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Medical school admissions competencies: what problems need to be solved? 医学院招生能力:需要解决哪些问题?
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf053
Karen E Hauer, Erick Hung

Premedical or admissions competencies define a standard set of expectations for medical school candidates to guide their preparation for and application to medical school. These competencies also support medical school admissions committees in advancing fair and efficient application review. In their article updating the 2011 premedical competencies with a revised model consisting of 17 competencies that span professional, science, and thinking and reasoning domains, Wasserman and colleagues have attempted to characterize a qualified medical school applicant poised for success. While these competencies constitute a laudable effort, additional refinement may be needed over time to address emerging areas in medical education and health care. These include incorporating qualitative reasoning, fostering artificial intelligence literacy, emphasizing feedback literacy, and articulating the skills required to reconcile competing needs, ultimately reframing professionalism as a complex, adaptive challenge of developing professional identity within a demanding environment. To operationalize these competencies, a robust toolkit with resources, assessment tools, and training materials will be needed. To understand how well the updated competencies capture the enduring and evolving expectations for incoming medical students, medical educators must ask what problems these competencies are trying to solve. In this commentary, the authors propose 3 problems that admissions competencies could solve: (1) linking education to health outcomes, (2) promoting fairness in admissions, and (3) cultivating physicians skilled in relationship building and team functioning. For the competencies to address these problems, medical schools must continue to commit to holistic review processes that evaluate applicants within their unique contexts and opportunities. Focusing the admissions competency framework on behavior-based guidance, rather than on prescriptive experiences, creates a more accessible pathway to medical education that honors applicants' backgrounds, while identifying qualified candidates with the potential to become compassionate, adaptable physicians prepared for the ever-evolving health care landscape. Teaser text: This commentary reflects on updated premedical competencies that characterize a qualified medical school applicant. Forward looking premedical student competencies should incorporate qualitative reasoning, fostering artificial intelligence literacy, emphasizing feedback literacy, and articulating the skills required to reconcile competing needs, ultimately reframing professionalism as a complex, adaptive challenge of developing professional identity within a demanding environment. Used well, premedical competencies can facilitate linking education to health outcomes, promote fairness in admissions, and cultivate physicians' skills in relationship building and team functioning.

医学院预科或入学能力定义了医学院候选人的一套标准期望,以指导他们准备和申请医学院。这些能力也支持医学院招生委员会推进公平和有效的申请审查。Wasserman和他的同事们在他们的文章中更新了2011年的医学预科能力,其中包含了跨越专业、科学、思维和推理领域的17种能力,他们试图描述一个合格的医学院申请者准备成功的特征。虽然这些能力是值得称赞的努力,但随着时间的推移,可能需要进一步改进,以解决医学教育和保健中的新兴领域。这些包括结合定性推理,培养人工智能素养,强调反馈素养,阐明协调竞争需求所需的技能,最终将专业精神重新定义为在苛刻的环境中发展专业身份的复杂的适应性挑战。为了实现这些能力,将需要一个包含资源、评估工具和培训材料的强大工具包。为了理解更新后的能力在多大程度上满足了对即将入学的医科学生的持久和不断变化的期望,医学教育者必须问这些能力试图解决什么问题。在这篇评论中,作者提出了招生能力可以解决的3个问题:(1)将教育与健康结果联系起来,(2)促进招生公平,(3)培养善于建立关系和团队运作的医生。为了具备解决这些问题的能力,医学院必须继续致力于全面审查程序,根据申请人的独特背景和机会对其进行评估。将招生能力框架的重点放在基于行为的指导上,而不是规范的经验上,这为医学教育创造了一条更容易接近的途径,既尊重申请人的背景,又能识别出有潜力成为富有同情心、适应性强的医生的合格候选人,为不断发展的医疗保健领域做好准备。导语:这篇评论反映了一个合格的医学院申请人的最新医学前能力。前瞻性医学预科学生的能力应包括定性推理、培养人工智能素养、强调反馈素养、阐明协调相互竞争的需求所需的技能,最终将专业精神重新定义为在苛刻的环境中发展职业身份的复杂、适应性挑战。如果运用得当,医前能力可以促进教育与健康结果的联系,促进招生公平,培养医生建立关系和团队运作的技能。
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引用次数: 0
Faculty developers in academic medicine: roles and competencies in times of change. 学术医学的教师发展:变革时代的角色和能力。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf054
Heather A Billings, Stacey Pylman, Larry Hurtubise, Judy Blebea, David P Way, Rachel Moquin, Anthony Gaynier, Deborah Simpson

