In our Guest Editorial “The COVID-19 Pandemic: A Family Affair,” which was published in the Journal of Family Nursing by members of the FAMily Health in Europe–Research in Nursing (FAME-RN) group (Luttik et al., 2020), we highlighted the impact on nurses and families.The pandemic was at its beginning, and we described the situation of patients and families and the need for family nursing. Furthermore, we addressed the effect on the mental health of nurses and other health care professionals, due to the increasing workload they needed to manage. In this Guest Editorial, we discuss the impact of the COVID-19 on families during and post pandemic.
Aim: to identify: (1) nursing competencies for FCC in a hospital setting; and (2) to explore perspectives on these competencies among Dutch and Australian professionals including lecturers, researchers, Registered Nurses and policy makers. Design: A multinational cross-sectional study using Q-methodology. Methods: First, an integrative review was carried out to identify known competencies regarding FCC and to develop the Q-set (search up to July 2018). Second, purposive sampling was used to ensure stakeholder involvement. Third, participants sorted the Q-set using a web-based system between May and August 2019. Lastly, the data were analysed using a by-person factor analysis. The commentaries on the five highest and lowest ranked competencies were thematically analysed. Results: The integrative review identified 43 articles from which 72 competencies were identified. In total 69 participants completed the Q-sorting. We extracted two factors with an explained variance of 24%. The low explained variance hampered labelling. Based on a post-hoc qualitative analysis, four themes emerged from the competencies that were considered most important, namely: (a) believed preconditions for FCC; (b) promote a partnership between nurses, patients and families; (c) be a basic element of nursing; and (d) represent a necessary positive attitude and strong beliefs of the added value of FCC. Three themes appeared from the competencies that were considered least important because they: (a) were not considered a specific nursing competency; (b) demand a multidisciplinary approach; or (c) require that patients and families take own responsibility. Conclusions: Among healthcare professionals, there is substantial disagreement on which nursing competencies are deemed most important for FCC. Impact: Our set of competencies can be used to guide education and evaluate practicing nurses in hospitals. These findings are valuable to consider different views on FCC before implementation of new FCC interventions into nursing practice.
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).
