connect with and build with professional care organizations. Their aim coincides with this project proposal: promoting health and wellbeing of senior citizens by combining intervention, participatory research and educational perspectives from social work, occupational therapy and hidden voluntary social work. With a boundary crossing innovation of participatory health research, education and Kraktie’s work in the community we co-create, change and innovate towards sustainable interventions with impact.
The project aims to improve palliative care in China through the competence development of Chinese teachers, professionals, and students focusing on the horizontal priority of digital transformation.Palliative care (PC) has been recognised as a public health priority, and during recent years, has seen advances in several aspects. However, severe inequities in the access and availability of PC worldwide remain. Annually, approximately 56.8 million people need palliative care, where 25.7% of the care focuses on the last year of person’s life (Connor, 2020).China has set aims for reaching the health care standards of the developed countries by 2030 through the Healthy China Strategy 2030, where one of the improvement areas in health care includes palliative care, thus continuing the previous efforts.The project provides a constructive, holistic, and innovative set of actions aimed at resulting in lasting outcomes and continued development of palliative care education and services. Raising the awareness of all stakeholders on palliative care, including the public, is highly relevant and needed. Evidence based practice guidelines and education are urgently required for both general and specialised palliative care levels, to increase the competencies for health educators, professionals, and students. This is to improve the availability and quality of person-centered palliative care in China. Considering the aging population, increase in various chronic illnesses, the challenging care environment, and the moderate health care resources, competence development and the utilisation of digitalisation in palliative care are paramount in supporting the transition of experts into the palliative care practice environment.General objective of the project is to enhance the competences in palliative care in China through education and training to improve the quality of life for citizens. Project develops the competences of current and future health care professionals in China to transform the palliative care theory and practice to impact the target groups and the society in the long-term. As recognised by the European Association for Palliative Care (EAPC), palliative care competences need to be developed in collaboration. This includes shared willingness to learn from each other to improve the sought outcomes in palliative care (EAPC 2019). Since all individuals have a right to health care, project develops person-centered and culturally sensitive practices taking into consideration ethics and social norms. As concepts around palliative care can focus on physical, psychological, social, or spiritual related illnesses (WHO 2020), project develops innovative pedagogy focusing on evidence-based practice, communication, and competence development utilising digital methods and tools. Concepts of reflection, values and views are in the forefront to improve palliative care for the future. Important aspects in project development include health promotion, digital competences and digital health literacy skills of professionals, patients, and their caregivers. Project objective is tied to the principles of the European Commission’s (EU) Digital Decade that stresses the importance of placing people and their rights in the forefront of the digital transformation, while enhancing solidarity, inclusion, freedom of choice and participation. In addition, concepts of safety, security, empowerment, and the promotion of sustainable actions are valued. (European Commission: Digital targets for 2030).Through the existing collaboration, strategic focus areas of the partners, and the principles of the call, the PalcNet project consortium was formed by the following partners: JAMK University of Applied Sciences (JAMK ), Ramon Llull University (URL), Hanze University of Applied Sciences (HUAS), Beijing Union Medical College Hospital (PUMCH), Guangzhou Health Science College (GHSC), Beihua University (BHU), and Harbin Medical University (HMU). As project develops new knowledge, innovations and practice through capacity building, finalisation of the consortium considered partners development strategy regarding health care, (especially palliative care), ability to create long-term impact, including the focus on enhancing higher education according to the horizontal priority. In addition, partners’ expertise and geographical location was also considered important to facilitate long-term impact of the results.Primary target groups of the project include partner country’s (China) staff members, teachers, researchers, health care professionals and bachelor level students engaging in project implementation. Secondary target groups include those groups who will use the outputs and results and continue in further development in palliative care upon the lifetime of the project.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
