Article(id=1322929472982364515, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929431592972373, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.15.026, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1765296000000, receivedDateStr=2025-12-10, revisedDate=1783180800000, revisedDateStr=2026-07-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340565199, onlineDateStr=2026-10-30, pubDate=1786291200000, pubDateStr=2026-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340565199, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340565199, creator=13701087609, updateTime=1793340565199, updator=13701087609, issue=Issue{id=1322929431592972373, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='15', pageStart='2577', pageEnd='2768', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793340555332, creator='13701087609', updateTime=1793343107287, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940135335145743, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929431592972373, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940135335145744, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929431592972373, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2758, endPage=2763, ext={EN=ArticleExt(id=1322929473192079716, articleId=1322929472982364515, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Research progress on digital empathy of medical staff in telemedicine scenarios, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=

This study reviewed the concept connotation,evaluation tools,research status quo,role in patients and promotion strategies of digital empathy of medical staff in telemedicine scene,aiming to provide theoretical reference for making up for the lack of empathy of medical staff in telemedicine scene,optimizing the doctor-patient relationship,improving the quality of telemedicine service and patients' satisfaction.

, authors=Wancheng LIAO1, Xiaohong CHEN2, Liandan LIN1, Mengying WANG1, Ruoxuan AN1, Ruixia XIA1, authorsList=Wancheng LIAO, Xiaohong CHEN, Liandan LIN, Mengying WANG, Ruoxuan AN, Ruixia XIA, authorCompany=null, correspAuthors=Xiaohong CHEN, 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=1322929473510846821, articleId=1322929472982364515, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=远程医疗场景下医务人员数字共情的研究进展, columnId=1220689485482873504, journalTitle=护理研究, columnName=科研综述, runingTitle=null, highlight=null, articleAbstract=

对远程医疗场景下医务人员数字共情的概念内涵、评估工具、研究现状、对病人的作用及提升策略进行综述,旨在为弥补远程场景下医务人员的共情缺失、优化医患关系、提升远程医疗服务质量及病人满意度提供理论参考。

, authors=廖婉成1, 陈晓红2, *, *, 林连丹1, 王梦莹1, 安若轩1, 夏瑞霞1, authorsList=廖婉成, 陈晓红, 林连丹, 王梦莹, 安若轩, 夏瑞霞, authorCompany=null, correspAuthors=陈晓红, authorNote=

廖婉成,硕士研究生在读

, correspAuthorsNote=
陈晓红,E-mail:
CHEN Xiaohong, E-mail:
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廖婉成,硕士研究生在读

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远程医疗场景下医务人员数字共情的研究进展
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廖婉成 1 , 陈晓红 2, *, * , 林连丹 1 , 王梦莹 1 , 安若轩 1 , 夏瑞霞 1
护理研究 | 科研综述 2026,40(15): 2758-2763
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护理研究 |科研综述 2026 , 40 (15) : 2758 -2763
远程医疗场景下医务人员数字共情的研究进展
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廖婉成1, 陈晓红2, *, * , 林连丹1, 王梦莹1, 安若轩1, 夏瑞霞1
作者信息
  • 1丽水学院医学院,浙江 323000
  • 2丽水学院附属第一医院
通讯作者:
陈晓红,E-mail:
CHEN Xiaohong, E-mail:
作者简介:

廖婉成,硕士研究生在读

Research progress on digital empathy of medical staff in telemedicine scenarios
Wancheng LIAO1, Xiaohong CHEN2 , Liandan LIN1, Mengying WANG1, Ruoxuan AN1, Ruixia XIA1
Affiliations
  • 1Lishui University School of Medicine, Zhejiang 323000 China
  • 2The First Affiliated Hospital of Lishui University
出版时间: 2026-08-10 doi: 10.12102/j.issn.1009-6493.2026.15.026
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对远程医疗场景下医务人员数字共情的概念内涵、评估工具、研究现状、对病人的作用及提升策略进行综述,旨在为弥补远程场景下医务人员的共情缺失、优化医患关系、提升远程医疗服务质量及病人满意度提供理论参考。

