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To examine a model of the caregiving adaptation process among family caregivers supporting care recipients at home.
Global demand for the support of adults with long-term care needs and family caregivers is increasing. Caregivers' quality of life is affected by positive and negative appraisals of care; however, few studies have simultaneously investigated these factors.
A cross-sectional study.
The STROBE checklist for cross-sectional studies was followed. Seventy-four randomly selected home-visit nursing stations in Japan participated in this study from June 2023 to June 2024. A self-administered anonymous questionnaire was provided to family caregivers with care recipients at home. A total of 168 questionnaires were analysed. The variables included in the model were the European Quality of Life five-dimension five-level (EQ-5D-5L) instrument, positive and negative appraisals of care scale, four external resources and three internal resources, and six characteristics of caregivers and care recipients. Descriptive statistics and correlations between variables were analysed. The model was tested using structural equation modelling.
Family caregivers' negative appraisal of care directly and negatively affected quality of life, and positive appraisal of care had no statistically significant association with quality of life. Positive appraisal of care had a direct negative association with negative appraisal of care. External resources such as support from nurses directly affected the positive appraisal of care. Internal resources such as caregivers' coping strategies had a significant negative effect on negative appraisal of care.
The findings suggest that improving caregivers' quality of life requires support to decrease negative appraisal of care by increasing internal resources and increase positive appraisal of care by providing external resources. Understanding the caregivers' adaptation process model is essential to prevent the deterioration of their quality of life.
STROBE guidelines.
Seventy-four home visit nursing stations and participants who care for family members through home visit nursing were involved in the survey investigation and answering the questionnaires.
For supporting family caregivers' QoL, a reduction in negative appraisals of care is essential, increasing internal resources such as caregivers' coping and positive appraisal of care directly reduces negative appraisal of care.
The aim of this study is to assess nurse practitioner students' perceptions and engagement with Isabel's artificial intelligence (AI) based differential diagnosis tool to support their decision-making skills during their theoretical and clinical placement training.
This pilot study used a cross-sectional design.
Twenty-six nurse practitioner students provided feedback on their use of an AI differential diagnosis tool in both academic and clinical contexts. This survey used the Post-Study System Usability Questionnaire to assess the engagement levels and usability of the AI tool. Additional questions were included to evaluate the usage patterns, adequacy in training and confidence in diagnosis.
There were mixed engagement levels: 44.4% (n = 8/18) used Isabel in two subjects—typically one or both clinical placement units—and 27.8% (n = 5/18) in one subject; students most often used the tool to confirm differential diagnoses. Usability was rated positively with the disease ranking, red flag diagnosis and link to national guideline features demonstrating the highest student usage. While most students found the tool beneficial to use during clinical placement and completing university assignments, some reported challenges due to insufficient training, impacting confidence in clinical application.
Isabel has potential as a valuable educational tool in Nurse Practitioner programs, but successful implementation depends on adequate training and support. The findings highlight the importance of comprehensive training and support to maximise AI tool utilisation, with direct implications for programme curricula, clinical education strategies and potential improvements in diagnostic reasoning skills for future nurse practitioners.
This study provides an example of integrating artificial intelligence (AI) guided clinical decision-making training in nurse practitioner (NP) education. The findings can be used by educational institutions to trial similar AI-integrated learning approaches, enhancing diagnostic competence and potentially improving patient care outcomes.
The Study adhered to the STROBE checklist for reporting.
No patient or public contribution was made to this study.
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