Conectar
The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, holds great potential to enhance nurses' daily work if implemented systematically, paving the way for a promising future in healthcare.
To identify and describe AI technologies for nurses' clinical decision-making in healthcare settings.
A systematic literature review.
CINAHL, PubMed, Scopus, ProQuest, and Medic were searched for studies with experimental design published between 2005 and 2024.
JBI guidelines guided the review. At least two researchers independently assessed the eligibility of the studies based on title, abstract, and full text, as well as the methodological quality of the studies. Narrative analysis of the study findings was performed.
Eight studies showed AI tools improved decision-making, patient care, and staff performance. A discharge support system reduced 30-day readmissions from 22.2% to 9.4% (p = 0.015); a deterioration algorithm cut time to contact senior staff (p = 0.040) and order tests (p = 0.049). Neonatal resuscitation accuracy rose to 94%–95% versus 55%–80% (p < 0.001); seizure assessment confidence improved (p = 0.01); pressure ulcer prevention (p = 0.002) and visual differentiation (p < 0.001) improved. Documentation quality increased (p < 0.001).
AI integration in nursing has the potential to optimise decision-making, improve patient care quality, and enhance workflow efficiency. Ethical considerations must address transparency, bias mitigation, data privacy, and accountability in AI-driven decisions, ensuring patient safety and trust while supporting equitable, evidence-based care delivery.
The findings underline the transformative role of AI in addressing pressing nursing challenges such as staffing shortages, workload management, and error reduction. By supporting clinical decision-making and workflow efficiency, AI can enhance patient safety, care quality, and nurses' capacity to focus on direct patient care. A stronger emphasis on research and implementation will help bridge usability and scalability gaps, ensuring sustainable integration of AI across diverse healthcare settings.
To summarise the effect of mentoring within mentoring programmes on the retention and turnover of newly graduated nurses in healthcare settings.
An umbrella review.
Two independent reviewers screened the titles, abstracts and full texts for eligibility and critically appraised the included reviews using the JBI critical appraisal. The findings were tabulated and synthesised.
The search was conducted in five electronic databases (CINAHL, OvidMedline, ProQuest, Scopus, Cochrane and Medic) in November 2023.
Out of 450 Papers, 13 systematic and integrative reviews were included. Thirteen mentoring programmes were identified and categorised into three groups based on their content: didactic mentoring programmes, interaction-based mentoring programmes and combined mentoring programmes. Across these programme types, retention among newly graduated nurses ranged from 72% to 100% at the 1-year mark and 70% to 98% at 2 years. Turnover rates showed consistent reductions, with post-intervention rates ranging from 3.5% to 20% compared to pre-intervention rates of up to 50%. Several studies reported statistically significant improvements in retention and turnover, particularly in programmes integrating structured education and preceptorship models.
Several different mentoring programmes have been developed to support the transition of newly graduated nurses. Mentoring programmes that provide ongoing support and structured guidance increase retention and reduce turnover among newly graduated nurses.
Effective mentoring programmes are key to ensuring high-quality patient care and a sufficient supply of qualified nurses in the future.
The findings can provide information for developing transition support and mentoring programmes for newly graduated nurses.
This umbrella review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.
No patient or public contribution.
The umbrella review protocol was registered in PROSPERO: CRD42023478044.
To describe and compare the Evidence-Based HealthCare (EBHC) competence of Advanced Practice Nurses (APNs), and the factors associated with it in Finland and Singapore.
A descriptive and analytical cross-sectional study.
Data were collected from APNs working in healthcare in Finland (n = 157) or Singapore (n = 99) between May 2023 and October 2023 using a self-assessment instrument to measure EBHC competence (EBHC-Comp-APN) and an EBHC knowledge test. The data were analysed using descriptive statistics, analysis of variance, K-mean cluster and multivariate analyses.
The self-assessments of APNs working in Finland and Singapore regarding their EBHC competence level varied and three distinct profiles of APNs' EBHC competence were identified in both countries. The strongest EBHC competence was in ‘The Knowledge Needs Related to Global Health’, while the weakest in ‘Evidence Synthesis and Transfer’. The country-specific differences were identified in factors associated with EBHC competence.
The EBHC competencies of APNs vary widely and require planned and needs-driven development. In connection with the development of EBHC competence, the factors related to competence should be considered country-by-country.
The APN's EBHC competence should be systematically developed considering the factors associated with and the current level of EBHC competence.
The level of EBHC competence of APNs and associated factors should be identified when developing their competence and role in collaboration with APNs, leaders of healthcare and education organisations and policy makers. In addition, research into APNs' EBHC competence should continue.
The STROBE checklist was used in the reporting of the study.
No patient or public contribution.
To develop and test a Family and Community Nursing—Advanced Practice Scale.
A cross-sectional and methodological scale validation design, following classical test theory.
