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Article(id=1322168634282193268, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322168387082502716, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.06.024, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745337600000, receivedDateStr=2025-04-23, revisedDate=1770566400000, revisedDateStr=2026-02-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159167118, onlineDateStr=2026-10-28, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159167118, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159167118, creator=13701087609, updateTime=1793159167118, updator=13701087609, issue=Issue{id=1322168387082502716, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='6', pageStart='881', pageEnd='1056', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159108180, creator='13701087609', updateTime=1793159108180, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1043, endPage=1047, ext={EN=ArticleExt(id=1322168635964109173, articleId=1322168634282193268, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Research progress on attitudes toward artificial intelligence and related assessment tools in the nursing field, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=

This article reviewed the research progress on attitudes toward artificial intelligence and related assessment tools in the nursing field.It aimed to provide reference for studying the attitudes of nurses and patients toward artificial intelligence and for developing artificial intelligence attitude assessment tools suitable for China's cultural context.

, authors=Yifan MA1, 2, Longyu CAO2, Xiufen SHAO2, Wenyao LIU2, Wenxia SUN1, authorsList=Yifan MA, Longyu CAO, Xiufen SHAO, Wenyao LIU, Wenxia SUN, authorCompany=null, correspAuthors=Wenxia SUN, authorNote=null, correspAuthorsNote=
SUN Wenxia, E-mail:
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对护理领域人工智能态度的研究进展及相关评估工具进行综述，为研究护士和病人的人工智能态度及开发适合我国文化背景的人工智能态度评估工具提供参考。

, authors=马一帆1, 2, 曹龙钰2, 邵秀芬2, 刘雯垚2, 孙文霞1, *, *, authorsList=马一帆, 曹龙钰, 邵秀芬, 刘雯垚, 孙文霞, authorCompany=null, correspAuthors=孙文霞, authorNote=

马一帆，护师，硕士研究生在读

, correspAuthorsNote=
孙文霞，E-mail:
SUN Wenxia, E-mail:
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马一帆，护师，硕士研究生在读

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马一帆，护师，硕士研究生在读

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护理领域人工智能态度及相关评估工具的研究进展
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马一帆 1, 2 , 曹龙钰 2 , 邵秀芬 2 , 刘雯垚 2 , 孙文霞 1, *, *
护理研究 | 科研综述 2026,40(6): 1043-1047
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护理研究 |科研综述 2026 , 40 (6) : 1043 -1047
护理领域人工智能态度及相关评估工具的研究进展
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马一帆1, 2, 曹龙钰2, 邵秀芬2, 刘雯垚2, 孙文霞1, *, *
作者信息
  • 1延边大学附属医院（延边医院），吉林 133000
  • 2延边大学护理学院
通讯作者:
孙文霞，E-mail:
SUN Wenxia, E-mail:
作者简介:

马一帆，护师，硕士研究生在读

Research progress on attitudes toward artificial intelligence and related assessment tools in the nursing field
Yifan MA1, 2, Longyu CAO2, Xiufen SHAO2, Wenyao LIU2, Wenxia SUN1
Affiliations
  • 1Yanbian University Hospital/Yanbian Hospital, Jilin 133000 China
  • 2School of Nursing, Yanbian University
    • Corresponding Author:
    SUN Wenxia, E-mail:
出版时间: 2026-03-25 doi: 10.12102/j.issn.1009-6493.2026.06.024
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摘要
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对护理领域人工智能态度的研究进展及相关评估工具进行综述，为研究护士和病人的人工智能态度及开发适合我国文化背景的人工智能态度评估工具提供参考。

人工智能  /  量表  /  护理  /  态度  /  评价工具  /  综述

This article reviewed the research progress on attitudes toward artificial intelligence and related assessment tools in the nursing field.It aimed to provide reference for studying the attitudes of nurses and patients toward artificial intelligence and for developing artificial intelligence attitude assessment tools suitable for China's cultural context.