远程医疗  /  医务人员  /  数字共情  /  综述

This study reviewed the concept connotation,evaluation tools,research status quo,role in patients and promotion strategies of digital empathy of medical staff in telemedicine scene,aiming to provide theoretical reference for making up for the lack of empathy of medical staff in telemedicine scene,optimizing the doctor-patient relationship,improving the quality of telemedicine service and patients' satisfaction.

telemedicine  /  medical staff  /  digital empathy  /  review
廖婉成, 陈晓红, 林连丹, 王梦莹, 安若轩, 夏瑞霞. 远程医疗场景下医务人员数字共情的研究进展. 护理研究, 2026 , 40 (15) : 2758 -2763 . DOI: 10.12102/j.issn.1009-6493.2026.15.026
Wancheng LIAO, Xiaohong CHEN, Liandan LIN, Mengying WANG, Ruoxuan AN, Ruixia XIA. Research progress on digital empathy of medical staff in telemedicine scenarios[J]. Chinese Nursing Research, 2026 , 40 (15) : 2758 -2763 . DOI: 10.12102/j.issn.1009-6493.2026.15.026
近年来,在慢性病发病率攀升以及公共卫生事件频发的背景下,远程医疗凭借其便捷性、高效性及可及性优势,在慢性病管理、常见疾病诊疗及应急医疗救援等领域得到广泛应用[1-3]。远程医疗虽打破了诊疗时间与空间的限制,但物理接触的缺失也带来了新的挑战。原本传统医疗中依靠非语言传递的共情信号被削弱,医患间情感连接弱化,可能导致病人对医务人员的就诊满意度下降、治疗依从性降低,甚至影响病人的健康结局[4]。共情作为医务人员必备的职业素养,是建立良好医患关系、提升医疗服务质量的基础[5]。而数字共情作为传统共情在远程医疗场景中的延伸,其对于维系远程场景下的医患情感连接、提升远程医疗服务质量及保障远程诊疗效果也同样重要[6]。目前,国内外研究较多关注远程医疗的技术优化以及成本效益[7-8],对医务人员数字共情的关注尚处于起步阶段,相关概念的界定也未统一。因此,本研究围绕远程医疗场景下医务人员数字共情的概念内涵、评估工具、研究现状、对病人的作用及提升策略展开综述,旨在为弥补远程医疗场景下医务人员的共情缺失、优化医患关系、提升远程医疗服务质量及病人满意度提供理论参考。
共情一词源于德语“einfühlung”,最初用于描述个体对艺术作品的共鸣[9]。Titchener等于1909年首次将共情引入心理学领域,并定义为个体通过想象自己处于他人境遇体验他人的感受[10]。在医疗领域,共情通常被视为一种情感体验能力,要求医务人员站在病人的角度感知并准确识别其情绪,进而满足病人的身心需求[11]。随着远程医疗的兴起,数字共情作为共情在数字语境下的延伸概念被提出,但目前尚未形成统一定义。Terry等[12]于2016年首次在医疗领域提出数字共情概念,将其定义为通过计算机中介沟通所表达的传统共情特征,在医疗场景中具体体现为医务人员借助数字平台向病人传递理解并提供情感支持。随后,Rachmad[13]于2017年提出数字共情理论,其核心原理为数字技术与人类共情能力的融合,将数字共情定义为通过数字媒体理解并感受他人情感的能力。