The demand for high-quality faculty development in medical education is more acute than ever. Faculty developers' responsibilities are growing exponentially as they are called upon to help medical faculty maintain expertise in an evolving array of content domains, curricular and assessment strategies, education technologies, and active learning methods. Faculty development has a long history of supporting medical education colleagues in times of change. More recently expectations for faculty developers are expanding due to (1) increasing enrollment of learners, (2) emphasis on learner-centered teaching practices and competency-based assessment methods, and (3) heightened accreditation standards for training faculty. Yet, faculty development in medical education lacks the conventional structures of a profession, such as an adopted set of competencies. In 2023, a series of sessions at regional, national, and international medical education conferences were held to generate ideas and collect examples of faculty developer competencies from over 100 stakeholders. More than 500 responses were gathered from prompts such as "What are the competencies faculty developers need?" and "What is one of the most valuable faculty developer competencies today?" This perspective offers both a rationale for establishing faculty development in medical education as a profession and a path forward through the provision and broad promotion of clearly defined core competencies. The competencies outlined are intended to help inform those entering or currently working in the profession, recruiters of faculty developers, medical education leaders, institutional partners, those conducting performance appraisals of faculty developers, and the broader medical education community. The aim of this work is to generate discussion among vested medical education and faculty development stakeholders who seek to provide structure, clarify roles, and further define medical education faculty development as a profession.

医学教育对高素质师资队伍建设的需求比以往任何时候都更加迫切。教师开发人员的责任正呈指数级增长,因为他们被要求帮助医学院在不断发展的内容领域、课程和评估策略、教育技术和主动学习方法方面保持专业知识。教师发展在变革时期支持医学教育同仁的历史悠久。最近,由于(1)学习者人数的增加,(2)强调以学习者为中心的教学实践和基于能力的评估方法,以及(3)提高了培训教师的认证标准,对教师开发人员的期望正在扩大。然而,医学教育中的教师发展缺乏一个专业的传统结构,比如一套被采用的能力。2023年,在区域、国家和国际医学教育会议上举行了一系列会议,从100多个利益相关者那里产生想法并收集教师开发人员能力的例子。从诸如“教师开发人员需要什么能力?”和“当今最有价值的教师开发人员能力之一是什么?”等提示中收集了500多个回复。这一观点既为将医学教育中的教师发展作为一种职业提供了理论依据,也为通过提供和广泛促进明确定义的核心能力提供了一条前进的道路。概述的能力旨在帮助那些进入或目前在该行业工作的人,教师开发人员的招聘人员,医学教育领导者,机构合作伙伴,对教师开发人员进行绩效评估的人员以及更广泛的医学教育界。这项工作的目的是在既得的医学教育和教师发展利益相关者之间产生讨论,他们寻求提供结构,澄清角色,并进一步将医学教育教师发展定义为一种职业。
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引用次数: 0
One academic health center's response to a freedom of expression controversy. 一个学术健康中心对言论自由争议的回应。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf007
John R Raymond, Laura C Michaelis, Adrienne B Mitchell, Michael DeBisschop, Elizabeth M Drew, Christopher Fadumiye, Natalie Fleury, Kelly Horton, Daniel Hughes, Thomas Mier, Abhijai Singh, Elizabeth Sweeny, Alicia Witten, Heather Carroll, M Tracy Zundel, Cheryl A Maurana