artificial intelligence  /  scale  /  nursing  /  attitude  /  assessment tool  /  review
马一帆, 曹龙钰, 邵秀芬, 刘雯垚, 孙文霞. 护理领域人工智能态度及相关评估工具的研究进展. 护理研究, 2026 , 40 (6) : 1043 -1047 . DOI: 10.12102/j.issn.1009-6493.2026.06.024
Yifan MA, Longyu CAO, Xiufen SHAO, Wenyao LIU, Wenxia SUN. Research progress on attitudes toward artificial intelligence and related assessment tools in the nursing field[J]. Chinese Nursing Research, 2026 , 40 (6) : 1043 -1047 . DOI: 10.12102/j.issn.1009-6493.2026.06.024
正文
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人工智能（artificial intelligence，AI）是一门研究如何使计算机模拟人类智能行为的科学与技术，旨在通过计算机程序和算法让机器具备类似人类的感知、学习、推理、决策和创造等能力，从而完成各种复杂的任务[1]。DeepSeek国产大模型掀起的智能应用拓展浪潮，使人们看到我国AI发展的光明前景，也让我国人民率先体验到智能社会转型变革的复杂性[2]。近年来，随着AI快速发展，护理机器人、疾病诊疗、健康管理、药物研发、精准医学、跨地域医疗整合系统等方面均取得显著成果，对护理模式、护理质量提升具有重要影响[3-6]。然而，AI在临床护理中涉及大量数据处理，存在数据偏见、隐私泄露等伦理问题[7-8]。不同人群的AI态度存在一定差异，但也有许多共同点[9]。大多数群体对AI在医学中的应用持较高期望，认为AI在医疗诊断中具有提高准确性的潜力[10]。已有研究表明，公众对医疗领域AI系统的信任度较低，病人普遍更信任人类医生[11-12]。在此背景下，AI态度及相关评估工具的研究显得尤为重要。评估护士和病人的AI态度对于AI在临床护理中的有效应用、伦理困境的解决、技术的持续改进及社会信任的增强均具有重要意义。国外关于医学领域AI态度的研究较早，评估工具相对完善。现对国外护理领域AI态度的研究及评估工具进行综述，旨在为我国护理领域AI态度的研究和相关工具的开发提供参考。
1　AI态度概述
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AI态度定义尚未统一。2016年，Fast等[13]探讨了公众对AI的长期趋势感知，揭示了公众对AI的复杂态度，既有对其潜力的乐观期待，也有对其潜在风险的担忧。2020年，Schepman等[14]研究后提出，AI态度包含积极和消极两方面，涉及情感、伦理、社会和个人效用。2021年，Young等[15]使用混合方法系统综述了病人和公众对临床中AI的态度，将AI态度定义为一个多维度的概念，其涵盖接受度、信任、满意度、使用意图等多个方面。2024年，Stein等[16]提出，AI态度是一个涵盖认知、情感和行为意向的综合性心理构念，这种态度不仅受到个体认知和情感的影响，还可能通过行为意向表现出来。综合来看，可以将AI态度概括为个体或群体对AI技术、应用及其影响所持有的情感、认知和行为倾向，这种态度可以是积极的，也可以是消极的，通常受到个人经验、知识水平、社会文化背景、伦理观念等多种因素影响。通过开发有效的评估工具，研究者能更准确地评估和理解个体的AI态度。
2　护理领域AI态度研究进展
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2.1　医学生AI态度
