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.
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AIMS AND OBJECTIVE: To explore differences in nurses' attitudes regarding the importance of family in nursing care and factors associated with nurses' attitudes across 11 European countries.BACKGROUND: Family involvement in healthcare has received attention in many European healthcare systems. Nurses have a unique opportunity to promote family involvement in healthcare; however, their attitudes and beliefs may facilitate or impede this practice.DESIGN: A cross-sectional survey across European countries.METHOD: A broad convenience sample of 8112 nurses across 11 European countries was recruited from October 2017 to December 2019. Data were collected using the Families' Importance in Nursing Care-Nurses' Attitudes (FINC-NA) questionnaire. We used the STROBE checklist to report the results.RESULTS: There were significant differences in nurses' attitudes about families' importance in nursing care across Europe. Country was the factor with the strongest association with the total scores of the FINC-NA. Older age, higher level of education, increased years since graduation, having a strategy for the care of families in the workplace, and having experience of illness within one's own family were associated with a higher total FINC-NA score. Being male and working in a hospital or other clinical settings were associated with a lower total FINC-NA score.CONCLUSION: Nurses' attitudes regarding the importance of family in nursing care vary across 11 European countries. This study highlights multiple factors associated with nurses' attitudes. Further research is necessary to gain a deeper understanding of the reasons for nurses' different attitudes and to develop a strong theoretical framework across Europe to support family involvement in patient care. The inclusion of family healthcare programs in the baccalaureate curriculum may improve nurses' attitudes.RELEVANCE FOR CLINICAL PRACTICE: In clinical practice, the focus should be on identifying influencing factors on nurses' attitudes to enhance families' importance in nursing care across Europe.
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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).
