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Background <p>As artificial intelligence (AI) becomes increasingly integrated into healthcare, nurses need to recognise and respond to its ethical implications. However, validated Chinese-language instruments for assessing nurses’ ethical awareness of AI use remain limited. This study aimed to translate, culturally adapt, and psychometrically evaluate the Chinese version of the Nurses’ AI Ethical Awareness Scale.</p> Methods <p>A methodological study was conducted among 455 nurses from a tertiary hospital in Shenzhen, China, between December 2025 and January 2026. The original scale was translated and cross-culturally adapted using a Brislin-style procedure. Participants were randomly divided into an exploratory factor analysis (EFA)/model-refinement subsample (<i>n</i> = 227) and a confirmatory factor analysis (CFA) subsample (<i>n</i> = 228). Item analysis, EFA, candidate-version comparison, CFA, reliability testing, and validity assessment were performed.</p> Results <p>The data were suitable for factor analysis (KMO = 0.9509; Bartlett’s test χ² = 4943.71, <i>P</i> &lt; 0.001). The scale was reduced from 21 to 15 items, forming three closely related content domains. In the CFA subsample, the correlated three-factor and second-order models showed better relative fit than the one-factor and alternative two-factor models. The bifactor model showed the most favourable relative fit among the tested models, with favourable incremental fit indices (CFI = 0.9587, TLI = 0.9421), although RMSEA remained elevated (RMSEA = 0.0967). In the correlated three-factor CFA model, standardised factor loadings ranged from 0.7201 to 0.9455. In the full sample, internal consistency was high for the final 15-item scale (Cronbach’s α = 0.9669). Discriminant validity was only partially supported, whereas bifactor indices suggested that the total score captured most of the common variance (ωh = 0.9459; ECV = 0.8596).</p> Conclusions <p>The adapted 15-item Chinese version showed high internal consistency and preliminary validity evidence among hospital nurses. Its three closely related content domains were largely explained by a dominant general factor, supporting total-score interpretation as the primary approach and cautious use of subscale scores. Because privacy, confidentiality, and data-governance items were not retained, the scale should be interpreted as assessing practice-facing aspects of AI ethical awareness. Further multicentre validation is needed.</p>

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Chinese version of the Nurses’ AI Ethical Awareness Scale: translation, cross-cultural adaptation, and psychometric evaluation among hospital nurses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Mali Zhang,
  • Yanan Wei,
  • Fangfang Feng,
  • He Tian,
  • Yuandi Yang,
  • Li Yan,
  • Yu Cai,
  • Dan Wang,
  • Chenqian Zhu,
  • Cong Yu

摘要

Background

As artificial intelligence (AI) becomes increasingly integrated into healthcare, nurses need to recognise and respond to its ethical implications. However, validated Chinese-language instruments for assessing nurses’ ethical awareness of AI use remain limited. This study aimed to translate, culturally adapt, and psychometrically evaluate the Chinese version of the Nurses’ AI Ethical Awareness Scale.

Methods

A methodological study was conducted among 455 nurses from a tertiary hospital in Shenzhen, China, between December 2025 and January 2026. The original scale was translated and cross-culturally adapted using a Brislin-style procedure. Participants were randomly divided into an exploratory factor analysis (EFA)/model-refinement subsample (n = 227) and a confirmatory factor analysis (CFA) subsample (n = 228). Item analysis, EFA, candidate-version comparison, CFA, reliability testing, and validity assessment were performed.

Results

The data were suitable for factor analysis (KMO = 0.9509; Bartlett’s test χ² = 4943.71, P < 0.001). The scale was reduced from 21 to 15 items, forming three closely related content domains. In the CFA subsample, the correlated three-factor and second-order models showed better relative fit than the one-factor and alternative two-factor models. The bifactor model showed the most favourable relative fit among the tested models, with favourable incremental fit indices (CFI = 0.9587, TLI = 0.9421), although RMSEA remained elevated (RMSEA = 0.0967). In the correlated three-factor CFA model, standardised factor loadings ranged from 0.7201 to 0.9455. In the full sample, internal consistency was high for the final 15-item scale (Cronbach’s α = 0.9669). Discriminant validity was only partially supported, whereas bifactor indices suggested that the total score captured most of the common variance (ωh = 0.9459; ECV = 0.8596).

Conclusions

The adapted 15-item Chinese version showed high internal consistency and preliminary validity evidence among hospital nurses. Its three closely related content domains were largely explained by a dominant general factor, supporting total-score interpretation as the primary approach and cautious use of subscale scores. Because privacy, confidentiality, and data-governance items were not retained, the scale should be interpreted as assessing practice-facing aspects of AI ethical awareness. Further multicentre validation is needed.