Pinto Dos Santos等[9]使用SurveyMonkey设计了一个网络问卷，旨在评估医学生对AI在放射学中的了解程度、对AI在放射学和医学中应用的态度以及他们是否担心AI会取代人类医生。调查结果显示，医学生普遍认为AI将在放射学和医学中发挥重要作用，但他们并不担心AI会取代人类，反而认为AI将改善这些领域，大多数学生认为AI应该纳入医学培训中。Stewart等[17]调查了西澳大利亚州医学生对AI在医疗保健中应用的态度，结果显示大多数医学生对AI感兴趣，认为AI将在医学中发挥重要作用，但87.5%的医学生未接受过AI教育，58.6%的学生认为AI教育应纳入医学培训，72.7%的医学生希望获得更多关于医学中AI的教学。Mousavi Baigi等[18]系统回顾了医疗保健专业学生对AI的态度、知识和技能，结果显示，学生对AI的态度在不同收入水平的国家之间存在差异，高收入水平国家的学生AI态度较消极，而中低收入水平国家的学生AI态度较积极；不同专业学生对AI的态度也有所不同，如放射学领域的学生更担心AI对其职业的影响；学生对AI的态度受其知识水平影响，对AI知识了解更多的学生往往对AI态度更积极。
2.2　医护人员AI态度
Buck等[19]探讨了全科医生对AI辅助系统在医疗诊断中应用的态度，结果显示，全科医生认为AI辅助系统是一种有潜力提高医疗质量的解决方案，研究揭示了5类全科医生对AI辅助系统态度的决定因素，即担忧、期望、环境影响、个人特征、AI辅助系统的最低要求，其中，担忧因素包括工作替代性、医患关系变化、数据滥用、诊断偏见；期望因素包括诊断质量、诊断效率、法律责任；环境影响因素包括工作环境变化、利益相关者影响、媒体影响、信息技术基础设施；个人特征因素包括年龄、技术亲和力；AI辅助系统的最低要求因素包括时间效率、诊断质量、数据安全、经济可行性、透明度、自主性。Hamedani等[20]对伊朗医生和护士进行调查，结果发现，参与调查的医生和护士对AI有一定了解，但缺乏深入的技术细节和应用知识；参与者对AI的态度积极，70.3%的参与者支持AI教育，84.7%的参与者认为AI将为医学带来积极变化，但仍有38.8%的参与者担心AI可能带来错误和不确定性；参与者对AI的接受度处于中等水平，表明他们对AI的应用既不完全拒绝，也不完全接受，主要担心问题包括伦理问题、技术问题和资源问题。Morales-García等[21]对243名秘鲁护士进行测评，结果显示，护士对AI的整体态度较为积极，尤其是在对AI未来的应用方面，但护士也表现出一定的谨慎态度，这可能与其对AI技术的了解不足或技术实施的不确定性有关。
2.3　病人AI态度
Robertson等[11]通过混合研究方法（包括定性研究和定量研究）探讨了多样化病人对诊断中使用AI的态度，并分析了不同因素对病人接受AI诊断的影响，结果显示，病人对AI诊断的接受程度较低，仅47.1%的病人愿意选择AI诊断，52.9%的病人更倾向于选择人类医生。影响病人选择AI的因素包括AI的准确性、AI的个性化和医生的角色。此外，种族、背景、年龄、教育水平和政治立场等也是影响病人AI态度的重要因素。
3　AI态度评估工具
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3.1　机器人负面态度量表（Negative Attitudes toward Robots Scale，NARS）
该量表由Nomura等[22]开发，用于测量人们对于机器人的负面态度。量表由3个维度（负面态度对机器人的交互情景、负面态度对机器人的社会影响、负面态度对机器人交互中的情感）、14个条目组成，采用Likert 5级评分法，从“非常不同意”到“非常同意”依次计1~5分，得分越高说明个体对机器人的态度越消极。量表各维度的Cronbach's α系数为0.648~0.782；2个维度的重测信度>0.7，第3个维度的重测信度为0.538，略低于标准[22]。该量表经过专家讨论和预调查条目筛选，内容覆盖了负面态度的各个方面，并通过因子分析验证了量表的三因子结构，其与一般焦虑量表（STAI）的相关性有统计学意义。毕轩懿等[23]对NARS进行汉化，汉化后的量表包括3个维度、13个条目，采用Likert 5级评分法，其总体的Cronbach's α系数为0.834，各维度的Cronbach's α系数为0.811~0.919，量表水平的内容效度指数为0.923。
3.2　AI态度量表4个条目版（AI Attitude Scale，AIAS-4）