Three phases, the first of which involved scale development, including item generation. Phase two assessed the content validity index. The third phase involved a cross-sectional survey to establish construct validity, content validity, internal consistency reliability, and exploratory factor analysis.
The Family and Community Nursing Advanced Practice Scale has good construct validity, with the final scale consisting of 5 domains and 27 items. This was confirmed by both the exploratory and confirmatory factor analysis. The Cronbach's Alpha is very good, suggesting that the scale is reliable. When comparing family practice advanced practice nurses with those working in the community, the results show that scores are similar except for clinical reasoning and health promotion, which consistently showed statistically significant higher scores among the family practice nurses. While community nurses scored higher on items in the leading practice domain reflecting their role in a wider team of nurses.
This study developed and psychometrically tested the Family and Community Nursing—Advanced Practice Scale. The scale has good reliability, and analysis of the construct validity reveals five domains of advanced practice among this practitioner group.
The study suggests that advanced practice nurses working in community roles perform similar activities to those working in family practice in the United Kingdom. However, activity related to research was less evident.
The study examined the scope of the advanced practice nurse role in family and community nursing. The study illustrated practice across five domains: clinical care, leading practice, clinical reasoning, health promotion, and ethics. The family practice and wider community roles were largely homogenous, with only two items showing a statistically significant difference in scores.
STROBE guidelines for cross-sectional studies.
No patient or public contribution.
The current study aimed to identify digital health literacy levels among nurses with respect to their education, role and attitude towards digital technologies.
Cross-sectional study.
Through convenience sampling, all Registered Nurses, managers/leaders and nurse researchers employed in Hospitals, University Hospitals and Districts were recruited and surveyed using an online questionnaire. The data collection tool assessed: (I) demographics, (II) Digital Health Literacy (DHL) with the Health Literacy Survey19 Digital (HLS19-DIGI) instrument including DHL dealing with digital health information (HL-DIGI), interaction with digital resources for health (HL-DIGI-INT) and use of digital devices for health (HL-DIGI-DD); (III) attitudes on the use of digital technologies in clinical practice. The multiple correspondence analysis was applied to identify three clusters for the education/professional role (A, B, C) and three for digital technologies' use (1, 2, 3). The one-way nonparametric analysis of variance (Kruskal–Wallis test) was applied to compare HL-DIGI, HL-DIGI-INT and the HL-DIGI-DD scores among clusters.
Among 551 participants, the median scores of the HL-DIGI, the HL-DIGI-INT and the HL-DIGI-DD questionnaires were 70.2, 72 and 2.00, respectively. The distribution in the clusters ‘educational/professional role’ was A, (58.8%); B, (16.5%); and C, (24.7%). Nurses in a managerial or coordinator role and with a postgraduate degree used digital resources with greater frequency. The distribution in the clusters ‘use of digital technologies’ was: 1, (54.6%); 2, (12.2%); and 3, (33.2%). The HL-DIGI-DD and HL-DIGI scores of clusters 1, 2 and 3 differed significantly.
DHL among nurses is strongly influenced by the education level, professional role, habits and attitude towards digital technologies. Nurses with coordinator roles used digital technologies with greater frequency and had a higher level of DHL.
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used for reporting.
No Patient or Public Contribution.
Trial Registration: Local Ethical Committee of the Polyclinic of Bari (code: DHL7454, date: 21/09/22)
Evidence-based healthcare (EBHC) enables consistent and effective healthcare that prioritises patient safety. The competencies of advanced practice nurses (APNs) are essential for implementing EBHC because their professional duties include promoting EBHC.
To identify, critically appraise, and synthesise the best available evidence concerning the EBHC competence of APNs and associated factors.
A systematic review.
CINAHL, PubMed, Scopus, Medic, ProQuest, and MedNar.
Databases were searched for studies (until 19 September 2023) that examined the EBHC competence and associated factors of APNs were included. Quantitative studies published in English, Swedish and Finnish were included. We followed the JBI methodology for systematic review and performed a narrative synthesis.
The review included 12 quantitative studies, using 15 different instruments, and involved 3163 participants. The quality of the studies was fair. The APNs' EBHC competence areas were categorised into five segments according to the JBI EBHC model. The strongest areas of competencies were in global health as a goal, transferring and implementing evidence, while the weakest were generating and synthesising evidence. Evidence on factors influencing APNs' EBHC competencies was contradictory, but higher levels of education and the presence of an organisational research council may be positively associated with APNs' EBHC competencies.
The development of EBHC competencies for APNs should prioritise evidence generation and synthesis. Elevating the education level of APNs and establishing a Research Council within the organisation can potentially enhance the EBHC competence of APNs.
We should consider weaknesses in EBHC competence when developing education and practical exercises for APNs. This approach will promote the development of APNs' EBHC competence and EBHC implementation in nursing practice.
The review was registered in PROSPERO (CRD42021226578), and reporting followed the PRISMA checklist.
None.
FreshRSS 