ArticleFrontiers in digital health2026
A cross-sectional analysis of AI readiness and attitudes among nurses in resource-limited Chinese county hospitals.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Change Fatigue and Attitudes Toward AI in County-Level Nurses: The Mediating Role of AI Literacy.Journal of nursing management · 2026Article
- Nurses' AI training acceptance and tool usage: a structural equation model of individual and hospital-level factors.Frontiers in public health · 2026Article
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9 authors.
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Abstract
Aim: To investigate the current situation of clinical nurses' attitudes towards artificial intelligence in county hospitals and analyze its influencing factors, so as to provide a reference for promoting the application of artificial intelligence technology in the field of primary medical care. Design: A descriptive, cross-sectional study. Methods: A total of 449 clinical nurses from a Chinese county-level B-level hospital in Nantong City were selected from August to September 2025 by convenience sampling, and the general information questionnaire, the Attitude Scale for the Application of Artificial Intelligence Technology in Nursing, the Artificial Intelligence Literacy Scale and the Change Fatigue Scale were used to investigate the influencing factors. Results: The total score of clinical nurses' attitudes toward AI was 45.17 ± 2.38, indicating a moderate level. Multiple linear regression analysis identified age, participation in AI-related training, education level, number of monthly night shifts, change fatigue, and total AI literacy score as significant determinants of AI attitudes (all Conclusion: The attitude of Chinese county-level clinical nurses towards AI is at a moderate level and is influenced by multiple modifiable factors. To enhance AI acceptance and facilitate its integration into primary care, we recommend implementing targeted AI training programs, improving AI literacy, optimizing scheduling to reduce night shift burdens, and proactively managing change fatigue.
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