The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross-Sectional Study.

IF 3.4 3区医学 Q1 NURSING

Journal of Advanced Nursing Pub Date : 2025-09-25 DOI:10.1111/jan.70250

Xiangxia Liu,Yuxi Chen,Wenqing Guan,Pingping Jiang,Lihui Yan,Miao Fan,Qi Zhou

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引用次数: 0

Abstract

AIM This study aimed to validate the mediating role of nurses' AI trust in the relationship between AI uncertainties and AI competence. DESIGN A cross-sectional study. METHODS A purposive sample of 550 registered nurses with at least 1 year of clinical experience from three tertiary and two secondary hospitals in Jinan and Hangzhou, China, was used. Data were collected using structured questionnaires assessing AI uncertainty, trust and competence. Demographic data included gender, age, education level, years of clinical experience, professional title and hospital level. Mediation analysis. RESULTS Most nurses were from tertiary hospitals (88.9%), held a bachelor's degree (87.6%), and had over 6 years of experience. The mediating role of AI trust between AI uncertainties and AI competence is validated. AI uncertainties affected AI trust (B = 0.39, p < 0.0001), explaining 10% of the variance. AI uncertainties and AI trust affected AI competence (B = 0.25 and 0.67, p < 0.0001), explaining 63% of the variation. AI trust's total effect was 0.51, comprising direct and indirect effects of 0.25 and 0.26, respectively. CONCLUSION Hospitals can reduce uncertainty through an AI-transparent decision-making process, providing clinical examples of AI and training nurses to use AI, thereby increasing trust. Second, AI systems should be designed to consider nurses' psychological safety needs. Hospital administrators utilise optimised AI technology training and promotional techniques to mitigate nurses' resistance to AI and enhance their positive perceptions of AI competence through trust-building mechanisms. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE Impact: Enhancing nurses' AI trust can reduce uncertainty and improve their competence in clinical use. Strategies such as transparency, explainability and training programmes are crucial for improving AI implementation in healthcare. NO PATIENT OR PUBLIC CONTRIBUTION This study focused solely on clinical nurses and did not include patients or the public. REPORTING METHOD The study adhered to STROBE guidelines.

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护士人工智能信任对人工智能不确定性和人工智能胜任力的中介作用：一项横断面研究

本研究旨在验证护士人工智能信任在人工智能不确定性与人工智能能力关系中的中介作用。设计横断面研究。方法对济南和杭州三所三级医院和两所二级医院的550名具有1年以上临床经验的注册护士进行目的性抽样。使用结构化问卷收集数据，评估人工智能的不确定性、信任和能力。人口统计资料包括性别、年龄、文化程度、临床经验年限、职称、医院级别。中介分析。结果三级医院护士占88.9%，本科学历占87.6%，工作年限在6年以上。验证了人工智能信任在人工智能不确定性与人工智能能力之间的中介作用。人工智能不确定性影响人工智能信任（B = 0.39, p < 0.0001），解释了10%的方差。人工智能不确定性和人工智能信任影响人工智能能力（B = 0.25和0.67,p < 0.0001），解释了63%的变化。AI信托的总效应为0.51，其中直接效应为0.25，间接效应为0.26。结论医院可以通过人工智能透明的决策过程、提供人工智能的临床案例和培训护士使用人工智能来减少不确定性，从而增加信任。其次，人工智能系统的设计应考虑护士的心理安全需求。医院管理人员利用优化的人工智能技术培训和推广技术来减轻护士对人工智能的抵制，并通过建立信任机制增强他们对人工智能能力的积极看法。对职业和/或患者护理的影响：增强护士对人工智能的信任可以减少不确定性，提高他们在临床应用中的能力。透明度、可解释性和培训方案等战略对于改善人工智能在医疗保健领域的实施至关重要。无患者或公众贡献本研究仅关注临床护士，不包括患者或公众。报告方法：本研究遵循STROBE指南。

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来源期刊

Journal of Advanced Nursing 医学-护理

CiteScore

6.40

自引率

7.90%

发文量

369

审稿时长

3 months

期刊介绍： The Journal of Advanced Nursing (JAN) contributes to the advancement of evidence-based nursing, midwifery and healthcare by disseminating high quality research and scholarship of contemporary relevance and with potential to advance knowledge for practice, education, management or policy. All JAN papers are required to have a sound scientific, evidential, theoretical or philosophical base and to be critical, questioning and scholarly in approach. As an international journal, JAN promotes diversity of research and scholarship in terms of culture, paradigm and healthcare context. For JAN’s worldwide readership, authors are expected to make clear the wider international relevance of their work and to demonstrate sensitivity to cultural considerations and differences.