该理论强调尽管技术介入人际互动,但共情的人为因素仍具关键意义。其主张在数字技术介导的沟通中通过关注倾听、理解与情感回应的实践实现真实的情感联结。2019年,Grondin等[14]提出了一个适用于数字媒介沟通的在线共情概念框架,其将数字共情定义为在远程互动中通过媒介传递和感知共情的过程。该框架包括病人发出社会情绪线索、医务人员感受共情、医务人员传达共情行为、病人感知到医务人员的共情4个核心环节。2025年,Abou等[6]结合护理领域的远程医疗场景,将数字共情定义为一种动态适配的共情形式,该形式通过整合护理人员的数字技术熟练度与富含情感的沟通,旨在远程医疗环境中有效满足病人的需求。综上所述,目前学者们对医疗领域数字共情的定义虽存在差异,但其大多包含数字媒介载体、医务人员对病人情感的识别与回应以及医患双方的情感连接3个特点。未来学者可结合数字共情的既往定义和我国文化背景,不断形成和完善在医疗领域数字共情的概念,为后续研究奠定理论基础。
JSE由Hojat等[15]基于文献回顾于2001年编制,主要用于评估医疗领域人员的共情水平,该量表包括医学生、健康专业人员、健康专业学生(除医学生以外的其他健康专业学生)3个版本,3个版本的量表针对不同人群设计,其区别主要在于措辞的微调以适应不同的目标群体。JSE量表包含观点采择、情感关怀以及换位思考3个维度,共20个条目,采用Likert 7级评分法,从“完全不同意”到“完全同意”分别计1~7分,量表总分为20~140分,得分越高表明受试者共情水平越高。3个版本的JSE量表Cronbach's α系数均大于0.7[16]。安秀琴[17]于2008年对JSE健康专业人员版本进行了汉化,结果显示,中文版JSE的Cronbach's α系数为0.75。Kane等[18]于2007年在JSE的基础上编制了病人感知医生共情简表(Jefferson Scale of Patient's Perceptions of Physician Empathy,JSPPPE),该量表包含5个条目,采用Likert 5级评分法,从“完全不同意”到“完全同意”分别计1~5分,量表总分为5~25分,得分越高表明病人感知医生的共情水平越高,量表的Cronbach's α系数为0.58。目前JSE和JSPPPE均已在远程医疗场景中用于测量医务人员或病人感知医务人员的数字共情[19-20],但尚未检索到其在我国医学、护理领域的应用,因此,今后研究还需进一步验证其在我国远程医疗场景下的适用性。
DCES由Collins等[21]于2024年基于文献回顾编制而成,量表包括数字情绪匹配、数字感知共情准确性、数字换位思考、数字同情、数字个人担忧及数字共情愤怒6个维度,每个维度包含6个条目,共36个条目。量表采用Likert 6级评分法,从“非常不同意”到“非常同意”分别计1~6分,所有条目均为正向计分,量表总分为36~216分,得分越高代表个体共情水平越高。量表各维度的Cronbach's α系数为0.83~0.93,总量表的Cronbach's α系数为0.95,信度较好。该量表可从多个维度评估个体在数字沟通场景,如社交媒体、短信、邮件等场景中的个人共情能力。但该量表目前仅在使用数字工具的成年群体中应用过,未结合医务人员的特殊性进行考虑。因此,其在我国医疗领域使用的有效性和适配性还需进一步验证。
ESS由Sperandeo等[22]于2021年基于Barrett-Lennard关系量表[23]改编而成,主要用于评估心理治疗场景中治疗师与病人的共情和支持感知,量表分为治疗师自评版与病人自评版。ESS量表的2个版本结构完全一致,均包含正向与负向2个维度,每个维度包含14个条目,共28个条目。量表采用Likert 4级评分法,从“从不”到“经常”分别计0~3分,其中正向维度采用正向计分,负向维度采用反向计分,2个量表版本总分均为0~84分。治疗师版得分越高,代表治疗师自我感觉向病人传递的共情与支持水平越强;病人版得分越高,代表病人感知到的治疗师共情与支持水平越高。Sperandeo等[22]将该量表用于远程心理治疗场景中的共情评估发现,治疗师与病人在ESS正向维度的评分一致性为70.7%,负向维度的评分一致性为82.9%,证明该量表在评估远程心理治疗场景中治疗师的共情和病人的共情感知具有较好的适用性。但该量表尚未进行信效度检验且仅在远程心理治疗场景中应用过,因此,其测量的可信度、有效性及在其他远程医疗场景中的适用性均需进一步验证。
