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Article(id=1322927421342745362, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924976893993108, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.16.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760198400000, receivedDateStr=2025-10-12, revisedDate=1785168000000, revisedDateStr=2026-07-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340076051, onlineDateStr=2026-10-30, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340076051, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340076051, creator=13701087609, updateTime=1793340076051, updator=13701087609, issue=Issue{id=1322924976893993108, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='16', pageStart='2769', pageEnd='2960', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339493249, creator='13701087609', updateTime=1793343103544, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940119640047671, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924976893993108, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940119640047672, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322924976893993108, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2797, endPage=2803, ext={EN=ArticleExt(id=1322927421556654867, articleId=1322927421342745362, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the latent profiles of artificial intelligence anxiety among undergraduate nursing students,and to explore the influencing factors of artificial intelligence anxiety among different profiles of nursing students.

Methods

Using the convenience sampling method,from April to May 2025,a total of 720 undergraduate nursing students from three medical colleges in Hubei province were selected as the research subjects.The general information questionnaire,Artificial Intelligence Anxiety Scale(AIAS) and Medical Artificial Intelligence Readiness Scale(MAIRS) were used for the investigation.

Results

The score of artificial intelligence anxiety among undergraduate nursing students was 86.00±20.95.They could be divided into three latent profiles:low anxiety-rational alertness type (10.4%), medium anxiety-risk sensitivity type(65.3%),and high anxiety-generalized fear type(24.3%).Logistic regression analysis results showed that grade,the willingness to try using artificial intelligence tools in clinical practice,and medical artificial intelligence readiness were the influencing factors of artificial intelligence anxiety among undergraduate nursing students(all P<0.05).

Conclusion

The artificial intelligence anxiety among undergraduate nursing students is at a moderately high level.There is heterogeneity.Nursing educators need to develop precise intervention plans for undergraduate nursing students based on the influencing factors,so as to help them better adapt to the nursing education and career development requirements in the era of intelligence.

, authors=Li SHANG, Jiayi HU, Lujia BI, Lanlan XU, authorsList=Li SHANG, Jiayi HU, Lujia BI, Lanlan XU, authorCompany=null, correspAuthors=Lanlan XU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322927422135468822, articleId=1322927421342745362, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=本科护生人工智能焦虑的潜在剖面分析, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

分析本科护生人工智能焦虑的潜在剖面，并探讨不同剖面护生人工智能焦虑的影响因素。

方法

采用便利抽样法，于2025年4月—5月，选取湖北省3所医学院校的720名本科护生作为研究对象，应用一般资料调查表、人工智能焦虑量表、医学人工智能准备度量表进行调查。

结果

本科护生人工智能焦虑得分为（86.00±20.95）分，可分为低焦虑-理性警觉型（10.4%）、中焦虑-风险敏感型（65.3%）和高焦虑-泛化恐惧型（24.3%）3个潜在剖面。Logistic回归分析结果显示，年级、临床实践中尝试使用人工智能工具的意愿、医学人工智能准备度是本科护生人工智能焦虑的影响因素（均P<0.05）。

结论

本科护生人工智能焦虑处于中等偏高水平，且存在群体异质性。护理教育者需根据影响因素为本科护生制定精准干预方案，帮助其更好地适应智能时代的护理教育与职业发展需求。

, authors=商丽, 胡佳一, 毕璐佳, 徐兰兰*, *, authorsList=商丽, 胡佳一, 毕璐佳, 徐兰兰, authorCompany=null, correspAuthors=徐兰兰, authorNote=