Academic health centers (AHCs) face unique challenges regarding freedom of expression, a topic that recently has caused considerable public controversy at universities across the United States. Little has been published on institutional responses to these controversies. To inspire conversation, reflection, and policy development at other AHCs, in this article, the authors outline steps taken by the Medical College of Wisconsin (MCW) in 2023 and 2024 to respond to an immediate challenge to freedom of expression and to develop a long-term response that will support the institution's commitment to freedom of expression as an essential element of education, patient care, and scientific inquiry. The authors describe the work of the MCW Committee on Freedom of Expression, including its formation, the steps it took to develop guiding principles, and the reaction of faculty, staff, and particularly medical students to initial educational programming around applying the newly developed principles. As a private institution, MCW has greater legal latitude than public AHCs, which allowed the committee to engage leaders and stakeholders in a reflective process, asking questions about the institution's position as a "public square," how to best address the needs of institutional and clinical partners, and the impact of power and inequity on broad protections of freedom of expression. To date, among stakeholders, students have been the most hesitant to embrace the new principles and consistently have shared concerns. Feedback from students has demonstrated that self-censorship is widespread, social justice concerns are a high priority, and structured programming can support and scaffold constructive conversations. The authors conclude that providing opportunities for engagement with institutional freedom of expression principles, especially when uncomfortable, is an essential step in integrating them into educational and clinical practices.

学术健康中心(AHCs)在言论自由方面面临着独特的挑战,这一话题最近在美国各地的大学引起了相当大的公众争议。有关机构对这些争议的回应的文章很少。为了激发其他ahc的对话、反思和政策制定,在本文中,作者概述了威斯康星医学院(MCW)在2023年和2024年采取的步骤,以应对言论自由面临的直接挑战,并制定长期应对措施,以支持该机构将言论自由作为教育、患者护理和科学探究的基本要素的承诺。作者描述了MCW言论自由委员会的工作,包括它的组成,它为制定指导原则所采取的步骤,以及教职员工,特别是医科学生对围绕应用新制定的原则的初步教育方案的反应。作为一个私人机构,MCW比公共ahc拥有更大的法律自由度,这使得委员会能够让领导人和利益相关者参与反思过程,询问有关该机构作为“公共广场”的地位,如何最好地满足机构和临床合作伙伴的需求,以及权力和不平等对广泛保护言论自由的影响。迄今为止,在利益相关者中,学生们对接受新原则最为犹豫,并一直有共同的担忧。来自学生的反馈表明,自我审查是普遍存在的,社会正义问题是高度优先考虑的,结构化的编程可以支持和支撑建设性的对话。作者的结论是,提供参与机构自由表达原则的机会,特别是在不舒服的时候,是将它们整合到教育和临床实践中的重要一步。
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引用次数: 0
What premedical students need to succeed: updated premed competencies for entering medical students. 医学预科学生需要什么才能成功:为进入医学院的学生更新医学预科能力。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf069
Gautam Krishna Koipallil, Meghana Reddy, Elimarys Perez-Colon
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引用次数: 0
Using the past to explain the present: understanding tiered grading in medical education. 用过去解释现在:对医学教育分级制度的理解。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf015
James F Smith, Nicole M Piemonte
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引用次数: 0
Artificial intelligence-assisted shared decision-making training for medical students transitioning to residency. 向住院医师过渡的医学生的人工智能辅助共享决策培训。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf006
Young-Min Kim, Young-Mee Lee, Do-Hwan Kim, Suyoun Kim, Ji-Hoon Kim, Hye Rim Jin, Chang-Jin Choi

Problem: Although the use of artificial intelligence (AI) as a diagnostic aid is increasing in clinical practice, medical education provides little training on how to incorporate AI-generated information into diagnosis and use it effectively in shared decision-making (SDM) with patients.