随着数字技术在医疗领域的快速发展,远程医疗、线上咨询等数字诊疗模式逐步成为传统面对面诊疗模式的重要补充,数字共情作为医务人员在远程场景中传递关怀、感知病人需求的核心能力,其效果直接影响病人的就诊质量和满意度。然而,目前国内外关于医务人员数字共情的研究尚处于初步探索阶段。Subramanya等[19]针对远程会诊与传统面对面诊疗中的共情效果开展试点研究,共纳入34例病人,其中面对面诊疗病人20例,远程医疗病人14例,结果显示,2种模式下病人感知医生的共情水平评分差异无统计学意义,提示2种诊疗模式均能有效实现共情沟通。Sperandeo等[22]聚焦线上视频心理治疗场景,以23例不同病情严重程度的病人和5名心理治疗师为研究对象,从治疗师自我感知与病人接收感知双视角对比共情效果,结果显示,治疗师对自身在2种治疗场景中传递共情与支持的能力差异无统计学意义,而病人感知在远程视频场景中治疗师的共情与支持水平更高。Chosh等[20]则以100例成年物质使用障碍病人为研究对象,比较该类病人在远程与面对面咨询中所感知的医生共情水平,结果显示,病人在远程咨询时感知到的医生共情水平显著低于面对面咨询,且多数病人表示医生在远程咨询期间出现不耐烦的可能性更高。由此可见,当前医务人员数字共情的研究结论存在较大差异,这可能与研究的样本量较少、评估工具不一、评估视角不同有关。尤其是在评估医患双方时出现了明显的感知差异问题,即医务人员认为自己在不同场景下的共情表达无差异,而病人并非如此。这表明问题的关键并不在于医务人员是否拥有共情能力,而在于他们是否通过数字媒介成功地向病人传递了共情。因此,建议未来学者应进一步研究共情传递的机制,设计大样本、多中心的研究重点探讨共情如何在数字媒介中进行有效传递,并明确导致医患感知差异的具体因素,以及医务人员如何调整沟通策略以补偿感知差异,从而为后续提升远程医疗场景下医务人员的数字共情提供有效策略。
根据信息传递能力的强弱,数字媒介通常被从低到高依次排序为文字沟通、纯音频沟通、视频会议以及虚拟现实[14]。研究表明,在仅使用文字沟通的医患交流情景下,非语言线索的缺失会导致医务人员难以准确识别病人的情绪波动,进而增加了医务人员对病人共情需求的判断难度[24]。同时,文字沟通的不同步也会对医务人员共情表达的时机和方式产生影响[25]。而在缺乏视觉线索的情况下,音频沟通比文本更能传递共情。语音保留了语调、语速、停顿等富含情感的副语言线索,是病人感知医务人员共情并建立情感连接的关键,病人常会通过医务人员温和的语调与适时的停顿判断自己是否被倾听与理解[26]。与以上2种数字媒介类型相比,视频沟通能够再现面部表情和肢体语言,有助于医患之间建立信任与情感连接,是当前较为接近面对面交流的数字共情媒介[27]。目前,有关于虚拟现实是否能有效传递共情的研究尚不充分,未来需进一步探讨其作为沉浸式交流媒介在医疗领域数字共情中的实际效果与应用潜力。
医务人员娴熟的数字技术技能是数字共情得以实现的基础保障,而专业的沟通技能则是其有效传递的核心。相关研究表明,在虚拟互动中实施具有共情的护理服务均需医务人员发展或巩固数字技术技能,若医务人员对远程技术操作不熟练,会将其注意力从病人身上转移至技术界面,进而导致共情的缺失[27]。同时,沟通技能在远程互动中也尤为关键。另有研究发现,在远程互动中病人常将被倾听和被关注视为感知共情的核心[28]。这意味着医务人员需要在远程沟通时具备一些特定的沟通行为技巧,例如进行虚拟眼神交流、提问开放性问题、通过寒暄建立关系、引导家属参与对话以及适时跟进病人诉求,以在数字空间中准确传递共情与关怀。
