Are you looking for some tips to stay focused on your studies, now that education has gone online? Have a read through the tips below from the Study Success research group. These tips have been compiled on the basis of scientific insight from cognitive psychology, neuropsychology and educational science, as well as our own studies into motivation, stress, enthusiasm and drop-out.
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Background The sense of home of nursing home residents is a multifactorial phenomenon which is important for the quality of living. This purpose of this study is to investigate the factors influencing the sense of home of older adults residing in the nursing home from the perspective of residents, relatives and care professionals. Methods A total of 78 participants (n = 24 residents, n = 18 relatives and n = 26 care professionals) from 4 nursing homes in the Netherlands engaged in a qualitative study, in which photography was as a supportive tool for subsequent interviews and focus groups. The data were analyzed based on open ended coding, axial coding and selective coding. Results The sense of home of nursing home residents is influenced by a number of jointly identified factors, including the building and interior design; eating and drinking; autonomy and control; involvement of relatives; engagement with others and activities; quality of care are shared themes. Residents and relatives stressed the importance of having a connection with nature and the outdoors, as well as coping strategies. Relatives and care professionals emphasized the role the organization of facilitation of care played, as well as making residents feel like they still matter. Conclusions The sense of home of nursing home residents is influenced by a multitude of factors related to the psychology of the residents, and the social and built environmental contexts. A holistic understanding of which factors influence the sense of home of residents can lead to strategies to optimize this sense of home. This study also indicated that the nursing home has a dual nature as a place of residence and a place where people are supported through numerous care strategies.
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To face the challenges of an ageing population, many Western countries nowadays stimulate an ageing in place policy to empower older adults to grow old in their own homes with the highest degree of self‐reliance. However, many community‐living older adults experience limitations in (instrumental) activities of daily living ((I)ADLs), which may result in a need for home‐care services. Unfortunately, home‐care workers often provide support by taking over tasks, as they are used to doing things for older adults rather than with them, which undermines their possibilities to maintain their self‐care capabilities. In contrast, reablement focuses on capabilities and opportunities of older adults, rather than on disease and dependency. Consequently, older adults are stimulated to be as active as possible during daily and physical activities. The 'Stay Active at Home' programme was designed to train home‐care workers to apply reablement in practice. To explore the experiences of home‐care workers with this programme an exploratory study was conducting in the Netherlands, between April and July, 2017. In total, 20 participants were interviewed: nine nurses (including a district nurse), 10 domestic support workers and the manager of the domestic support workers. The semi‐structured interviews focused on the experienced improvements with regard to knowledge, skills, self‐efficacy and social support. Furthermore, the most and least appreciated programme components were identified. The study has shown that home‐care workers perceived the programme as useful to apply reablement. However, they also need more support with mastering particular skills and dealing with challenging situations. Future implementation of the 'Stay Active at Home' programme can potentially benefit from small adaptions. Furthermore, future research is needed to examine whether the programme leads to more (cost‐) effective home care.
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As the Dutch population is aging, the field of music-in-healthcare keeps expanding. Healthcare, institutionally and at home, is multiprofessional and demands interprofessional collaboration. Musicians are sought-after collaborators in social and healthcare fields, yet lesser-known agents of this multiprofessional group. Although live music supports social-emotional wellbeing and vitality, and nurtures compassionate care delivery, interprofessional collaboration between musicians, social work, and healthcare professionals remains marginal. This limits optimising and integrating music-making in the care. A significant part of this problem is a lack of collaborative transdisciplinary education for music, social, and healthcare students that deep-dives into the development of interprofessional skills. To meet the growing demand for musical collaborations by particularly elderly care organisations, and to innovate musical contributions to the quality of social and healthcare in Northern Netherlands, a transdisciplinary education for music, physiotherapy, and social work studies is needed. This project aims to equip multiprofessional student groups of Hanze with interprofessional skills through co-creative transdisciplinary learning aimed at innovating and improving musical collaborative approaches for working with vulnerable, often older people. The education builds upon experiential learning in Learning LABs, and collaborative project work in real-life care settings, supported by transdisciplinary community forming.The expected outcomes include a new concept of a transdisciplinary education for HBO-curricula, concrete building blocks for a transdisciplinary arts-in-health minor study, innovative student-led approaches for supporting the care and wellbeing of (older) vulnerable people, enhanced integration of musicians in interprofessional care teams, and new interprofessional structures for educational collaboration between music, social work and healthcare faculties.
The main objective is to write a scientific paper in a peer-reviewed Open Access journal on the results of our feasibility study on increasing physical activity in home dwelling adults with chronic stroke. We feel this is important as this article aims to close a gap in the existing literature on behavioral interventions in physical therapy practice. Though our main target audience are other researchers, we feel clinical practice and current education on patients with stroke will benefit as well.
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).