Approach: The authors developed and piloted a simulation-based course to train AI-assisted SDM to final-year medical students preparing for residency. Conducted between June and October 2023, the course combined online prelearning with onsite simulations using clinically approved AI tools (Lunit INSIGHT CXR, version 3.1.4.1 and MMG, version 1.1.4.3; Lunit Inc, Seoul, South Korea; used November 16 and 27, 2023). Scenarios portrayed asymptomatic patients with incidental findings (eg, pulmonary nodules, breast microcalcifications). Students engaged in two 12-minute simulated patient encounters featuring SDM with 2 management options. Sessions concluded with simulated patient-written feedback and expert-facilitated debriefing. Twenty-seven students from 3 medical schools participated.

Outcomes: Program evaluation showed significant improvements in participants' comprehension and confidence in SDM (t = 6.51 and t = 7.56, P < .001, respectively) and AI-assisted SDM (t = 5.72 and t = 5.80, P < .001, respectively). Students found AI tools helpful for facilitating SDM and patient communication. Thematic analysis of interviews highlighted strengths, such as structured course design and reflective debriefing. Participants noted that prior education focused on diagnostic algorithms, whereas this course emphasized patient communication and preference-based decisions. They found AI tools useful for diagnosis and supporting discussion with patients through visual outputs. However, they identified limitations, including their own clinical knowledge gaps and lack of explainability in AI tool shortage. They suggested integrating SDM and AI-assisted diagnosis training into formal curricula to better prepare students for clinical practice.

Next steps: Future efforts should focus on integrating this course into undergraduate curricula or transition training programs to provide experiential learning opportunities in AI-assisted clinical practice.