病人的远程咨询时长对医务人员数字共情的表达具有重要影响。Grondin等[14]研究指出,在线情感交流通常需要比面对面互动更长的时间才能达到相近的情感传递深度。若咨询时间过短,医务人员可能难以充分捕捉病人的情绪线索,进而导致其共情表达不足。Rouleau等[27]研究进一步指出,在目前按服务收费且高病人量的诊疗结构下,医务人员被要求每日接诊较多病人,导致每例病人的咨询时间有限。在这种时间压力下,医务人员难以在远程互动中提供全面且富有共情的护理。
病人的参与行为是影响医务人员数字共情呈现的重要因素。Zhang等[29]通过聚类分析将在线问诊中病人的参与行为划分为情感表露型、决策参与型、消费导向型和医生主导型4种类型。其发现医务人员的沟通策略和共情支持力度会因病人类型的不同而呈现差异。当面对情感表露型病人时,医务人员会感知到其表达的焦虑、担忧等负面情绪,并做出相应的共情调整。而对于其他极少表露自身情感的病人,医务人员的回应则更侧重信息提供与诊断建议,其情感支持的频率和深度较低。这表明病人是否主动表露情绪与自身需求将直接导致医务人员差异化的共情反应。该结果揭示了数字共情的双向性特点,即病人并非被动接受者,其主动信息提供行为会引发医务人员更频繁的共情表达。
数字共情在远程医疗场景中对提升病人信任度具有重要作用。吴桂萍等[30]研究发现,当医务人员展现数字共情时医患信任建立的可能性是未展现共情的2.394倍。数字共情能通过情感慰藉有效地缓解病人焦虑情绪,让其在远程沟通中感受到被理解与尊重,进而推动医患信任关系的形成。Zhang等[31]研究进一步验证了这一结论,其指出由于在远程医疗咨询场景中缺乏物理接触,病人更依赖语言和非语言线索判断医务人员的可信度,此时若医务人员提供积极的共情表达就可以增强病人的被重视感,从而提升病人对医务人员的信任度。
病人满意度是评估医疗服务质量的重要指标之一。研究发现,数字共情是远程医疗治疗性关系连接的基本属性,当病人觉得医务人员具有共情时,他们对远程医疗的满意度更高[4]。Han等[32]对我国互联网医院服务质量的分析也发现,数字共情对病人满意度具有显著的正向预测作用,是影响病人满意度的关键维度之一。
医务人员的数字共情也能有效改善病人的心理状态。吴桂萍等[30]研究指出,对病人而言,疾病本身产生的负面情绪有时会比疾病本身带来的生理影响更严重。尤其在医患双方不熟悉的在线问诊中,若医务人员对病人表达共情,更能缓解病人的负面情绪,让病人感受到理解和尊重。Martikainen等[33]研究表明,在基于文字聊天的远程医疗咨询中,病人对医务人员共情的评价越高,其对自己症状的担忧程度越低,症状严重程度的感知也越低,且更可能报告症状得到缓解。
医务人员作为数字共情的实践主体,需主动树立数字场景下的共情意识,提升自身的共情能力,深入学习数字技能和沟通技能,从而打破数字技术带来的情感隔阂。Rey Velasco等[34]研究显示,医务人员在与病人进行文字沟通时常常会导致病人的隐性情感语言被忽略,而Grondin等[14]认为视频虽接近面对面交流却仍需主动调整以弥补非语言线索缺失。Rahmanti等[35]研究发现,面部表情、眼神交流、语气等非语言行为对数字共情识别具有核心作用。因此,医务人员应加强自身对各种数字媒介特性的理解和熟悉不同平台的技术局限与使用模式,从而更精准地捕捉病人在沟通中传递的显性与隐性信息。在远程会诊中医务人员还可以进行一些针对性的练习,比如在文字沟通时主动询问病人情绪、用情感词汇替代非语言线索;在视频沟通时调整摄像头角度保持眼神接触、适度夸张面部表情与点头强化数字共情的传递。Efthymiou等[36]研究指出,学习者可依托异步讨论板、多媒体叙事平台等数字媒体开展反思性实践,有助于提升自身的道德推理能力与人文关怀意识。反思性实践是学习者通过对所学知识和经验进行批判性分析,以获得更深层次理解并指导未来专业行为的学习方法[37]。在传统医疗环境中医务人员通过对日常临床案例的讨论、反思和总结,已被证明可有效提高自身的共情[38]。因此,在目前缺乏专门针对数字共情干预研究的情况下借鉴传统共情训练中成熟的反思性实践方法,并结合数字工具加以改造,有望成为提升医务人员在远程医疗场景中共情水平的重要路径。鉴于此,建议医务人员在每次远程诊疗后撰写反思日记,记录其与病人交流沟通的感悟、发现自身共情行为的不足与改进方向,并借助远程社交平台与同伴进行互动交流以深化反思,最终进一步强化人文关怀意识,提升其在远程场景下理解病人、建立情感联结的数字共情水平。
