BACKGROUND: Today's nursing school applicants are considered “digital natives.” This study investigated students' views of new health care technologies. METHOD: In a cross-sectional survey among first-year nursing students, 23 common nursing activities and five telehealth nursing activities were presented along with three statements: “I consider this a core task of nursing,” “I look forward to becoming trained in this task,” and “I think I will do very well in performing this task.” RESULTS: Internet-generation nursing students (n = 1,113) reported a significantly (p ⩽ .001) less positive view of telehealth activities than of common nursing activities. Median differences were 0.7 (effect size [ES], −0.54), 0.4 (ES, −0.48), and 0.3 (ES, −0.39), measured on a 7-point scale. CONCLUSION: Internet-generation nursing students do not naturally have a positive view of technology-based health care provision. The results emphasize that adequate technology and telehealth education is still needed for nursing students. [J Nurs Educ. 2017;56(12):717–724.]
In this paper we present a system that generates questions from an ontology to determine a crisis situation by ordinary people using their mobile phone: the Situation Awareness Question Generator. To generate questions from an ontology we propose a formalization based on Situation Theory and several strategies to determine a situation as quickly as possible. A suitable ontology should comply with human categorization to enhance trustworthiness. We created three ontologies, i.e. a pragmatic-based ontology, an expert-based ontology and a basiclevel ontology. Several experiments, published elsewhere, showed that the basic-level ontology is most suitable.
The propagandization of a Net Generation adds nothing to our understanding of the digital behaviour of young people. Indeed, it is becoming increasingly obvious that the whole concept of a Net Generation rests on incorrect assumptions. Hence, arguments based on a Net Generation are not only irrelevant and misleading but precarious as well. Precarious in the sense that they are mobilized as a decisive means of engineering change, not least in education policy. Only when we stop thinking in terms of the Net Generation can we form a more astute vision of when the deployment of digital learning aids will have a realistic chance of success.
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).
The maritime transport industry is facing a series of challenges due to the phasing out of fossil fuels and the challenges from decarbonization. The proposal of proper alternatives is not a straightforward process. While the current generation of ship design software offers results, there is a clear missed potential in new software technologies like machine learning and data science. This leads to the question: how can we use modern computational technologies like data analysis and machine learning to enhance the ship design process, considering the tools from the wider industry and the industry’s readiness to embrace new technologies and solutions? The obbjective of this PD project is to bridge the critical gap between the maritime industry's pressing need for innovative solutions for a more agile Ship Design Process; and the current limitations in software tools and methodologies available via the implementation into Ship Design specific software of the new generation of computational technologies available, as big data science and machine learning.
Individuals are increasingly confronted with ‘diseases of modernity’, such as stress and burnout. While insights from the work-family interface have mainly pointed towards demands and resources coming from the work and nonwork domains, the proposed multi-method PhD research project aims to contribute to contemporary scholarly and societal work-life and burnout debates by presenting an alternative theoretical lens on the development of mental health complaints in today’s society, especially among the younger Millennial generation. The project aims to shed light on how and why Millennial employees engage in a so-called ‘work/nonwork image (re)construction process’.The project will reflect on the following questions:How, why and when do individual workers engage in a process in which they construct their image(s) in the work and nonwork domains? What are the relationships, if any, between the image (re)construction process individuals engage in and potential positive- and negative consequences?The findings are expected to have important implications not only for preventive measures for individuals and organizations, but also for possible avenues for future studies. Project Partner: Nyenrode Business Universiteit