问题:尽管在临床实践中越来越多地使用人工智能(AI)作为诊断辅助手段,但医学教育几乎没有提供关于如何将人工智能生成的信息纳入诊断并有效地将其用于与患者共同决策(SDM)的培训。方法:作者开发并试点了一门基于模拟的课程,为准备住院医师的最后一年级医学生培训人工智能辅助SDM。该课程于2023年6月至10月进行,使用临床批准的人工智能工具(Lunit INSIGHT CXR,版本3.1.4.1和MMG,版本1.1.4.3;Lunit Inc,韩国首尔;于2023年11月16日至27日使用),将在线预学习与现场模拟相结合。场景描述无症状患者附带发现(如肺结节,乳房微钙化)。学生们参与了两次12分钟的模拟患者接触,其中包括SDM和两种管理选择。会议以模拟患者书面反馈和专家指导的汇报结束。来自3所医学院的27名学生参与了研究。结果:项目评估显示参与者对SDM的理解和信心(t = 6.51和t = 7.56, P < 0.001)和ai辅助SDM (t = 5.72和t = 5.80, P < 0.001)有显著改善。学生发现人工智能工具有助于促进SDM和患者沟通。访谈的专题分析突出了优势,如结构化的课程设计和反思性汇报。参与者注意到先前的教育侧重于诊断算法,而本课程强调患者沟通和基于偏好的决策。他们发现人工智能工具有助于诊断,并通过视觉输出支持与患者的讨论。然而,他们发现了局限性,包括他们自己的临床知识差距和人工智能工具短缺缺乏可解释性。他们建议将SDM和人工智能辅助诊断培训纳入正式课程,以更好地为学生的临床实践做好准备。下一步:未来的努力应该集中在将这门课程整合到本科课程或过渡培训计划中,以提供人工智能辅助临床实践的体验学习机会。
{"title":"Artificial intelligence-assisted shared decision-making training for medical students transitioning to residency.","authors":"Young-Min Kim, Young-Mee Lee, Do-Hwan Kim, Suyoun Kim, Ji-Hoon Kim, Hye Rim Jin, Chang-Jin Choi","doi":"10.1093/acamed/wvaf006","DOIUrl":"https://doi.org/10.1093/acamed/wvaf006","url":null,"abstract":"<p><strong>Problem: </strong>Although the use of artificial intelligence (AI) as a diagnostic aid is increasing in clinical practice, medical education provides little training on how to incorporate AI-generated information into diagnosis and use it effectively in shared decision-making (SDM) with patients.</p><p><strong>Approach: </strong>The authors developed and piloted a simulation-based course to train AI-assisted SDM to final-year medical students preparing for residency. Conducted between June and October 2023, the course combined online prelearning with onsite simulations using clinically approved AI tools (Lunit INSIGHT CXR, version 3.1.4.1 and MMG, version 1.1.4.3; Lunit Inc, Seoul, South Korea; used November 16 and 27, 2023). Scenarios portrayed asymptomatic patients with incidental findings (eg, pulmonary nodules, breast microcalcifications). Students engaged in two 12-minute simulated patient encounters featuring SDM with 2 management options. Sessions concluded with simulated patient-written feedback and expert-facilitated debriefing. Twenty-seven students from 3 medical schools participated.</p><p><strong>Outcomes: </strong>Program evaluation showed significant improvements in participants' comprehension and confidence in SDM (t = 6.51 and t = 7.56, P < .001, respectively) and AI-assisted SDM (t = 5.72 and t = 5.80, P < .001, respectively). Students found AI tools helpful for facilitating SDM and patient communication. Thematic analysis of interviews highlighted strengths, such as structured course design and reflective debriefing. Participants noted that prior education focused on diagnostic algorithms, whereas this course emphasized patient communication and preference-based decisions. They found AI tools useful for diagnosis and supporting discussion with patients through visual outputs. However, they identified limitations, including their own clinical knowledge gaps and lack of explainability in AI tool shortage. They suggested integrating SDM and AI-assisted diagnosis training into formal curricula to better prepare students for clinical practice.</p><p><strong>Next steps: </strong>Future efforts should focus on integrating this course into undergraduate curricula or transition training programs to provide experiential learning opportunities in AI-assisted clinical practice.</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145985867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Joining the conversation: introducing a dedicated medical education corpus. 加入对话:引入专门的医学教育语料库。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf008
Gregory M Ow, Geoffrey V Stetson, Joseph A Costello, Anthony R Artino, Lauren A Maggio
