The transition from home to a nursing home can be stressful and traumatic for both older persons and informal caregivers and is often associated with negative outcomes. Additionally, transitional care interventions often lack a comprehensive approach, possibly leading to fragmented care. To avoid this fragmentation and to optimize transitional care, a comprehensive and theory-based model is fundamental. It should include the needs of both older persons and informal caregivers. Therefore, this study, conducted within the European TRANS-SENIOR research consortium, proposes a model to optimize the transition from home to a nursing home, based on the experiences of older persons and informal caregivers. These experiences were captured by conducting a literature review with relevant literature retrieved from the databases CINAHL and PubMed. Studies were included if older persons and/or informal caregivers identified the experiences, needs, barriers, or facilitators during the transition from home to a nursing home. Subsequently, the data extracted from the included studies were mapped to the different stages of transition (pre-transition, mid-transition, and post-transition), creating the TRANSCITmodel. Finally, results were discussed with an expert panel, leading to a final proposed TRANSCIT model. The TRANSCIT model identified that older people and informal caregivers expressed an overall need for partnership during the transition from home to a nursing home. Moreover, it identified 4 key components throughout the transition trajectory (ie, pre-, mid-, and post-transition): (1) support, (2) communication, (3) information, and (4) time. The TRANSCIT model could advise policy makers, practitioners, and researchers on the development and evaluation of (future) transitional care interventions. It can be a guideline reckoning the needs of older people and their informal caregivers, emphasizing the need for a partnership, consequently reducing fragmentation in transitional care and optimizing the transition from home to a nursing home.
Nationwide and across the globe, the quality, affordability, and accessibility of home-based healthcare are under pressure. This issue stems from two main factors: the rapidly growing ageing population and the concurrent scarcity of healthcare professionals. Older people aspire to live independently in their homes for as long as possible. Additionally, governments worldwide have embraced policies promoting “ageing in place,” reallocating resources from institutions to homes and prioritising home-based services to honour the desire of older people to continue living at home while simultaneously addressing the rising costs associated with traditional institutional care.Considering the vital role of district nursing care and the fact that the population of older people in need of assistance at home is growing, it becomes clear that district nursing care plays a crucial role in primary care. The aim of this thesis is twofold: 1) to strengthen the evidence base for district nursing care; and 2) to explore the use of outcomes for learning and improving in district nursing care. The first part of this thesis examines the current delivery of district nursing care and explores its challenges during the COVID-19 pandemic to strengthen the evidence base and get a better understanding of district nursing care. Alongside the goal of strengthening the evidence for district nursing care, the second part of this thesis explores the use of patient outcomes for learning and improving district nursing care. It focuses on nurse-sensitive patient outcomes relevant to district nursing care, their current measurement in practice, and what is needed to use outcomes for learning and improving district nursing practice.
Most nurse leadership studies have concentrated on a classical, heroic, and hierarchical view of leadership. However, critical leadership studies have argued the need for more insight into leadership in daily nursing practices. Nurses must align their professional standards and opinions on quality of care with those of other professionals, management, and patients. They want to achieve better outcomes for their patients but also feel disciplined and controlled. To deal with this, nurses challenge the status quo by showing rebel nurse leadership. In this paper, we describe 47 nurses’ experiences with rebel nurse leadership from a leadership-as-practice perspective. In eight focus groups, nurses from two hospitals and one long-term care organization shared their experiences of rebel nurse leadership practices. They illustrated the differences between “bad” and “good” rebels. Knowledge, work experience, and patient-driven motivation were considered necessary for “good” rebel leadership. The participants also explained that continuous social influencing is important while exploring and challenging the boundaries set by colleagues and management. Credibility, trust, autonomy, freedom, and preserving relationships determined whether rebel nurses acted visibly or invisibly. Ultimately, this study refines the concept of rebel nurse leadership, gives a better understanding of how this occurs in nursing practice, and give insights into the challenges faced when studying nursing leadership practices.
MULTIFILE
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).
