# A cross-sectional analysis of AI readiness and attitudes among nurses in resource-limited Chinese county hospitals

## TL;DR

This study examines how clinical nurses in Chinese county hospitals feel about artificial intelligence and what factors influence their attitudes.

## Contribution

The study identifies modifiable factors influencing AI attitudes among nurses in resource-limited hospitals.

## Key findings

- Clinical nurses' attitudes toward AI are at a moderate level.
- Age, AI training, education, night shifts, change fatigue, and AI literacy significantly influence AI attitudes.
- These factors explain 60.6% of the variance in AI attitude scores.

## Abstract

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.

A descriptive, cross-sectional study.

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.

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.

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.