商丽，副教授，硕士研究生

, correspAuthorsNote=
徐兰兰，E-mail：
XU　Lanlan， E-mail：
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development and deployment[J].International Journal of Advanced Engineering Technologies and Innovations,2023,1(1):319-353., articleTitle=Evaluating the impact of data protection regulations on AI development and deployment, refAbstract=null), Reference(id=1322927430356304736, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, doi=null, pmid=null, pmcid=null, year=2025, volume=13, issue=2, pageStart=82, pageEnd=null, url=null, language=null, rfNumber=29, rfOrder=28, authorNames=Frenkenberg A, Hochman G, journalName=Systems, refType=null, unstructuredReference=Frenkenberg A,Hochman G.It's scary to use it,it's scary to refuse it:the psychological dimensions of AI adoption--anxiety,motives,and dependency[J].Systems,2025,13(2):82., articleTitle=It's scary to use it,it's scary to refuse it:the psychological dimensions of AI adoption--anxiety,motives,and dependency, refAbstract=null), Reference(id=1322927430423413601, tenantId=1146029695717560320, 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Hubei University of Medicine, Hubei 442000 China), AuthorCompanyExt(id=1322927422357766937, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, companyId=1322927422336795415, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1湖北医药学院护理学院，湖北 442000)])], figs=[ArticleFig(id=1322927425692238647, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=EN, label=Figure 1, caption=Latent profile characteristics of artificial intelligence anxiety among undergraduate nursing students, figureFileSmall=nfm9x48CvokNGymEkwZf9Q==, figureFileBig=VZh+FUpBCJIHASQ/WEAtdw==, tableContent=null), ArticleFig(id=1322927425759347512, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=CN, label=图1, caption=本科护生人工智能焦虑的潜在剖面特征, figureFileSmall=nfm9x48CvokNGymEkwZf9Q==, figureFileBig=VZh+FUpBCJIHASQ/WEAtdw==, tableContent=null), ArticleFig(id=1322927425943896889, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=EN, label=Table 1, caption=

Chinese version of AIAS scores for undergraduate nursing students（x±s,n=720）

, figureFileSmall=null, figureFileBig=null, tableContent=
项目条目数（个）得分条目均分
学习维度832.19±9.954.02±1.24
工作替代维度627.01±6.914.50±1.15
社会技术盲点维度418.37±4.544.59±1.14
人工智能配置维度28.44±2.474.22±1.24
总分2086.00±20.954.30±1.05
), ArticleFig(id=1322927426036171578, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=CN, label=表1, caption=

本科护生中文版AIAS得分（x±s,n=720）

, figureFileSmall=null, figureFileBig=null, tableContent=
项目条目数（个）得分条目均分
学习维度832.19±9.954.02±1.24
工作替代维度627.01±6.914.50±1.15
社会技术盲点维度418.37±4.544.59±1.14
人工智能配置维度28.44±2.474.22±1.24
总分2086.00±20.954.30±1.05
), ArticleFig(id=1322927426103280443, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=EN, label=Table 2, caption=

Fitting indicators of the latent profile model for anxiety related to artificial intelligence among undergraduate nursing students

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICaBICEntropyP类别概率
LMRTBLRT
模型149 103.72949 286.89949 159.888
模型244 216.27644 495.61044 301.9180.941<0.001<0.0010.614/0.386
模型340 527.01140 902.51040 642.1370.976<0.001<0.0010.104/0.653/0.243
模型438 252.92438 724.58738 397.5330.970<0.001<0.0010.099/0.167/0.506/0.228
模型537 013.59537 581.42337 187.6880.9630.646<0.0010.067/0.157/0.415/0.254/0.107
), ArticleFig(id=1322927426170389308, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=CN, label=表2, caption=

本科护生人工智能焦虑的潜在剖面模型拟合指标

, figureFileSmall=null, figureFileBig=null, tableContent=
模型AICBICaBICEntropyP类别概率
LMRTBLRT
模型149 103.72949 286.89949 159.888
模型244 216.27644 495.61044 301.9180.941<0.001<0.0010.614/0.386
模型340 527.01140 902.51040 642.1370.976<0.001<0.0010.104/0.653/0.243
模型438 252.92438 724.58738 397.5330.970<0.001<0.0010.099/0.167/0.506/0.228
模型537 013.59537 581.42337 187.6880.9630.646<0.0010.067/0.157/0.415/0.254/0.107
), ArticleFig(id=1322927426241692477, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=EN, label=Table 3, caption=

Univariate analysis of latent influencing factors of artificial intelligence anxiety among undergraduate nursing students

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类人数低焦虑-理性
警觉型(n=75)		中焦虑-风险
敏感型(n=470)		高焦虑-泛化
恐惧型(n=175)		统计值P
年级（人）大学一年级2012414433χ²=45.188<0.001
大学二年级2071715733
大学三年级147129144
大学四年级165227865
学习成绩（人）优秀102184836χ²=23.807<0.001
良好3223220684
中等2642119449
较差324226
每周愿意投入学习医学人工智能知识的时间（人）<3 h45742314101χ²=12.1980.016
3~5 h2012312751
>5 h62102923
临床实践中尝试使用人工智能工具的意愿（人）愿意57469359146χ²=11.7230.003
不愿意146611129
医学人工智能准备度（分）83(66，88)69(66，78)82(70，88)H=65.165<0.001
), ArticleFig(id=1322927426333967166, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322927421342745362, language=CN, label=表3, caption=