<p><strong>Problem: </strong>Medical education scholars struggle to join ongoing conversations in their field due to the lack of a dedicated medical education corpus. Without such a corpus, scholars must search too widely across thousands of irrelevant journals or too narrowly by relying on PubMed's Medical Subject Headings (MeSH). In tests conducted for this study, MeSH missed 34% of medical education articles.</p><p><strong>Approach: </strong>From January to December 2024, the authors developed the Medical Education Corpus (MEC), the first dedicated collection of medical education articles, through a 3-step process. First, using the core-periphery model, they created the Medical Education Journals (MEJ), a collection of 2 groups of journals based on participation and influence in medical education discourse: the MEJ-Core (formerly the MEJ-24, 24 journals) and the MEJ-Adjacent (127 journals). Second, they developed and evaluated a machine learning model, the MEC Classifier, trained on 4,032 manually labeled articles to identify medical education content. Third, they applied the MEC Classifier to extract medical education articles from the MEJ-Core and MEJ-Adjacent journals.</p><p><strong>Outcomes: </strong>As of December 2024, the MEC contained 119,137 medical education articles from the MEJ-Core (54,927 articles) and MEJ-Adjacent journals (64,210 articles). In an evaluation using 1,358 test articles, the MEC Classifier demonstrated significantly improved sensitivity compared with MeSH (90% vs 66%, P = .001), while maintaining a similar positive predictive value (82% vs 81%).</p><p><strong>Next steps: </strong>The MEC provides a focused corpus that enables medical education scholars to more easily join conversations in the field. Scholars can rely on the MEC when reviewing literature to frame their work, and the MEC also creates opportunities for field-wide analyses and meta-research. The core methodology also underlies the MedEdMentor Paper Database (mededmentor.org), a separately maintained online tool that complements the versioned MEC snapshot with a web-based search interface.Teaser text: Medical education scholars often struggle to effectively "join the conversation" because relevant literature is buried within biomedical databases like PubMed or general academic search engines like Google Scholar. This article introduces the Medical Education Corpus (MEC), a dedicated collection of 119,137 medical education articles curated using a specialized machine-learning classifier. In head-to-head testing, the MEC significantly outperformed PubMed's MeSH terms, capturing 90% of medical education articles compared with MeSH's 66%. By assembling these articles into a single, focused dataset, the MEC allows scholars to more easily find the literature they need to frame their work. The core methodology also underlies MedEdMentor, a separately maintained online tool that makes these optimized searches accessible to the wider medical education community.
问题:由于缺乏专门的医学教育语料库,医学教育学者很难加入他们领域内正在进行的对话。如果没有这样的语料库,学者们必须在成千上万不相关的期刊中进行过于广泛的搜索,或者依靠PubMed的医学主题标题(MeSH)进行过于狭隘的搜索。在为这项研究进行的测试中,MeSH漏掉了34%的医学教育文章。方法:从2024年1月到12月,作者通过三个步骤开发了医学教育语料库(MEC),这是第一个专门的医学教育文章集。首先,利用核心-外围模型,他们创建了医学教育期刊(MEJ),这是基于医学教育话语参与和影响力的两组期刊的集合:MEJ-核心(以前的MEJ-24, 24种期刊)和MEJ-相邻(127种期刊)。其次,他们开发并评估了一个机器学习模型MEC Classifier,该模型对4,032篇人工标记的文章进行了训练,以识别医学教育内容。第三,他们应用MEC分类器从MEJ-Core和mej -邻期刊中提取医学教育文章。结果:截至2024年12月,MEC包含来自mej核心期刊(54,927篇)和mej邻近期刊(64,210篇)的119,137篇医学教育文章。在使用1,358篇试验文章的评估中,MEC分类器与MeSH相比显示出显着提高的敏感性(90%对66%,P = .001),同时保持相似的阳性预测值(82%对81%)。下一步:MEC提供了一个集中的语料库,使医学教育学者能够更容易地加入该领域的对话。学者们在回顾文献时可以依靠MEC来构建他们的工作,MEC也为领域范围的分析和元研究创造了机会。核心方法也是MedEdMentor论文数据库(mededmentor.org)的基础,这是一个单独维护的在线工具,它通过基于web的搜索界面补充了版本化的MEC快照。导语:医学教育学者常常难以有效地“加入对话”,因为相关文献被埋没在PubMed等生物医学数据库或谷歌Scholar等通用学术搜索引擎中。本文介绍了医学教育语料库(MEC),这是一个使用专门的机器学习分类器策划的119,137篇医学教育文章的专用集合。在正面测试中,MEC的表现明显优于PubMed的MeSH,捕获了90%的医学教育文章,而MeSH的捕获率为66%。通过将这些文章汇编成一个单一的、集中的数据集,MEC允许学者更容易地找到他们需要的文献来构建他们的工作。核心方法也是MedEdMentor的基础,这是一个单独维护的在线工具,可以让更广泛的医学教育界访问这些优化的搜索。
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引用次数: 0
Seeking stabilization: how medical learners engage with shame during training. 寻求稳定:医学学习者在训练中如何处理羞耻。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf029
Anna V Kulawiec, Luna Dolezal, William E Bynum

