中国医学生医学人工智能准备量表(MAIRS-MS)的翻译与心理测量验证

IF 3.9 2区 医学 Q1 NURSING
Xuancheng Chen, Yangyi Chen, Yuhuan Xie, Linan Cheng
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引用次数: 0

摘要

背景:随着人工智能(AI)快速融入医学教育,评估医学生的准备情况变得至关重要。这种准备不仅包括对人工智能工具的熟悉,还包括应用、评估和道德反思的能力。尽管国际上取得了进步,但中国目前缺乏一种有效的工具来系统地评估医学生对医疗人工智能的准备情况。因此,本研究旨在翻译、文化适应和评估医学学生的医学人工智能准备量表(MAIRS-MS)的心理测量特性。方法:按照Brislin的指南将MAIRS-MS翻译成中文,随后通过专家咨询进行文化适应。然后对30名医科学生进行了初步研究,以完善中文版本(C-MAIRS-MS)。本文于2025年3月至5月对516名医学本科学生进行了横断面调查。采用探索性因子分析(EFA)、验证性因子分析(CFA)、Cronbach's α系数、Spearman-Brown分半信度和类内相关系数(ICC)对C-MAIRS-MS的心理测量特性进行评价。结果:C-MAIRS-MS包括22个项目,量表内容效度指数(S-CVI)为0.982。EFA提取的4个因子解释总方差的65.274%,因子负荷范围为0.508 ~ 0.881。CFA结果表明,修正后的四因素模型拟合良好(χ²/df = 2.303, RMSEA = 0.071, CFI = 0.924, IFI = 0.925, TLI = 0.912),具有良好的结构效度。C-MAIRS-MS量表的Cronbach’s α系数、Spearman-Brown Split-half信度和ICC值分别为0.935、0.832和0.945,具有较好的信度。结论:C-MAIRS-MS表现出良好的心理测量特性,为评估医学生对医疗人工智能的准备程度提供了可靠的工具。除了个人评估之外,该量表还可以为课程制定提供信息,促进对学生进展的持续监测,并支持评估以人工智能为重点的教育方案,从而为教育工作者提供有价值的证据,指导医学教育中与人工智能相关的培训的设计和改进。临床试验号:不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.

Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.

Background: With the rapid integration of artificial intelligence (AI) into medical education, assessing medical students' readiness has become critical. This readiness encompasses not only familiarity with AI tools but also the ability to apply, evaluate, and ethically reflect on them. Despite international advances, China currently lacks a validated instrument to systematically evaluate medical students' readiness for medical AI. Therefore, this study aimed to translate, culturally adapt, and evaluate the psychometric properties of the Medical Artificial Intelligence Readiness Scale (MAIRS-MS) for Chinese medical students.

Methods: The MAIRS-MS was translated into Chinese following Brislin's guidelines, with subsequent cultural adaptation informed by expert consultation. A pilot study was then conducted with 30 medical students to refine the Chinese version (C-MAIRS-MS). A cross-sectional survey was conducted among 516 undergraduate medical students from March to May 2025. The psychometric properties of the C-MAIRS-MS were evaluated through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Cronbach's α coefficient, Spearman-Brown split-half reliability, and the intraclass correlation coefficient (ICC).

Results: The C-MAIRS-MS included 22 items with scale content validity index (S-CVI) of 0.982. EFA extracted four factors explaining 65.274% of the total variance, with factor loadings ranging from 0.508 to 0.881. CFA results indicating that the revised 4-factor model was well fitted (χ²/df = 2.303, RMSEA = 0.071, CFI = 0.924, IFI = 0.925, and TLI = 0.912), with good structural validity. The Cronbach's α coefficient, Spearman-Brown Split-half reliability, and ICC values for the C-MAIRS-MS were 0.935, 0.832, and 0.945, indicating the scale has good reliability.

Conclusion: The C-MAIRS-MS demonstrated sound psychometric properties and provides a reliable tool to assess medical students' readiness for medical AI. Beyond individual assessment, the scale can inform curriculum development, facilitate ongoing monitoring of students' progress, and support the evaluation of AI-focused educational programmes, thereby offering educators valuable evidence to guide the design and refinement of AI-related training in medical education.

Clinical trial number: Not applicable.

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来源期刊
BMC Nursing
BMC Nursing Nursing-General Nursing
CiteScore
3.90
自引率
6.20%
发文量
317
审稿时长
30 weeks
期刊介绍: BMC Nursing is an open access, peer-reviewed journal that considers articles on all aspects of nursing research, training, education and practice.
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