Up until 2005 Peter Mak was involved as pedagogy teacher in the instrumental teacher education of the Bachelor of Music of the Prince Claus Conservatoire. The programme’s pedagogy section consisted of modules developed by Peter including ‘Didactics’, ‘Learning processes’, ‘Study skills’, and ‘Exceptional learners.’ These modules, all thoroughly developed and described by Peter, formed a neatly rolled-out set of tuition for the students in the programme. The content and set-up of the modules were based on the latest developments and insights in education. All modules were underpinned by authentic sources from the field and were easy to read. During the past decade Peter’s influence and ideas for the instrumental teacher education remained of great importance. As a committed colleague he was always interested to look into issues and ideas related to the curriculum and stayed an important critical friend. But possibly most distinguished was his between-the-lines plea for all present and future teachers to approach each individual learner with respect and dignity.
This report describes the validation of a "framework that delivers insight into the tangible and intangible effects of a mobile (IT) system, before it is being implemented".
The aim of this study was to develop a valid instrument to measure student nurses’ perceptions of community care (SCOPE). DeVellis’ staged model for instrument development and validation was used. Scale construction of SCOPE was based on existing literature. Evaluation of its psychometric properties included exploratory factor analysis and reliability analysis. After pilot-testing, 1062 bachelor nursing students from six institutions in the Netherlands (response rate 81%) took part in the study. SCOPE is a 35-item scale containing: background variables, 11 measuring the affective component, 5 measuring community care perception as a placement, 17 as a future profession, and 2 on the reasons underlying student preference. Principal axis factoring yielded two factors in the affective component scale reflecting ‘enjoyment’ and ‘utility’, two in the placement scale reflecting ‘learning possibilities’ and ‘personal satisfaction’, and four in the profession scale: ‘professional development’, ‘collaboration’, ‘caregiving’, and ‘complexity and workload’. Cronbach’s α of the complete scale was .892 and of the subscales .862, .696, and .810 respectively. SCOPE is a psychometrically sound instrument for measuring students’ perceptions of community care. By determining these perceptions, it becomes possible to positively influence them with targeted curriculum redesign, eventually contributing to decreasing the workforce shortage in community nursing.
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).