本科护生人工智能焦虑潜在剖面影响因素的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量分类人数低焦虑-理性
警觉型(n=75)		中焦虑-风险
敏感型(n=470)		高焦虑-泛化
恐惧型(n=175)		统计值P
年级（人）大学一年级2012414433χ²=45.188<0.001
大学二年级2071715733
大学三年级147129144
大学四年级165227865
学习成绩（人）优秀102184836χ²=23.807<0.001
良好3223220684
中等2642119449
较差324226
每周愿意投入学习医学人工智能知识的时间（人）<3 h45742314101χ²=12.1980.016
3~5 h2012312751
>5 h62102923
临床实践中尝试使用人工智能工具的意愿（人）愿意57469359146χ²=11.7230.003
不愿意146611129
医学人工智能准备度（分）83(66，88)69(66，78)82(70，88)H=65.165<0.001
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Logistic regression analysis of latent influencing factors of artificial intelligence anxiety among undergraduate nursing students

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组别变量回归系数标准误Wald χ²值POR值95%CI
C2与C1比较①常数项1.7900.7935.1000.024
医学人工智能准备度-0.3660.1327.6630.0060.693[0.535，0.899]
临床实践中尝试使用人工智能工具的意愿（愿意）-1.1140.4506.1280.0130.328[0.136，0.793]
C3与C1比较①常数项0.5010.8960.3130.576
医学人工智能准备度0.3090.1464.4720.0341.363[1.023，1.815]
临床实践中尝试使用人工智能工具的意愿（愿意）-1.0870.4855.0180.0250.337[0.130，0.873]
大学三年级1.0960.4326.4220.0112.992[1.282，6.982]
大学四年级0.9610.3975.8540.0162.616[1.200，5.699]
C2与C3比较②常数项1.2890.6194.3310.037
医学人工智能准备度-0.6750.10640.535<0.0010.509[0.413，0.627]
大学三年级-0.7800.2817.6740.0060.459[0.264，0.796]
大学四年级-1.0600.28214.177<0.0010.346[0.199，0.602]
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本科护生人工智能焦虑潜在剖面影响因素的Logistic回归分析

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组别变量回归系数标准误Wald χ²值POR值95%CI
C2与C1比较①常数项1.7900.7935.1000.024
医学人工智能准备度-0.3660.1327.6630.0060.693[0.535，0.899]
临床实践中尝试使用人工智能工具的意愿（愿意）-1.1140.4506.1280.0130.328[0.136，0.793]
C3与C1比较①常数项0.5010.8960.3130.576
医学人工智能准备度0.3090.1464.4720.0341.363[1.023，1.815]
临床实践中尝试使用人工智能工具的意愿（愿意）-1.0870.4855.0180.0250.337[0.130，0.873]
大学三年级1.0960.4326.4220.0112.992[1.282，6.982]
大学四年级0.9610.3975.8540.0162.616[1.200，5.699]
C2与C3比较②常数项1.2890.6194.3310.037
医学人工智能准备度-0.6750.10640.535<0.0010.509[0.413，0.627]
大学三年级-0.7800.2817.6740.0060.459[0.264，0.796]
大学四年级-1.0600.28214.177<0.0010.346[0.199，0.602]
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本科护生人工智能焦虑的潜在剖面分析
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商丽 , 胡佳一 , 毕璐佳 , 徐兰兰 *, *
护理研究 | 科研论著 2026,40(16): 2797-2803
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护理研究 |科研论著 2026 , 40 (16) : 2797 -2803
本科护生人工智能焦虑的潜在剖面分析
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商丽, 胡佳一, 毕璐佳, 徐兰兰*, *
作者信息
  • 1湖北医药学院护理学院，湖北 442000
通讯作者:
徐兰兰，E-mail：
XU　Lanlan， E-mail：
作者简介:

商丽，副教授，硕士研究生

Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students
Li SHANG, Jiayi HU, Lujia BI, Lanlan XU
Affiliations
  • 1School of Nursing, Hubei University of Medicine, Hubei 442000 China
出版时间: 2026-08-25 doi: 10.12102/j.issn.1009-6493.2026.16.005
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摘要
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目的

