INTRODUCTION
Competency-based assessment (CBA) is central to competency-based education (CBE) in nursing, yet the evidence about its effectiveness is fragmented across methods, settings, and outcomes.
AIM
To synthesize the strengths and limitations of CBA approaches in nursing education and to map them onto established educational frameworks.
METHODS
An umbrella review of reviews was conducted, including systematic, scoping, integrative, and theoretical reviews of CBA in pre-registration and early-career nursing. Searches of PubMed, CINAHL, Scopus, Web of Science, and ERIC (from inception to November 2025) were conducted in accordance with Joanna Briggs Institute and PRISMA guidance, with eligibility defined using the Population, Concept, and Context (PCC) framework, and data were integrated through narrative synthesis.
RESULTS
Twenty reviews, encompassing more than 700 primary studies, identified six CBA domains: simulation-based and Objective Structured Clinical Examination (OSCE) assessments, workplace-based assessments (WBAs), technology-enhanced assessments, learning-based and reflective assessments, research competency assessments, and program-level CBE evaluations. Across domains, CBAs predominantly targeted higher-order cognitive and performance outcomes aligned with the upper levels of Bloom's taxonomy and Miller's pyramid. Simulations and OSCEs showed strong validity and acceptability but were resource-intensive and often anxiety-provoking. Workplace-based tools captured authentic performance but were constrained by assessor variability and limited psychometric properties. Technology-enhanced and reflective approaches fostered engagement and self-regulation yet frequently relied on heterogeneous, weakly validated measures. Evidence for research competencies and program-level CBE remained limited and methodologically modest.
CONCLUSION
CBAs in nursing provide rich information on complex competencies but are unevenly developed. Integrated, theory-informed, and programmatic assessment systems are required to optimise their contribution to practice-ready graduates.
R. K. Ibrahim, A. Darwish, Lisa Bayliss-Pratt et al.· Nurse Education Today· 0 citations
BACKGROUND
Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous surveillance, recognise deterioration, coordinate escalation and translate protocols into bedside action. AI-CDSS may be particularly relevant when they support rather than replace clinical judgement.
AIM
To examine whether nurse-used AI-CDSS improve patient-important outcomes in acute and critical care contexts and summarise effects on care processes and nurse-reported outcomes.
STUDY DESIGN
Following PRISMA 2020 and a preregistered protocol, we searched eight databases and major trial registries for English-language studies from 1 January 2010 to 1 January 2026. Searches were conducted on 1 January 2026. We included randomised, quasi-experimental and adjusted cohort studies in which registered nurses or nursing teams were primary users of AI-CDSS generating patient-specific predictions or recommendations. Mortality was pooled using a random effects model; other outcomes were synthesised narratively.
RESULTS
Seven studies involving about 75 000 patients were included. Most evidence came from acute wards, intensive care units, sepsis, deterioration and delirium-prevention contexts, with additional home and palliative care evidence. Three mortality studies were pooled. Nurse-facing AI-CDSS were associated with lower hospital mortality (RR 0.68, 95% CI 0.53-0.87; I2 = 24%), although the prediction interval included possible no effect. Length of stay and protocol adherence generally improved when tools were embedded in nursing workflows. Nurse-reported outcomes were sparse.
CONCLUSION
Nurse-facing AI-CDSS may strengthen acute and critical care nursing by improving surveillance, escalation and protocol delivery for patients at risk of deterioration. Evidence is promising but limited by small study numbers, heterogeneous interventions and sparse nurse-reported outcomes. Critical care implementation should prioritise nurse-centred design, alert burden, equity, safety monitoring and rigorous evaluation before scale-up.
RELEVANCE TO CLINICAL PRACTICE
Nurse-used AI-CDSS show potential to improve patient outcomes and care processes, but evidence remains limited and context dependent.
W. Almagharbeh, S. Alkubati, A. A. Alasmari et al.· Nursing Critical Care· 0 citations
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