Nursing Reports

Background/Objectives: Caregivers play a central role in ensuring the safety of community-dwelling older adults receiving home-based care, yet gaps remain in ongoing educational support and communication with professionals. This protocol describes a two-phase study to customise an existing platform for use as a mobile health (mHealth) intervention supporting caregivers in delivering safer home-based care and Family and Community Nurses (FCNs) in supporting them, and to evaluate its feasibility, acceptability and usability. Methods: Phase 1 will apply a User-Centred Design (UCD) approach, articulated into four subphases, involving caregivers and a multidisciplinary panel of professionals from district-based health and social care services. Needs will be identified and prioritised through focus groups, informing the customisation and testing of the platform for use as an mHealth intervention. Phase 2 will adopt a convergent mixed-methods design within a Local Health Authority in Northern Italy, among a sample of at least 10 caregivers and a similar number of FCNs, over a 3-month data collection period. Acceptability and usability will be assessed as preliminary conditions for the adoption of a safety-oriented digital intervention, through quantitative self-reported measures, system-generated usage data, and semi-structured interviews. Conclusions: This study is expected to result in a customised platform for use as an mHealth intervention, together with preliminary evidence on its feasibility, acceptability and usability in supporting safer home-based care. Findings may inform future implementation strategies for digitally supported caregiving.

Background/Objectives: Nursing workforce shortages and increasing care complexity have intensified the need to understand how registered nurses allocate their working time and whether nursing expertise is used where it adds the greatest clinical value. This study empirically measured the distribution, frequency, and duration of observed nursing activities and examined differences across participating hospitals and medical–surgical units in Italy. Methods: A multicentre observational study was conducted across 13 medical and surgical units in five Italian teaching hospitals. Registered nurses were observed during complete morning and afternoon shifts over a three-week period. Activities were classified as direct care, indirect care, organisational activities, or non-patient care. Time allocation, activity frequency, and duration were examined across units and hospitals. The Enacted Scope of Practice model, Role Theory, and Job Demands–Resources Theory informed interpretation of the findings but were not used to classify individual activities. Results: Overall, 45% of cumulative recorded activity time was devoted to direct care, 24% to indirect care, 10% to organisational activities, and 21% to non-patient care. Direct care accounted for 51% of recorded activity time in medical units and 41% in surgical units, whereas indirect care represented 17% and 28%, respectively. Considerable hospital-level variability was observed, particularly for indirect, organisational, and non-patient care activities. Direct care was also the most frequent activity category, while organisational activities were less frequent but comparatively time-intensive. Conclusions: Nursing work extends substantially beyond bedside care and includes clinically relevant indirect and organisational activities. Interpreted through the Enacted Scope of Practice framework, the findings identify areas requiring further competency-based evaluations to support work redesigning that protects activities requiring registered nursing expertise, redistributes appropriate tasks, and reduces avoidable organisational inefficiencies.

Background: Artificial intelligence (AI) is increasingly integrated into nursing practice, yet nurses often demonstrate low to moderate AI knowledge, mixed attitudes, and inconsistent practice behaviors. Although individual studies have examined these domains, no review has comprehensively examined AI-related knowledge, attitudes, and practice (KAP) together with the emerging evidence on patient safety culture among nurses. This gap is significant because safe AI adoption in nursing depends on both technical competence and a strong safety culture across diverse clinical settings. Objective: To systematically map international evidence on nurses’, nursing students’, and other included participants’ AI-related KAP, and to examine how these factors may relate to patient safety culture. Because several eligible studies included multidisciplinary samples in which nurse-specific quantitative data were not reported separately (China, Bangladesh, Pakistan, and three studies from the Kingdom of Saudi Arabia [KSA]), only studies with extractable nurse-specific results were used to inform nurse-focused conclusions, and multidisciplinary findings were not attributed solely to nurses. Methods: This scoping review followed Arksey and O’Malley’s framework and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Searches of PubMed, CINAHL, Scopus, Web of Science, and ScienceDirect (2021–2026) identified studies examining AI-related perceptions among nurses and other healthcare participants when nurse-specific data were reported separately or extractable. Data extraction and narrative synthesis were conducted using Joanna Briggs Institute guidance. Results: Ten studies met the inclusion criteria and were included in the final review. Studies from China, Bangladesh, Pakistan, the KSA, Turkey, and the United Arab Emirates showed variable AI knowledge but predominantly positive attitudes. Ethical concerns—privacy, job displacement, and reduced empathy—were common. Younger age, higher education, and prior AI exposure were associated with more favorable perceptions. Nurses across regions emphasized the need for structured AI training. Only one study examined patient safety culture, and it reported that AI knowledge and attitudes significantly predicted patient safety culture, with attitude showing the stronger effect. However, because this evidence comes from a single cross-sectional study conducted in a multidisciplinary sample without nurse-specific results, it is insufficient to establish causal conclusions, and its findings cannot be generalized specifically to nurses. Conclusions: Nurses are generally receptive to AI but lack sufficient knowledge, training, and organizational support for safe implementation. Preliminary evidence suggests a positive association between AI-related knowledge, attitudes, and patient safety culture, but this relationship remains uncertain due to the extremely limited evidence base and the reliance on evidence that is not exclusively nurse-specific. Available studies indicate generally favorable attitudes toward AI alongside variable knowledge, limited formal training, and persistent ethical and organizational concerns, with only one eligible cross-sectional study linking these factors to patient safety culture. The conclusion regarding this association should not be attributed exclusively to nurses because the patient safety culture evidence was derived from a multidisciplinary sample. Future research should explore how AI competencies may interact with safety-related behaviors and organizational systems. Strengthening AI training, ethical guidance, and organizational support remains essential for safe adoption.