分析本科护生人工智能焦虑的潜在剖面，并探讨不同剖面护生人工智能焦虑的影响因素。

方法

采用便利抽样法，于2025年4月—5月，选取湖北省3所医学院校的720名本科护生作为研究对象，应用一般资料调查表、人工智能焦虑量表、医学人工智能准备度量表进行调查。

结果

本科护生人工智能焦虑得分为（86.00±20.95）分，可分为低焦虑-理性警觉型（10.4%）、中焦虑-风险敏感型（65.3%）和高焦虑-泛化恐惧型（24.3%）3个潜在剖面。Logistic回归分析结果显示，年级、临床实践中尝试使用人工智能工具的意愿、医学人工智能准备度是本科护生人工智能焦虑的影响因素（均P<0.05）。

结论

本科护生人工智能焦虑处于中等偏高水平，且存在群体异质性。护理教育者需根据影响因素为本科护生制定精准干预方案，帮助其更好地适应智能时代的护理教育与职业发展需求。

本科护生  /  人工智能焦虑  /  潜在剖面分析  /  影响因素
Objective

To analyze the latent profiles of artificial intelligence anxiety among undergraduate nursing students,and to explore the influencing factors of artificial intelligence anxiety among different profiles of nursing students.

Methods

Using the convenience sampling method,from April to May 2025,a total of 720 undergraduate nursing students from three medical colleges in Hubei province were selected as the research subjects.The general information questionnaire,Artificial Intelligence Anxiety Scale(AIAS) and Medical Artificial Intelligence Readiness Scale(MAIRS) were used for the investigation.

Results

The score of artificial intelligence anxiety among undergraduate nursing students was 86.00±20.95.They could be divided into three latent profiles:low anxiety-rational alertness type (10.4%), medium anxiety-risk sensitivity type(65.3%),and high anxiety-generalized fear type(24.3%).Logistic regression analysis results showed that grade,the willingness to try using artificial intelligence tools in clinical practice,and medical artificial intelligence readiness were the influencing factors of artificial intelligence anxiety among undergraduate nursing students(all P<0.05).

Conclusion

The artificial intelligence anxiety among undergraduate nursing students is at a moderately high level.There is heterogeneity.Nursing educators need to develop precise intervention plans for undergraduate nursing students based on the influencing factors,so as to help them better adapt to the nursing education and career development requirements in the era of intelligence.