该量表由Grassini[24]开发，用于评估公众对AI的总体态度。AIAS-4为单因子结构，包含4个条目，采用Likert 10级评分法，从“完全不同意”到“完全同意”依次计1~10分。量表Cronbach's α系数为0.902，比较拟合优度指数（CFI）为0.999，Tucker-Lewis指数（TLI）为0.998，近似误差均方根（RMSEA）为0.028 5，验证性因子分析显示单因子模型拟合良好[24]。Morales-García等[21]在秘鲁243名护士中对AIAS-4进行测量，结果表明，AIAS-4在秘鲁护士中具有良好的心理测量学特征，量表Cronbach's α系数为0.94，CFI为1.00，TLI为1.00，RMSEA为0.00，标准化残差均方根（SRMR）为0.00；且研究指出，尽管AI在医疗领域具有巨大潜力，但护士群体对AI的了解和实践经验仍有待提高。Köse等[25]对AIAS-4进行了土耳其版本改编，并对其信效度进行检验，结果显示，AIAS-4在土耳其语环境中保持了原量表的单因子结构，其总体的Cronbach's α系数为0.862，重测信度为0.71，量表水平的内容效度指数为0.95，CFI为0.998，TLI为0.993，RMSEA为0.048，验证性因子分析显示其结构效度良好。
3.3　AI焦虑量表（Artificial Intelligence Anxiety Scale，AIAS）
该量表由Wang等[26]开发，用于评估个体对AI的焦虑水平。量表包括4个维度（学习焦虑、工作替代焦虑、社技盲目焦虑、AI配置焦虑）、21个条目，采用Likert 7级评分法，从“非常不同意”到“非常同意”依次计1~7分，得分越高说明AI焦虑水平越高。该量表的Cronbach's α系数为0.964，各维度的Cronbach's α系数为0.917~0.974，量表的条目基于对AI焦虑相关文献的综合分析形成，通过专家评审和项目分析删除冗余条目，保留21个条目，确保了内容的全面性和代表性[26]。Article等[27]对AIAS进行土耳其语版本的改编，并对其信效度进行检验，结果显示，土耳其语版AIAS保持了原量表的四因子结构，共21个条目，量表Cronbach's α系数为0.96，各维度的Cronbach's α系数均>0.8。
3.4　AI态度量表（Attitude Towards Artificial Intelligence Scale，ATAI）
该量表由Sindermann等[28]开发，用于评估德语、汉语和英语使用者对AI的态度，可分为2个维度、5个条目，采用Likert 11级评分法，从“非常不满意”到“非常满意”依次计0~10分。该量表条目基于对AI相关媒体辩论的分析，覆盖了对AI的积极和消极态度，确保了内容的全面性和代表性。其在中国、德国、英国的测试中均显示包含接受度和恐惧2个因子，其中，条目1、条目3、条目5与恐惧因子相关，条目2、条目4与接受度因子相关，在中国样本中，量表接受度因子的Cronbach's α系数为0.73，恐惧因子的Cronbach's α系数为0.61，CFI为0.870，TLI为0.676，RMSEA为0.202，SRMR为0.079；德国样本中，量表接受度因子的Cronbach's α系数为0.65，恐惧因子的Cronbach's α系数为0.66，CFI为0.976，TLI为0.940，RMSEA为0.075，SRMR为0.032；在英国样本中，接受度因子的Cronbach's α系数为0.64，恐惧因子的Cronbach's α系数为0.65，CFI为0.933，TLI为0.833，RMSEA为0.127，SRMR为0.059[28]。
3.5　AI一般态度量表（General Attitudes towards Artificial Intelligence Scale，GAAIS）
该量表由Schepman等[14,29]开发，用于测量人们对AI的总体态度，包括积极态度和消极态度2个维度，其中积极态度维度包含12个条目，消极态度维度包含8个条目，量表采用Likert 5级评分法，积极态度维度条目从“非常不同意”到“非常同意”依次计1~5分，消极态度维度条目反向计分。积极态度维度的Cronbach's α系数为0.88，消极态度维度的Cronbach's α系数为0.83，验证性因子分析显示拟合指标良好[14]。GAAIS能够有效测量人们对AI的积极和消极态度，适用于研究AI态度与人格特质、信任等因素之间的关系。Seo等[30]对GAAIS进行翻译和文化调适后形成韩语版GAAIS，用于评估韩国护理学生的AI态度，其积极态度维度的Cronbach's α系数为0.86，消极态度维度的Cronbach's α系数为0.74，量表水平的内容效度指数≥0.8，CFI为0.92,TLI为0.9,RMSEA为0.06,SRMR为0.07。