Purpose: Shame is a deeply personal, complex, and underexplored emotion in medical training; however, how medical learners engage with shame (ie, how they process, recover from, and/or address shame) and the environmental factors affecting this process are currently unknown. This study used hermeneutic phenomenology to explore how medical learners (ie, resident physicians and medical students) engage with shame once it has occurred and the factors that influence this engagement.

Method: This study, which is part of a qualitative research program addressing shame in medical learners, used data collected from 12 residents (2016 and 2017) and 16 medical students (2018) from a residency program and a medical school in the United States. Data collection occurred via semistructured interviews during which participants reflected on shame experiences, including their engagement with it. The authors selected 14 transcripts (7 medical students, 7 residents) to achieve a range of shame experiences and impacts. Data were analyzed using Ajjawi and Higgs' 6 steps of hermeneutic analysis.

Results: Internal scaffolding (thought processes, self-evaluative tendencies, and position relative to others that informed participants' self-concept) was central to shame engagement. Learners' internal scaffoldings shaped and were shaped by distressing shame-integrating engagement (ie, hiding the self, deflecting shame, and transferring shame) and constructive shame-disintegrating engagement (ie, orienting toward others, exerting agency over self-evaluation, and reorienting to a core sense of self). Learning environments influenced shame engagement; environmental values that promoted shame-disintegrating engagement included learner centeredness, inclusivity, vulnerability, and respect.

Conclusions: Although struggle in medical training is inevitable, how learners respond to the shame that can follow is not. The divergent nature of shame engagement highlights the importance of learner agency and environmental response to shame. The authors provide specific suggestions for learners, faculty, and leaders to advance constructive shame engagement and the growth, connection, and belonging it can inspire.

目的:在医学训练中,羞耻是一种非常个人化的、复杂的、未被充分发掘的情感;然而,医学学习者如何与羞耻感接触(即他们如何处理、从羞耻感中恢复和/或解决羞耻感)以及影响这一过程的环境因素目前尚不清楚。本研究使用解释学现象学探讨医学学习者(即住院医师和医学生)在羞耻发生后如何参与以及影响这种参与的因素。方法:本研究是解决医学学习者羞耻感的定性研究项目的一部分,使用了来自美国一个住院医师项目和一所医学院的12名住院医生(2016年和2017年)和16名医学生(2018年)的数据。数据收集是通过半结构化访谈进行的,在访谈中,参与者反映了他们的羞耻经历,包括他们对羞耻的参与。作者选择了14份成绩单(7名医学生,7名住院医生)来获得一系列的羞耻经历和影响。数据分析采用Ajjawi和Higgs的6步解释学分析。结果:内部支架(思维过程、自我评价倾向和相对于他人的位置,这些影响了参与者的自我概念)是羞耻感参与的核心。学习者的内部框架通过痛苦羞耻整合参与(即隐藏自我、转移羞耻和转移羞耻)和建设性羞耻分解参与(即面向他人、在自我评价中发挥代理作用和重新定位于核心自我感)形成和被塑造。学习环境影响羞耻投入;促进羞耻感消解参与的环境价值观包括以学习者为中心、包容、脆弱和尊重。结论:虽然在医学训练中挣扎是不可避免的,但学习者如何应对随之而来的羞耻却不是。羞耻感参与的差异性突出了学习者能动性和羞耻感环境反应的重要性。作者为学习者、教师和领导者提供了具体的建议,以促进建设性的羞耻参与,以及它可以激发的成长、联系和归属感。
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引用次数: 0
Use of entrustable professional activities for reliable overall entrustment decisions. 利用可信赖的专业活动进行可靠的整体委托决策。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf001
Daniel J Schumacher, Daniel J Sklansky, Brian Rissmiller, Lynn Thoreson, Linda A Waggoner-Fountain, Rajat Pareek, Sue E Poynter, Ariel S Winn, Catherine Michelson, Benjamin Kinnear, David A Turner, Leah S Millstein, Jennifer R Di Rocco, Kelsie Avants, Joanna Lewis, Pavan Srivastava, Erin L Giudice, Michelle Arandes, Sylvia Yeh, Alan Schwartz, Daniel J Schumacher, Daniel J Sklansky, Brian Rissmiller, Lynn Thoreson, Linda A Waggoner-Fountain, Rajat Pareek, Sue E Poynter, Ariel S Winn, Catherine Michelson, Benjamin Kinnear, David A Turner, Leah S Millstein, Jennifer R Di Rocco, Kelsie Avants, Joanna Lewis, Pavan Srivastava, Erin L Giudice, Michelle Arandes, Sylvia Yeh, Alan Schwartz

Purpose: Entrustable professional activities (EPAs) detail essential activities within a given specialty. Although 17 general pediatrics EPAs have been defined, it is not known how many are needed to make high-reliability overall entrustment decisions about resident readiness for practice at the time of graduation and initial certification. This study sought to determine how many general pediatrics EPAs are needed.