undergraduate nursing students  /  artificial intelligence anxiety  /  latent profile analysis  /  influencing factors
商丽, 胡佳一, 毕璐佳, 徐兰兰. 本科护生人工智能焦虑的潜在剖面分析. 护理研究, 2026 , 40 (16) : 2797 -2803 . DOI: 10.12102/j.issn.1009-6493.2026.16.005
Li SHANG, Jiayi HU, Lujia BI, Lanlan XU. Latent profile analysis of artificial intelligence anxiety among undergraduate nursing students[J]. Chinese Nursing Research, 2026 , 40 (16) : 2797 -2803 . DOI: 10.12102/j.issn.1009-6493.2026.16.005
正文
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人工智能（artificial intelligence,AI）的快速发展引发了临床护理及护理教育[1-2]模式的深刻变革。越来越多的教育者将人工智能技术应用于护理教育中，这不仅有助于激发学生学习的积极性[3]、提高学生的知识水平与实践能力[4]，还可以满足其自主性、胜任感与归属感，进而提升学生的满意度[5]。护理本科生作为护理队伍的重要储备人才，其对人工智能技术的认知与应用能力将直接关系其未来在临床环境中对智能技术的接受与应用，是推动智慧护理落地的关键。然而，由于缺乏人工智能相关知识[6]、面临技术挑战、可能衍生伦理风险[7]等原因，护生在享受人工智能技术红利的同时，可能并发不同程度的人工智能焦虑（artificial intelligence anxiety）。人工智能焦虑是指个体因无法完全掌握或精通人工智能技术，并担忧其对未来职业与生活带来的影响而产生的焦虑和压力[8]。有研究证实，过高水平的人工智能焦虑不仅可能降低护生的学习动力和心理健康水平，还可能影响其职业选择与发展[9]。目前，关于人工智能焦虑的调查主要集中于教师[10]、员工[11]及图书馆员[12]等群体，针对护生的相关研究较少，尤其缺乏对其内部差异及潜在结构的深入探讨。基于此，本研究通过潜在剖面分析（latent profile analysis,LPA）识别本科护生人工智能焦虑的潜在类别及特征，并探究其影响因素，旨在为护理教育者优化课程内容、开展精准化干预、培养适应人工智能时代的护理人才提供实证参考。
1　对象与方法
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1.1　对象
采用便利抽样法，于2025年4月—5月，选取湖北省3所医学院校的本科护生作为研究对象。纳入标准：全日制护理学专业本科生；自愿参与本研究。排除标准：专科、专升本护生；因各种原因休学或退学的学生；患严重精神或心理疾病的学生。研究建议潜在剖面分析样本量应大于500例[13]，同时考虑到20%的无效样本量，本研究所需最小样本量为625人，最终纳入720人。本研究已通过湖北医药学院伦理委员会审核批准（编号：2025-PR-116）。
1.2　调查工具
1.2.1　一般资料调查表
该问卷由研究团队在查阅文献的基础上自行编制，包括性别、年龄、年级、家庭所在地、是否为独生子女、是否担任学生干部、学习成绩、家庭月收入、使用人工智能工具的频率、接触人工智能的途径、每周愿意投入学习医学人工智能知识的时间、在临床实践中尝试使用人工智能工具的意愿等内容。
1.2.2　人工智能焦虑量表（Artificial Intelligence Anxiety Scale，AIAS）
AIAS由Wang等[14]编制，用于评估个体对人工智能技术或产品的焦虑程度。本研究团队于2025年对该量表进行汉化及跨文化调试。中文版AIAS包含学习（8个条目）、工作替代（6个条目）、社会技术盲点（4个条目）和人工智能配置（2个条目）4个维度，共20个条目。采用Likert 7级评分法，总分20~140分，得分越高表示焦虑程度越高，总量表Cronbach's α系数为0.951。基于其良好的心理测量学特性，本研究使用该量表评估本科护生的人工智能焦虑水平。
1.2.3　医学人工智能准备度量表（Medical Artificial Intelligence Readiness Scale，MAIRS）