3.6　AI态度量表12个条目版（Attitudes towards Artificial Intelligence Scale，ATTARI-12）
该量表由Stein等[16]开发，为单维度量表，涵盖认知、情感和行为3个方面，共12个条目，条目分为正向和负向表述，以确保能够测量从厌恶到热情的完整态度谱系，所有条目共同反映一个总体的态度构念，采用Likert 5级评分法，从“非常不同意”到“非常同意”依次计1~5分，负向条目在评分时进行反向计分，以确保高分表示更积极的态度。量表Cronbach's α系数为0.92，验证性因子分析显示具有良好的单维度结构。ATTARI-12能够可靠地测量人们对AI的总体态度，适用于跨学科研究和不同文化背景下的态度测量。
3.7　生成性AI接受度量表（Generative Artificial Intelligence Acceptance Scale，GAIAS）
该量表由Yilmaz等[31]开发，用于测量学生对生成性AI应用的接受度。该量表包括4个维度（绩效期望、努力期望、促进条件、社会影响）、20个条目，采用Likert 5级评分法，从“非常不同意”到“非常同意”依次计1~5分，总分20~100分，得分越高表示对生成性AI的接受度越高。量表Cronbach's α系数为0.97，各维度Cronbach's α系数为0.87~0.96，量表重测信度为0.95，验证性因子分析显示CFI为0.97，TLI为0.97，RMSEA为0.067，SRMR为0.033 2[31]。
3.8　医学生医学AI准备度量表（Medical Artificial Intelligence Readiness Scale for Medical Students，MAIRS-MS）
该量表由Karaca等[32]开发，包括4个维度（认知、能力、愿景、伦理）、22个条目，采用Likert 5级评分法，从“非常不同意”到“非常同意”依次计1~5分，得分越高说明其AI准备度越高。量表总体的Cronbach's α系数为0.877，各维度的Cronbach's α系数为0.632~0.830，验证性因子分析显示，CFI为0.938，TLI为0.928，RMSEA为0.094，SRMR为0.057。MAIRS-MS具有良好的信度和效度，可用于评估医学生对AI技术及其在医学中应用的准备程度，可以作为医学教育中AI相关课程开发和评估的重要工具。
3.9　AI威胁量表（Threats of Artificial Intelligence Scale，TAI）
该量表由Kieslich等[33]开发，旨在评估人们对AI系统的威胁感知。量表通过标准化问卷的形式，针对不同应用领域评估人们对AI功能的威胁感知。量表分为3个子量表，分别对应AI在贷款发放、工作招聘、医疗治疗领域的应用，每个子量表4个维度（识别、推荐、决策、预测）、12个条目，采用Likert 5级评分法，从“非常不同意”到“非常同意”依次计1~5分，得分越高表示感知到的威胁水平越高。量表的Cronbach's α系数在不同领域（贷款发放、工作招聘、医疗治疗）中均表现良好，为0.80~0.92；验证性因子分析和结构方程模型验证显示，量表在不同应用领域（贷款发放、工作招聘、医疗治疗）的结构效度良好[33]。Cai等[34]将TAI翻译为中文，并在我国样本中进行信效度验证，中文版TAI的3个子量表Cronbach's α系数为0.95~0.96；通过验证性因子分析显示，贷款发放的CFI为0.93，TLI为0.92，RMSEA为0.120，SRMR为0.03；工作招聘的CFI为0.93，TLI为0.92，RMSEA为0.121，SRMR为0.04；医疗治疗的CFI为0.94，TLI为0.93，RMSEA为0.116，SRMR为0.03。中文版TAI信度和效度较好，具有良好的心理测量学特性，可以有效评估个体对AI威胁的感知。
3.10　护士AI态度量表（Artificial Intelligence Attitude Scale for Nurses）
