The transition from home to a nursing home can be stressful and traumatic for both older persons and informal caregivers and is often associated with negative outcomes. Additionally, transitional care interventions often lack a comprehensive approach, possibly leading to fragmented care. To avoid this fragmentation and to optimize transitional care, a comprehensive and theory-based model is fundamental. It should include the needs of both older persons and informal caregivers. Therefore, this study, conducted within the European TRANS-SENIOR research consortium, proposes a model to optimize the transition from home to a nursing home, based on the experiences of older persons and informal caregivers. These experiences were captured by conducting a literature review with relevant literature retrieved from the databases CINAHL and PubMed. Studies were included if older persons and/or informal caregivers identified the experiences, needs, barriers, or facilitators during the transition from home to a nursing home. Subsequently, the data extracted from the included studies were mapped to the different stages of transition (pre-transition, mid-transition, and post-transition), creating the TRANSCITmodel. Finally, results were discussed with an expert panel, leading to a final proposed TRANSCIT model. The TRANSCIT model identified that older people and informal caregivers expressed an overall need for partnership during the transition from home to a nursing home. Moreover, it identified 4 key components throughout the transition trajectory (ie, pre-, mid-, and post-transition): (1) support, (2) communication, (3) information, and (4) time. The TRANSCIT model could advise policy makers, practitioners, and researchers on the development and evaluation of (future) transitional care interventions. It can be a guideline reckoning the needs of older people and their informal caregivers, emphasizing the need for a partnership, consequently reducing fragmentation in transitional care and optimizing the transition from home to a nursing home.
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Background and objective: Hospital and home care nurses and nursing assistants do not provide optimal nutritional care to older adults, which is due to several factors that influence their current behaviour. To successfully target these factors, we developed a microlearning intervention. The next step is to assess its feasibility to achieve the best fit with nursing practice. The aim of this study was to test the feasibility of the microlearning intervention about nutritional care for older adults provided by hospital and home care nurses and nursing assistants. Methods: In a multicentre study, we used a mixed-methods design. Feasibility was determined by assessing 1) recruitment and retention of the participants and 2) the acceptability, compliance and delivery of the intervention. Data about the use of the intervention (consisting of 30 statements), and data from a standardised questionnaire and two focus group interviews were used to measure the feasibility outcomes. Results: Fourteen teams with a total of 306 participants (response rate: 89.7%) completed the intervention and the median (Q1, Q3) score for completed statements per participant was 23 (12, 28). The mean proportion of correct answers was 72.2%. Participants were both positive and constructive about the intervention. They confirmed that they mostly learned from the intervention. Overall, the intervention was acceptable to the participants and compliance and delivery was adequate. Conclusions: The microlearning intervention is mostly feasible for hospital and home care nurses and nursing assistants. Based on participants’ constructive feedback, we consider that the intervention needs refinement to improve its feasibility.
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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.
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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).
The findings suggests that participation in music practices can significantly support caregivers' and nurses' contact with the people to whom they give care and the healthcare professionals' insights into the patients' and residents' personhood. Music can create experienced changes in the care environment through kairotic moments of connectivity and intimacy of the musical interaction. The music sessions support and reinforce the person-centred values of care delivery.The meaning of participatory music practices for the well-being and learning of healthcare professionals working with ageing patients and nursing home residents.
Personal factors, team factors, and organizational factors have a strong influence on the adoption of technology used by, for instance, nurses in homecare. This part of the research portfolio in Point of Care Diagnostics regards the adoption of diagnostic technology in the health care domain.