MAIRS由Karaca等[15]开发，李稷璋等[16]汉化形成中文版MAIRS，用于评估医学生对人工智能在医学领域应用的准备程度。包括认知、能力、视野、伦理4个维度，共22个条目。量表采用Likert 5级评分法，非常不同意、不同意、不确定、同意、非常同意依次计1、2、3、4、5分，得分越高，表示医学生对人工智能应用的准备程度越高。量表的Cronbach's α系数为0.930。本研究的Cronbach's α系数为0.838。
1.3　资料收集与质量控制
采用线上问卷发放与回收形式收集资料，依托问卷星平台生成问卷二维码及链接。团队负责人联系各班级负责人，并对调查目的、对象及问卷填写方法进行解释，在征得同意后将二维码及链接发放至各班级，由符合纳入标准的学生进行填写。为确保数据质量，所有问卷题目设置为必答题，且每个IP地址只能填写1次，并由2名研究者对收集后的问卷进行核验,剔除填写时间<5 min或答题逻辑明显有误的无效问卷。此外，所有收集的数据均以编码形式处理，严格保密，以确保参与者的隐私安全。共回收问卷741份，剔除质量不合格问卷21份，回收有效问卷720份，有效回收率为97.2%。
1.4　统计学方法
采用SPSS 26.0软件进行数据分析。符合正态分布或近似正态分布的定量资料以均数±标准差（x±s）表示，定性资料用频数、百分比（%）表示。组间比较采用方差分析、χ2检验。检验水准α=0.05。使用Mplus 8.3软件对本科护生的人工智能焦虑水平进行潜在剖面分析，从假设1个类别开始，逐步增加类别个数，对照模型拟合指标，比较各模型间的拟合差异，选择最佳模型。赤池信息准则（AIC）、贝叶斯信息准则（BIC）、样本校正的贝叶斯信息准则（aBIC）统计值越小表示拟合效果越好；信息熵（Entropy）衡量分类精确度，取值为0~1，越接近1表明分类越精确；罗-梦戴尔-鲁本校正似然比检验（LMRT）和基于Bootstrap的似然比检验（BLRT）比较潜在类别模型的拟合差异，P<0.05表示k个类别模型优于k-1个类别模型[17]。上述评价指标仅为剖面决策提供参考，确定最佳模型时还需考虑各剖面类别的可解释性。
2　结果
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2.1　本科护生的一般资料
720名护生中，男156人，女564人；年龄17~25（20.40±1.51）岁；家庭所在地：城市257人，城镇200人，农村263人；是否为独生子女：独生子女223人，非独生子女497人；担任学生干部：245人担任，475人未担任；家庭月收入：<2 000元55人，2 000~5 000元291人，5 001~9 999元267人，≥10 000元107人；使用人工智能工具频率：307人几乎每天使用，287人每周使用几次，54人每个月使用几次，71人仅偶尔尝试，1人从未使用过。
2.2　本科护生人工智能焦虑得分（见表1）
2.3　本科护生人工智能焦虑的潜在剖面分析
依次选择1~5个潜在剖面模型对本科护生人工智能焦虑水平进行探索性分析，具体的模型拟合指数见表2。 结果显示，随着类别数目的增多，AIC、BIC及aBIC不断减小，模型3的Entropy值最大，LMRT、BLRT均达到显著水平，且模型3的各类别比例均达到5%以上，不存在类别过小影响稳定性的情况。此外，3个剖面也更符合护生人工智能焦虑的实际分布特点，能够清晰区分低、中、高焦虑群体，具有较好的可解释性。因此，选取模型3作为本科护生人工智能焦虑潜在剖面的最优模型。每个类别归属于该潜在剖面类别的平均概率分别为99.0%、99.1%及98.6%，表明本次潜在剖面分析的准确性较高。
根据分类结果绘制潜在剖面图，剖面1有75名护生（占10.4%），各条目得分均最低，仅在社会技术盲点维度略有上升，因此被定义为“低焦虑-理性警觉型”。剖面2有470名护生（占65.3%），各条目得分处于中等水平，但对于人工智能所致的依赖、变懒惰和被取代等长远后果尤为敏感，因此被命名为“中焦虑-风险敏感型”。剖面3有175名护生（占24.3%），其各条目得分均最高，各种原因均会导致其产生焦虑情绪，因此被命名为“高焦虑-泛化恐惧型”。具体见图1。
2.4　本科护生人工智能焦虑潜在剖面影响因素的单因素分析（见表3）
2.5　本科护生人工智能焦虑潜在剖面影响因素的多因素分析
以本科护生人工智能焦虑3个潜在剖面为因变量，将单因素分析中有统计学意义的变量作为自变量，进行Logistic回归分析，进入回归方程的变量有年级（大学四年级=3，大学三年级=2，大学二年级=1，大学一年级=0）、每周愿意投入学习医学人工智能知识的时间（<3 h=1，3~5 h=2，>5 h=3）、临床实践中尝试使用人工智能工具的意愿（愿意=1，不愿意=2）、学习成绩（优秀=1，良好=2，中等=3，较差=4）、医学人工智能准备度（原值输入），结果见表4。
3　讨论
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3.1　本科护生人工智能焦虑处于中等偏高水平