该量表由Yıldırım等[35]于2024年开发，用于评估护士对AI技术的态度。量表包括护理关怀（nursing care）、组织（organization）伦理（ethics）、AI准备度（artificial intelligence readiness）4个维度，共32个条目,采用Likert 5级评分法，从“非常不同意”到“非常同意”依次计1~5分。条目水平的内容效度指数为0.85~1.00，量表水平的内容效度指数为0.94。探索性因子分析提取4个公因子，解释了72.185%的总方差[35]。模型拟合指标显示拟合良好，拟合优度指数（GFI）为0.936，调整适配度指标值（AGFI）为0.957，模拟拟合指数（NFI）为0.950，递增拟合指数（IFI）为0.965，TLI为0.962，CFI为0.958，RMSEA为0.064[35]。量表的Cronbach's α系数为0.925，各维度的Cronbach's α系数为0.913~0.960[35]。该量表具有良好的结构效度和收敛区分效度，但因尚未检索到类似量表，无法与其他量表进行对比，可能影响敏感性。该量表最初为土耳其语量表，尽管纳入的研究样本量较大，但因均来自土耳其，可能限制其结果的普适性。
4　现存问题及护理启示
收起
4.1　现存问题
1）护理领域的AI态度定义尚未统一，概念不统一易导致研究目标和研究结果差异，难以进行有效的比较和整合，不利于相关研究的发展。2）现有评估工具的针对性仍有待加强，虽然已有专门针对护士群体的护士AI态度量表，但大多数研究仍倾向于使用普适性AI态度量表或医学通用型量表。3）研究多由国外学者开展，相关评估工具在跨文化应用时存在一定局限性。4）研究对象主要集中于护理学生、医护人员和病人，对其他相关群体（如病人家属、护理管理者等）的研究较少。对不同护理场景（如社区护理、重症护理、老年护理等）中AI态度的研究存在不足。5）研究主要集中在AI态度评估工具的开发和态度现状调查方面，对于如何改善护理人员和病人对AI的消极态度、提高其接受度和信任度研究较少。6）AI态度的研究应涉及心理学、社会学、伦理学、护理学等多个学科，但已有研究多集中在单一学科领域，缺乏跨学科的综合研究，可能导致研究结果存在片面性，无法多维度、多层次地理解护理领域中AI态度的影响因素和作用机制，进而可能影响研究的深度和广度。
4.2　护理启示
1）护理学生和医护人员对AI的了解程度直接影响其态度[18]。因此，护理教育中应加强AI相关课程的设置，帮助护理人员和学生更好地理解和掌握AI技术及其在护理中的应用。2）AI在护理中的应用涉及大量数据处理，存在数据偏见、隐私泄露等伦理问题[7-8]，这些伦理问题可能影响护理人员和病人对AI的态度。因此，护理领域应重视AI的伦理和隐私保护，增强其接受度和信任度。3）不同群体对AI的态度存在差异，需要根据具体态度制定针对性的干预措施，以提高人们对AI的接受度。4）病人和护理人员是AI技术在护理领域应用的直接参与者，其反馈对于技术的改进和推广至关重要，应建立反馈机制，积极收集病人和护理人员的反馈意见以进行技术优化和调整。
5　小结
收起
对AI态度在护理领域的研究进展及相关评估工具进行综述，有利于护理研究者了解护士和病人的AI态度，并为开发与本土文化相适应的评估工具提供参考。我国对护理领域AI态度的研究尚处于起步阶段，AI态度的定义尚未统一，未来可以对护理领域AI态度进行概念分析，可进行多种学科交互应用研究，积极构建针对护理领域的特异性量表。

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2026年第40卷第6期
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doi: 10.12102/j.issn.1009-6493.2026.06.024
  • 接收时间：2025-04-23
  • 首发时间：2026-10-28
  • 出版时间：2026-03-25
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  • 收稿日期：2025-04-23
  • 修回日期：2026-02-09
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    1延边大学附属医院（延边医院），吉林 133000
    2延边大学护理学院

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孙文霞，E-mail:
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