Aims and objectives: Examine beliefs about EBP and its level of implementation among nurses working in nursing homes in a bilingual canton of Switzerland and explore associations between these aspects and nurses’ sociodemographic and professional characteristics.Background: Although evidence‐based practice (EBP) is recognised as an effective strategy for improving the quality and safety of care, little is known about its use in nursing homes. Nurses’ beliefs about EBP and their implementation of it in Switzerland’s nursing homes have never been explored.Design: An observational cross‐sectional study.Methods: Beliefs about and implementation of EBP were evaluated using validated French‐ and German‐language versions of the EBP Beliefs Scale and the EBP Implementation Scale, developed by Melnyk and Fineout‐Overholt (Melnyk, Fineout‐Overholt, & Mays, 2008, Worldviews on Evidence‐Based Nursing, 5, 208). The STROBE checklist for cross‐sectional studies was used in reporting this study.Results: The participation rate was 40.6% (N = 194). Most participants stated that they had some knowledge of EBP and held favourable beliefs about it. Nevertheless, 37.1% of participants found the concept complicated and 36.1% found it time‐consuming. Participants were more likely to implement stages in the EBP process linked to direct clinical practice rather than those which required scientific knowledge and skills.Conclusion: Most participants had favourable beliefs about EBP, but the level of implementation of EBP among nurses in their daily clinical practice was sub‐optimal.Relevance to clinical practice: A greater emphasis should be put on fostering the use of EBP among nurses working in nursing homes. This could be achieved via training and the development of individual, institutional and contextual strategies promoting the integration of EBP in clinical settings.

Background: People with dementia commonly display behavioral and psychological symptoms, which have multiple negative consequences. Artificial intelligence–based technologies (AITs) have the potential to support earlier detection of the behavioral and psychological symptoms of dementia (BPSD). The recent surge of interest in this topic underscores the need to comprehensively examine the existing evidence. Objective: This scoping review aimed to identify and summarize the types and uses of AITs currently used for the early detection of BPSD among people diagnosed with the disease. We also examined which health care professionals were involved, nursing involvement and experience, the care settings in which these technologies are used, and the characteristics of the BPSD that were assessed. Methods: Our scoping review was conducted in accordance with the Joanna Briggs Institute manual for scoping reviews. Searches were conducted in March 2025 in the following bibliographic databases: MEDLINE ALL Ovid, Embase, APA PsycINFO Ovid, CINAHL EBSCO, Web of Science Core Collection, the Cochrane Library Wiley, and ProQuest Dissertations and Theses A&I. Additional searches were performed using citation tracking strategies and by consulting the Association for Computing Machinery Digital Library. Eligible studies included primary research involving people with dementia and examining the use of AITs for the detection of BPSD in real-world care settings. Results: After screening 3670 articles for eligibility, the review includes 12 studies. The studies retained were conducted between 2012 and 2025 in 5 countries and encompassed a range of care settings. The AITs used were predominantly based on classic machine learning approaches and used information from environmental sensors, wearable devices, and data recording systems. These studies primarily assessed behavioral and physiological parameters and focused specifically on symptoms, such as agitation and aggression. None of the retained studies explored nurses’ roles or their specific skills in using these technologies. Conclusions: The use of AITs for managing BPSD represents an emerging field of research offering novel opportunities to enhance their detection in various health care contexts. We recommended that nurses be actively engaged in developing and assessing these technologies. Future research should prioritize investigations into how effective AITs are across diverse populations, whether they can have a long-term impact on managing BPSD, and whether they can improve the quality of life of patients and caregivers.

Background: The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence–based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early. Objective: The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early. Methods: A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach. Results: A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD. Conclusions: Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.

BACKGROUND The behavioral and psychological symptoms of dementia (BPSD) are common among people with dementia and have multiple negative consequences. Artificial intelligence–based technologies (AITs) have the potential to help nurses in the early prodromal detection of BPSD. Despite significant recent interest in the topic and the increasing number of available appropriate devices, little information is available on using AITs to help nurses striving to detect BPSD early. OBJECTIVE The aim of this study is to identify the number and characteristics of existing publications on introducing AITs to support nursing interventions to detect and manage BPSD early. METHODS A literature review of publications in the PubMed database referring to AITs and dementia was conducted in September 2023. A detailed analysis sought to identify the characteristics of these publications. The results were reported using a narrative approach. RESULTS A total of 25 publications from 14 countries were identified, with most describing prospective observational studies. We identified three categories of publications on using AITs and they are (1) predicting behaviors and the stages and progression of dementia, (2) screening and assessing clinical symptoms, and (3) managing dementia and BPSD. Most of the publications referred to managing dementia and BPSD. CONCLUSIONS Despite growing interest, most AITs currently in use are designed to support psychosocial approaches to treating and caring for existing clinical signs of BPSD. AITs thus remain undertested and underused for the early and real-time detection of BPSD. They could, nevertheless, provide nurses with accurate, reliable systems for assessing, monitoring, planning, and supporting safe therapeutic interventions.