This speech discusses how the professorship intends to support practitioners in the nursing domain and contribute to shaping nursing leadership and each person's professional individuality. The title of the speech, “Notes on Nursing 2.0,” is particularly intended to emphasize the need for these changes in the nursing domain. Not by assuming that nothing has changed in care and nursing since Nightingale's time. There has. Being educated in the professional domain is not only a given but a requirement. The knowledge domain of care and nursing has developed far and wide in nursing diagnostics and standards. Nursing science research, which Nightingale once started as the first female statistician in the British Kingdom, has firmly established itself in education and practice. Wanting to be of significance to others out of compassion is still the professional motivation, but there is no longer a subservient servitude (Cingel van der, 2012). At the same time, wholehearted leadership is not yet taken for granted in daily practice and optimal professional practice falters due to an equality principle of differently educated caregivers and nurses that has been held for too long. That is the need for change to which this 2.0 version “Notes on Nursing” and the lectorate want to contribute in the coming years. Chapter 1, through the metaphors in the story “The Cat Who Looked at the King,” describes the vision of emancipatory action research and the change principles that the lectorate will deploy. Chapter 2 contains the reason, mission and lines of research that are interrelated within the lectorate. Chapters 3 and 4 address the themes of identity and leadership, discussing their interrelationship with professional practice and developing a research culture. In addition, specific aspects that influence practice and work culture today are addressed, and how the lectorate contributes specifically to the development of nursing leadership and the formation of professional identity in the relevant domain is described. Chapter 5 contains a summary of the principles on which the research program is based, as well as information on current and future projects. Chapter 6 provides background information on the lector and the members of the knowledge circle.
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To evaluate the effect of the educational program "Guided Clinical Reasoning" (GCR) and the introduction of an intelligent electronic nursing documentation system (e-doc) on the quality of the nursing process.Evaluation was conducted at three measurement points and rated with the instrument "Quality of Nursing Diagnoses, Interventions and Outcomes" (Q-DIO).GCR showed the best Q-DIO-scores. No long-term effect was found after GCR cessation. The e-doc delivered the lowest scores, while showing adequate support in using nursing diagnoses.E-docs can support conducting the nursing process, but for meaningful e-doc use, clinical reasoning is essential.High-quality nursing documentation requires recognition of factors obstructing or supporting nurses in the use of e-docs while conducting the nursing process.
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A Nursing Process-Clinical Decision Support System (NP-CDSS) Standard with 25 criteria to guide future developments of Nursing Process-Clinical Decision Support Systems was developed. The NP-CDSS Standards' content validity was established in qualitative interviews yielding fourteen categories that demonstrate international expert consensus. All experts judged the Advanced Nursing Process being the centerpiece for Nursing Process-Clinical Decision Support System that should suggest research-based, pre-defined nursing diagnoses and correct linkages between diagnoses, evidence-based interventions and patient outcomes.
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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).
In the Netherlands, 125 people suffer a stroke every day, which annually results in 46.000 new stroke patients Stroke patients are confronted with combinations of physical, psychological and social consequences impacting their long term functioning and quality of live. Fortunately many patients recover to their pre-stroke level of functioning, however, almost half of them never will. Consequently, rehabilitation often means that patients need to adapt to a new reality in their lives, requiring not only physical but also psychosocial adjustments. Nurses play a key role during rehabilitation of stroke patients. However, when confronted with psychosocial problems, they often feel insecure about identifying the specific psycho-social needs of the individual patient and providing adequate care. In our project ‘Early Detection of Post-Stroke Depression’, (SIA RAAK; 2010-12-36P), we developed a toolkit focusing on early identification of depression after stroke continued with interventions nurses can use during hospitalisation. During this project it became clear that evidence regarding possible interventions is scarce and inclusive. Moreover feasibility of interventions is often not confirmed. Our project showed that during the period of hospital admission patients and health care providers strongly focus on surviving the stroke and on the physical rehabilitation. Therefore, we concluded that to make one step beyond we first have to go one step back. To strengthen psychosocial care for patients after stroke we have to add, reconsider and shape knowledge in context of health care practices in a systematic way, resulting in evidence based and practice informed stepping stones. With this project we aim to collect these stepping stones and develop a nursing care programme that improves psychosocial well-being of patients after stroke, is tailored to the particular concerns and needs of patients, and is considered feasible for use in the usual care process of nurses in the stroke rehabilitation pathway.
