Objective: To obtain insight into (a) the prevalence of nursing staff–experienced barriers regarding the promotion of functional activity among nursing home residents, and (b) the association between these barriers and nursing staff–perceived promotion of functional activity. Method: Barriers experienced by 368 nurses from 41 nursing homes in the Netherlands were measured with the MAastrIcht Nurses Activity INventory (MAINtAIN)-barriers; perceived promotion of functional activities was measured with the MAINtAIN-behaviors. Descriptive statistics and hierarchical linear regression analyses were performed. Results: Most often experienced barriers were staffing levels, capabilities of residents, and availability of resources. Barriers that were most strongly associated with the promotion of functional activity were communication within the team, (a lack of) referral to responsibilities, and care routines. Discussion: Barriers that are most often experienced among nursing staff are not necessarily the barriers that are most strongly associated with nursing staff–perceived promotion of functional activity.
Objective To explore predictors of district nursing care utilisation for community-living (older) people in the Netherlands using claims data. To cope with growing demands in district nursing care, knowledge about the current utilisation of district nursing care is important. Setting District nursing care as a part of primary care. Participants In this nationwide study, claims data were used from the Dutch risk adjustment system and national information system of health insurers. Samples were drawn of 5500 pairs of community-living people using district nursing care (cases) and people not using district nursing care (controls) for two groups: all ages and aged 75+ years (total N=22 000). Outcome measures The outcome was district nursing care utilisation and the 114 potential predictors included predisposing factors (eg, age), enabling factors (eg, socioeconomic status) and need factors (various healthcare costs). The random forest algorithm was used to predict district nursing care utilisation. The performance of the models and importance of predictors were calculated. Results For the population of people aged 75+ years, most important predictors were older age, and high costs for general practitioner consultations, aid devices, pharmaceutical care, ambulance transportation and occupational therapy. For the total population, older age, and high costs for pharmaceutical care and aid devices were the most important predictors. Conclusions People in need of district nursing care are older, visit the general practitioner more often, and use more and/or expensive medications and aid devices. Therefore, close collaboration between the district nurse, general practitioner and the community pharmacist is important. Additional analyses including data regarding health status are recommended. Further research is needed to provide an evidence base for district nursing care to optimise the care for those with high care needs, and guide practice and policymakers’ decision-making.
The project aims to improve palliative care in China through the competence development of Chinese teachers, professionals, and students focusing on the horizontal priority of digital transformation.Palliative care (PC) has been recognised as a public health priority, and during recent years, has seen advances in several aspects. However, severe inequities in the access and availability of PC worldwide remain. Annually, approximately 56.8 million people need palliative care, where 25.7% of the care focuses on the last year of person’s life (Connor, 2020).China has set aims for reaching the health care standards of the developed countries by 2030 through the Healthy China Strategy 2030, where one of the improvement areas in health care includes palliative care, thus continuing the previous efforts.The project provides a constructive, holistic, and innovative set of actions aimed at resulting in lasting outcomes and continued development of palliative care education and services. Raising the awareness of all stakeholders on palliative care, including the public, is highly relevant and needed. Evidence based practice guidelines and education are urgently required for both general and specialised palliative care levels, to increase the competencies for health educators, professionals, and students. This is to improve the availability and quality of person-centered palliative care in China. Considering the aging population, increase in various chronic illnesses, the challenging care environment, and the moderate health care resources, competence development and the utilisation of digitalisation in palliative care are paramount in supporting the transition of experts into the palliative care practice environment.General objective of the project is to enhance the competences in palliative care in China through education and training to improve the quality of life for citizens. Project develops the competences of current and future health care professionals in China to transform the palliative care theory and practice to impact the target groups and the society in the long-term. As recognised by the European Association for Palliative Care (EAPC), palliative care competences need to be developed in collaboration. This includes shared willingness to learn from each other to improve the sought outcomes in palliative care (EAPC 2019). Since all individuals have a right to health care, project develops person-centered and culturally sensitive practices taking into consideration ethics and social norms. As concepts around palliative care can focus on physical, psychological, social, or spiritual related illnesses (WHO 2020), project develops innovative pedagogy focusing on evidence-based practice, communication, and competence development utilising digital methods and tools. Concepts of reflection, values and views are in the forefront to improve palliative care for the future. Important aspects in project development include health promotion, digital competences and digital health literacy skills of professionals, patients, and their caregivers. Project objective is tied to the principles of the European Commission’s (EU) Digital Decade that stresses the importance of placing people and their rights in the forefront of the digital transformation, while enhancing solidarity, inclusion, freedom of choice and participation. In addition, concepts of safety, security, empowerment, and the promotion of sustainable actions are valued. (European Commission: Digital targets for 2030).Through the existing collaboration, strategic focus areas of the partners, and the principles of the call, the PalcNet project consortium was formed by the following partners: JAMK University of Applied Sciences (JAMK ), Ramon Llull University (URL), Hanze University of Applied Sciences (HUAS), Beijing Union Medical College Hospital (PUMCH), Guangzhou Health Science College (GHSC), Beihua University (BHU), and Harbin Medical University (HMU). As project develops new knowledge, innovations and practice through capacity building, finalisation of the consortium considered partners development strategy regarding health care, (especially palliative care), ability to create long-term impact, including the focus on enhancing higher education according to the horizontal priority. In addition, partners’ expertise and geographical location was also considered important to facilitate long-term impact of the results.Primary target groups of the project include partner country’s (China) staff members, teachers, researchers, health care professionals and bachelor level students engaging in project implementation. Secondary target groups include those groups who will use the outputs and results and continue in further development in palliative care upon the lifetime of the project.
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).
De druk op de wijkverpleging neemt toe. Zelfredzaamheid van cliënten kan deze druk verlichten, maar zorgprofessionals krijgen onvoldoende steun om dit te bereiken. Data Nurse ondersteunt verpleegkundigen op een datagedreven manier door waardevolle inzichten uit cliëntendossiers te benutten om de zelfredzaamheid van cliënten te vergroten en de zorg te verbeteren.
