Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A cross-sectional analysis of AI readiness and attitudes among nurses in resource-limited Chinese county hospitals
0
Zitationen
9
Autoren
2026
Jahr
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 P < 0.05). Collectively, these factors accounted for 60.6% of the total variance in AI attitude scores. 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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.423 Zit.