本研究结果显示，本科护生中文版AIAS总分为（86.00±20.95）分，处于中等偏高水平，这与Yigit等[18]的调查结果一致。同时，本研究中，中焦虑-风险敏感型与高焦虑-泛化恐惧型护生的占比分别达65.3%和24.3%，提示相当一部分本科护生在面对人工智能技术时存在焦虑情绪。分析原因可能是：在认知方面，本科护生普遍缺乏系统的人工智能相关教育。相关调查显示，95.3%的护生没有接受过关于人工智能的培训，同时94.0%的护生表示其学校课程中未包含与人工智能有关的内容[18]，护生多依赖零散渠道获取智能信息，致使其在实际接触或学习人工智能技术时产生能力不足感和焦虑体验。在学习负荷方面，护理专业课程内容繁多、实践训练密集，临床实习压力大。学生在既有学习负担下还需适应快速发展的智能技术，额外的学习任务可能导致精力透支，从而增加对学习人工智能的紧张与抵触感。在专业角色定位方面，部分护生对“智慧医院”“机器人辅助护理”等概念缺乏清晰理解，难以准确界定人工智能在护理工作中的实际功能边界[19]，这容易放大其对能力不足、判断力下降或被技术替代的担忧。在社会与职业环境层面，本科护生面临毕业、就业竞争等压力，而社会媒体频繁讨论人工智能对就业结构的冲击，使学生对未来职业发展与专业价值的预期更不确定，从而进一步激发了焦虑情绪。提示护理院校应系统开展人工智能素养教育，将人工智能基础知识、伦理与风险评估、智能工具使用技能等内容纳入必修课程体系；要在教学中适当减轻不必要的学习负担，提供循序渐进的人工智能实践训练；还要适时给予职业规划支持，帮助护生明晰人工智能与护士的协同关系，以提高其面对新技术时的自信心与适应能力。
3.2　本科护生人工智能焦虑存在群体异质性
本研究通过潜在剖面分析将本科护生的人工智能焦虑分为3个类别。1）低焦虑-理性警觉型群体占10.4%，该类型条目得分整体较低，仅在“人工智能技术可能被滥用、失控或出现故障”的条目上略有升高，说明此类护生通常具备较好的信息化学习基础和技术自我效能，能够主动接受并尝试人工智能技术，将其视为学习和工作的工具，同时其对潜在风险保持一定警觉，尚未演变为过度担忧。提示护理教育者在教学中可适当赋予其更多参与智能化教学设计和朋辈指导的机会，发挥其示范带动作用，还可通过案例教学引导其在理性警觉的基础上形成更加系统的技术伦理观。2）中焦虑-风险敏感型群体占65.3%。该类别中学习维度得分中等，但在“依赖人工智能可能导致变懒、能力退化”“人工智能或机器人可能取代部分工作”“担心与人工智能相关的各种潜在问题”等条目上表现出明显高分，呈现出对职业前景与技术风险高度敏感的特征。说明此类护生一方面已在学习和生活中一定程度接触人工智能，能够感受到其便利；另一方面，因对岗位结构变化、技术滥用和责任划分等问题缺乏清晰认知，容易放大对被替代和被控制的担忧。提示护理教育者应在专业课程及职业规划教育中，重点澄清“人工智能与护士是协同而非简单替代”的关系，通过介绍真实工作场景、典型案例和发展路径，减轻其对未来就业和专业价值的焦虑。3）高焦虑-泛化恐惧型群体为焦虑水平最高的一类，占24.3%。该人群不仅在学习和使用人工智能的相关条目上感到明显紧张，对“人工智能可能失控、出现故障”“机器人拥有自主权”等内容同样表现出强烈恐惧，呈现从具体学习情境扩展至社会层面的泛化焦虑。此类护生往往存在人工智能知识基础薄弱、技术自我效能偏低的情况，同时叠加较高的一般性焦虑与就业压力，在负性舆论影响下更易产生灾难化的联想。因此，教育者应将该类护生作为重点干预人群，将人工智能素养训练由浅入深地融入课程，通过小组教学、示范操作和结构化练习，帮助其逐步建立对技术使用的信心；也可通过心理健康教育对焦虑显著的学生提供个性化支持，协助其降低技术威胁感。
3.3　本科护生人工智能焦虑潜在剖面的影响因素
3.3.1　年级
本研究结果发现，相较于大学一年级护生，大学三年级、四年级护生更容易对人工智能技术产生较高水平焦虑，说明高年级护生是人工智能焦虑干预的重点人群。这一结果可能与不同年级的课程学习及临床接触经历有关，大学一年级护生在基础课程学习阶段，初步接触人工智能应用，更倾向于将人工智能视为提升工作效率的机遇，而非职业挑战[20]。而大学三年级护生本身专业课程学习负担较重，医学人工智能涉及算法原理、数据处理、临床场景整合等多维内容，知识结构复杂、专业性较强[21]。高强度的学习任务容易导致其认知负荷过高，进而产生认知疲劳和情绪倦怠。研究发现，处于疲劳状态的个体常对潜在风险产生更为消极的情绪反应[22]。因此，当护生在认知疲倦的状态下，更容易放大对人工智能相关风险的负面情绪，从而加剧其焦虑。与此同时，大学四年级护生临床实习压力较大，且参与了临床情境体验，更易产生人工智能替代护理岗位的担忧[23]。此外，临床护士在实际工作中需要对人工智能输出结果承担审核与最终判断责任[24]，这一职业要求也会投射至实习护生，使其对未来职业胜任力产生额外的压力与焦虑。因此，建议护理院校需对人工智能学习内容进行合理的学期分配，避免在既有的高强度课业外再增加护生的学习负担，可参考Henning等[25]提出的干预方案，当护生进行30 min人工智能学习后，强制休息5 min，并远离学习设备，以缓解因长时间学习引发的认知疲劳。还可针对实习护生开展沙龙分享活动，邀请临床护士讲解在实际工作中对人工智能结果复核及终审的岗位职责，引导护生拆解岗位压力，把未知恐惧转化为专业提升目标。