Method: During the 2021 to 2022, 2022 to 2023, and 2023 to 2024 academic years, the authors collected entrustment-supervision levels, determined by clinical competency committees biannually, for the 17 general pediatrics EPAs for residents at 48 U.S. pediatric residency training programs. Midyear reports were collected between November and January of each year, and end-of-year reports were collected between May and July. The authors conducted generalizability and decision studies to determine the number of EPAs needed to make a reliable overall entrustment decision.

Results: A total of 166,077 individual entrustment-supervision levels were collected for 4,250 pediatric residents across the 17 general pediatrics EPAs. Across all data reporting cycles, the authors found that assessing 6 EPAs yields a generalizability coefficient of 0.8 and assessing 12 EPAs yields a generalizability coefficient of 0.9. However, results differed for midyear compared with end-of-year data collection timepoints as well as by postgraduate year. At graduation, 9 to 13 EPAs are needed to make a highly reliable (generalizability coefficient of 0.9) overall decision about degree of entrustment for unsupervised practice.

Conclusions: This study provides rich insight into the number of EPAs needed to make reliable entrustment decisions about resident readiness to provide patient care. Although readiness can be determined with as few as 9 general pediatrics EPAs (an assessment task), more may be needed to inform a comprehensive curriculum that ensures focus in all areas important to developing general pediatricians during residency training (a curricular task).Teaser text: This study sought to determine how many entrustable professional activities are necessary to make high reliability overall entrustment decisions about pediatric resident readiness for unsupervised practice.

目的:可信赖的专业活动(EPAs)详细描述了特定专业内的基本活动。虽然已经定义了17个普通儿科EPAs,但尚不清楚需要多少EPAs才能在毕业和初始认证时对住院医师的实践准备情况做出高可靠性的总体委托决策。这项研究试图确定需要多少普通儿科epa。方法:在2021年至2022年、2022年至2023年和2023年至2024学年期间,作者收集了48个美国儿科住院医师培训项目的17个普通儿科EPAs的委托监督水平,由临床能力委员会每两年确定一次。每年11月至1月收集年中报告,5月至7月收集年终报告。作者进行了概括性和决策研究,以确定做出可靠的总体委托决策所需的epa数量。结果:共收集到17个普通儿科epa 4250名儿科住院医师的个人委托监督水平166077条。在所有的数据报告周期中,作者发现评估6个epa产生0.8的通用性系数,评估12个epa产生0.9的通用性系数。然而,年中与年终数据收集时间点以及研究生学年的结果有所不同。毕业时,需要9 ~ 13个EPAs对无监督实践的委托程度做出一个高可靠(泛化系数为0.9)的总体决策。结论:本研究提供了丰富的见解所需的EPAs的数量作出可靠的委托决策的居民准备提供病人护理。虽然准备就绪可以通过9个普通儿科epa(一项评估任务)来确定,但可能需要更多的信息来提供一个全面的课程,以确保在住院医师培训(一项课程任务)期间关注发展普通儿科医生的所有重要领域。摘要:本研究旨在确定有多少可信赖的专业活动是必要的,以使高可靠性的总体委托决策的儿科住院医师准备无监督的做法。
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引用次数: 0
Psychological safety and stress in the learning environment. 学习环境中的心理安全和压力。
IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Pub Date : 2025-12-03 DOI: 10.1093/acamed/wvaf040
Alaina B Mui, Timothy D Bradley, Erika S Abel
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Academic Medicine
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