ArticleBMC nursing2026
A concept analysis of artificial intelligence anxiety among nurses based on Walker and Avant's method.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
Funding
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Abstract
backgroundThe growing integration of artificial intelligence in nursing has been accompanied by increased anxiety among nurses, even as they aim to enhance the quality and efficiency of care. Artificial intelligence anxiety (AI anxiety) constitutes another significant factor influencing the decline in nursing work quality and the deterioration of nurses' physical and mental health. However, AI anxiety among nurses is still not clearly conceptualized and has received limited empirical examination.
objectiveThis study clarifies the concept of AI anxiety among nurses, offering a comprehensive understanding for nursing managers and researchers to support relevant measurements and interventions. DATA SOURCES: This study retrieved studies from inception to July 2026 across PubMed, CINAHL (via EBSCO), ELSEVIER ScienceDirect, ProQuest, Embase, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), China Wanfang Database, and China VIP Database. Relevant references were tracked. This systematic database search aimed to comprehensively collect various research and academic literature to provide evidence and support for conceptual analysis. A total of 31 articles were included in the review.
methodsThis study employed Walker and Avant's concept analysis method.
resultsA total of 31 papers were included in the study. The four primary characteristics of nurse AI anxiety are: Technical concerns, patient safety concerns, perceived ethical burden, and sense of occupational devaluation. The antecedents are categorized into individual, environmental and related to artificial intelligence. The consequences of AI anxiety are distinguished into individual-level and hospital-level impacts.
conclusionThis study provides a comprehensive understanding of the concept of AI anxiety among nurses by outlining its antecedents, attributes, and consequences. The conceptualization of AI anxiety will facilitate future research aimed at establishing effective prevention strategies.
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Registered trials
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