医疗机构需从平台建设、培训体系与质量评价三方面构建数字共情保障机制。研究发现,表情符号能增强信息的情绪强度和促进情绪传染,在纯文字沟通中引入表情符号可以有效弥补语气和视觉线索的缺失,从而提升共情传递的效果[39]。医疗机构可在文字型远程医疗平台中加入并鼓励医务人员使用表情符号,以辅助其更有效地向病人传递共情。Rey Velasco等[34]研究表明,系统功能语言学框架可被用于有效识别和分类这些隐藏在文本背后的隐性情绪表达。相关医疗组织可以借鉴此思路开发具备自然语言处理能力的人工智能分析工具,通过自动扫描病人提交的文本信息以识别其中的共情机会,进而为医务人员提供实时提示与建议。医疗组织积极开展相关培训也尤为重要,Chuensombat等[39]提出并通过专家验证了一个互动模拟视频的设计框架,主要用于提升学习者的数字共情技能。该视频通过模拟真实场景、嵌入互动问题、提供即时反馈等方式,帮助学习者在认知、技能和态度3个维度上提升数字共情水平。这为医疗组织培训医务人员提升数字共情水平提供了具体、可操作的培训工具和方法。研究表明,数字共情并非附加项而是与专业能力、服务效率同等重要的核心质量维度[31]。因此,未来可将其明确纳入远程医疗服务质量评价指标体系,从而推动医疗机构重视数字共情提升,优化远程医患互动与服务体验。
社会层面的支持是远程医疗场景下医务人员数字共情发展的重要保障。强化社会层面支持,可通过制订相关政策、加大科研支持以及营造社会氛围等措施实现。结合实际医疗现状,制订数字共情相关政策规范,明确医务人员数字共情的实践要求与标准。目前,国内关于数字共情领域的研究尚有欠缺,加大远程医疗数字共情研究支持力度对其发展具有重要意义,设立专项科研基金,鼓励学者开展数字共情相关研究可为实践提供科学依据,如数字共情的影响因素、评估工具开发、干预效果验证等。营造重视数字共情的社会氛围亦是关键,可通过媒体宣传(如科普文章、短视频等)向公众普及远程医疗中数字共情的重要性,引导公众理解数字共情的价值;鼓励病人积极参与数字共情实践,主动向医务人员表达需求与感受,有助于形成医患共同参与的数字共情互动模式。
远程医疗场景下医务人员数字共情是适应数字医疗发展的新型职业素养。目前,关于数字共情的研究呈上升趋势,但我国关于医务人员数字共情的相关研究还较少,今后可尝试开发适用于我国本土文化的评估工具,围绕影响因素分析和干预性研究对其进行深入探讨。同时,医务人员、医疗机构以及社会组织应共同努力提升医务人员数字共情水平,促进医患情感交流,创造信任、互动的医疗环境,对提升病人信任度、满意度以及改善其心理状态具有重要意义。

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2026年第40卷第15期
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doi: 10.12102/j.issn.1009-6493.2026.15.026
  • 接收时间:2025-12-10
  • 首发时间:2026-10-30
  • 出版时间:2026-08-10
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  • 收稿日期:2025-12-10
  • 修回日期:2026-07-05
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    1丽水学院医学院,浙江 323000
    2丽水学院附属第一医院

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2种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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