3.3.2　医学人工智能准备度
医学人工智能准备度是指医疗保健提供者在知识、技能和态度上利用医疗保健人工智能的准备状态，需在提供预防、诊断、治疗和康复服务时结合自己的专业知识。本研究结果显示，医学人工智能准备度对护生人工智能焦虑水平的影响存在阶段性差异：低焦虑-理性警觉型与中焦虑-风险敏感型、中焦虑-风险敏感型与高风险-泛化恐惧型之间，较高的医学人工智能准备度可降低护生焦虑；但在低焦虑组与高焦虑组之间，准备度较高的护生反而更易进入高焦虑状态。这种“先保护后风险”的阶段效应符合Frenkenberg提出的人工智能焦虑U型曲线理论，即随着知识积累和理解的加深，个体的技术焦虑会有所缓解，但当其接触更复杂的应用和伦理情境时，焦虑水平可能再次上升[26]。分析原因可能是，准备度较高的护生较早接触了人工智能工具并具备较好的基础知识，能够较快适应人工智能技术的基本应用，从而在初期有效缓解焦虑情绪。然而，随着学习和实践的深入，他们开始面对更复杂的使用场景[27]，并逐渐意识到技术在数据安全、算法偏见及技术伦理等方面的潜在风险[28]，进而诱发了“预期性焦虑”[29]。护理教育者应将医学人工智能准备度视为随学习阶段动态变化的指标，实施差异化、阶段性干预。对于准备度较低的护生，应优先提升其基本操作能力与使用信心，减轻陌生感带来的焦虑；而对于准备度较高、已进行深入学习和训练阶段的护生，则应在课程中强化技术伦理、数据质量判断与高阶思维训练等内容[30]，引导其将对潜在风险的敏感性转化为成熟的专业判断能力，避免因风险认知累积而进入高度焦虑状态。
3.3.3　临床实践中尝试使用人工智能工具的意愿
本研究结果发现，护生在临床实践中尝试使用人工智能工具的意愿水平越高，其整体人工智能焦虑水平越低，这与Cho等[31]研究结果一致。分析原因可能是，主观上更愿意在未来工作中运用智能技术的护生，本身对人工智能技术持有更开放、积极的接纳态度。这类护生更愿意主动了解人工智能在临床护理中的应用价值和功能优势，不易因技术陌生、信息未知而产生畏惧或抵触情绪。同时，高使用意愿的个体在遇到技术难题时，常会主动请教带教老师，与实习同伴交流解决问题，及时化解疑问有助于维持其平稳的心理状态。因此，护理院校可在智慧教学中以未来临床护理应用场景为导向开展仿真模拟教学，向护生展示人工智能技术在临床风险筛查、护理文书优化、病情辅助评估等领域的优势，引导护生建立科学、正向的智能技术认知；同时，护理教育者可采用小组合作的学习形式开展教学，引导护生在同伴互助中减少面对陌生技术的孤独感；还可以在教学中增设交流与反思环节，让护生及时表达担忧、厘清期待，有效缓解智慧技术学习过程中的负面情绪。
4　小结
收起
本研究通过潜在剖面分析，识别出本科护生人工智能焦虑存在低焦虑-理性警觉型、中焦虑-风险敏感型与高焦虑-泛化恐惧型3种潜在剖面。进一步分析发现，年级、医学人工智能准备度及在临床实践中尝试使用人工智能工具的意愿是影响其焦虑水平的关键因素。但本研究仍存在一些不足，首先，抽样方法采用便利抽样，可能存在选择偏倚，限制了样本代表性及结果的普适性。未来研究可开展多中心、大样本的横断面调查，以提高结果的外推性。其次，仅通过护生自评的方式测量人工智能焦虑水平，后续研究可尝试纳入多源评估方式，如护理教育者评价、同行互评及客观行为考核等，以更全面、客观地评估焦虑水平。此外，今后还可结合质性访谈方法，深入探讨不同焦虑类型的成因与体验，以验证潜在剖面的分析结果，从而为制定针对性干预措施提供依据。

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2026年第40卷第16期
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doi: 10.12102/j.issn.1009-6493.2026.16.005
  • 接收时间：2025-10-12
  • 首发时间：2026-10-30
  • 出版时间：2026-08-25
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  • 收稿日期：2025-10-12
  • 修回日期：2026-07-28
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    1湖北医药学院护理学院，湖北 442000

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