This study seeks to explore the sources of strength giving rice to resilience among older people. Twenty-nine in-depth interviews were conducted with older people who receive long-term community care. The interviews were subjected to a thematic content analysis. The findings suggest that the main sources of strength identified among older people were constituted on three domains of analysis; the individual-, interactional and contextual domain. The individual domain refers to the qualities within older people and comprises of three sub-domains, namely beliefs about one's competence, efforts to exert control and the capacity to analyse and understand ones situation. Within these subdomains a variety of sources of strength were found like pride about ones personality, acceptance and openness about ones vulnerability, the anticipation on future losses, mastery by practising skills, the acceptance of help and support, having a balanced vision on life, not adapting the role of a victim and carpe-diem. The interactional domain is defined as the way older people cooperate and interact with others to achieve their personal goals. Sources of strength on this domain were empowering (in)formal relationships and the power of giving. Lastly, the contextual domain refers to a broader political-societal level and includes sources of strength like the accessibility of care, the availability of material resources and social policy. The three domains were found to be inherently linked to each other. The results can be used for the development of positive, proactive interventions aimed at helping older people build on the positive aspects of their lives.
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Offering physical activities matching with the preferences of residents in long-term care facilities could increase compliance and contribute to client-centered care. A measure to investigate meaningful activities by using a photo-interview has been developed (“MIBBO”). In two pilot studies including 133 residents living on different wards in long-term care facilities, feasibility, most chosen activities, and consistency of preferences were investigated. It was possible to conduct the MIBBO on average in 30 min with the majority (86.4%) of residents. The most frequently chosen activities were: gymnastics and orchestra (each 28%), preparing a meal (31%), walking (outside, 33%), watering plants (38%), and feeding pets (40%). In a retest one week after the initial interview 69.4% agreement of chosen activities was seen. The MIBBO seems a promising measure to help health care professionals in identifying residents’ preferred activities. Future research should focus on the implementation of the tailored activity plan, incorporating it into the daily routine.
Background: As more and more older adults prefer to stay in their homes as they age, there’s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in longterm care. Methods: A three round modified Delphi study was conducted, consisting of two online questionnaires and one focus group. A group of 48 experts participated in the study: nurses, innovators, developers, researchers, managers and educators. In the first two rounds experts assessed clarity and relevance on a proposed list of competencies, with the opportunity to provide suggestions for adjustments or inclusion of new competencies. In the third round the items without consensus were bespoken in a focus group. Findings: After the first round consensus was reached on relevance and clarity on n = 46 (72 %) of the competencies, after the second round on n = 54 (83 %) of the competencies. After the third round a final list of 10 competency domains and 61 sub-competencies was finalized. The 10 competency domains are: Fundamentals of AI, Participation in AI design, Patient-centered needs assessment, Personalisation of AI to patients’ situation, Data reporting, Interpretation of AI output, Integration of AI output into clinical practice, Communication about AI use, Implementation of AI and Evaluation of AI use. These competencies span from basic understanding of AIdriven lifestyle monitoring, to being able to integrate it in daily work, being able to evaluate it and communicate its use to other stakeholders, including patients and informal caregivers. Conclusion: Our study introduces a novel framework highlighting the (sub)competencies, required for nurses to work with AI-driven lifestyle monitoring in long-term care. These findings provide a foundation for developing initial educational programs and lifelong learning activities for nurses in this evolving field. Moreover, the importance that experts attach to AI competencies calls for a broader discussion about a potential shift in nursing responsibilities and tasks as healthcare becomes increasingly technologically advanced and data-driven, possibly leading to new roles within nursing.
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