Joseph A. Costello

Purpose: The residency application and match process is a consequential transition in physicians’ careers. Although designed to “match” applicants and programs based on mutual preferences, applicants' decisions are influenced by contextual, cultural, and personal factors. Despite its importance, the literature on how applicants evaluate and select residency programs remains fragmented and lacks a unifying conceptual framework. This scoping review synthesizes the literature to map factors influencing residency program selection. Method: The authors searched three databases without date restrictions, with the final search conducted in June 2026. Studies were eligible if published between January 2015 and June 2026 and reported residency selection factors among US or Canadian participants involved in the National Resident Matching Program or Canadian Resident Matching Service. Two reviewers independently screened titles/abstracts and full texts. A standardized tool captured bibliographic details, participant characteristics, methods, and reported selection factors. Data were summarized using descriptive statistics and content analysis. Factors were mapped to the Learning Environment framework, encompassing social, organizational, personal, societal, and physical and virtual space components. Results: Sixty-five studies published in 41 journals were included. Most studies used surveys (n = 62; 95.4%), typically author-developed. Four studies (6.2%) used interviews. Most included medical students (n = 48; 73.8%), while 25 (38.5%) included residents or fellows reflecting retrospectively. Across studies, 1,237 factors were identified (mean 19.1 per study). Social (n = 317; 25.6%), Organizational (n = 313; 25.3%), personal (n = 305; 24.7%), and societal factors (n = 255; 20.6%) were similarly reported. Physical and Virtual space factors were infrequently reported (n = 30, 2.4%). Conclusions: Residency selection reflects applicants’ assessments of interrelated learning environment components rather than isolated program attributes. Applying the Learning Environment framework highlights that applicants are selecting not only a training site, but a learning environment where they hope to grow personally and professionally. These findings may inform more intentional program design, recruitment strategies, and applicant decision making.

Introduction: Journal editors-in-chief (EICs) play a central role in shaping scholarly publishing, yet little is known about how they understand and enact their roles. We explored how medical education journal EICs describe their roles in shaping the publication process. Methods: We conducted a qualitative study using thematic analysis of interviews with nine medical education journal EICs. The interviews were part of a broader investigation of publication timelines and explored editorial leadership and practices, including experiences during the COVID-19 pandemic. Participants’ accounts also provided insight into the broader work of leading a journal, which became the focus of the present analysis. Results: Participants described their work not as a set of discrete tasks, but as five interrelated roles: knowledge developer, proactive communicator, steward of time and energy, nurturer, and orchestrator. These roles were enacted simultaneously and often in tension, reflecting the need to balance scholarly rigor, relational commitments, and system constraints. EICs described actively shaping knowledge, orchestrating complex communication networks, safeguarding finite reviewer and editorial resources, supporting authors and editorial teams, and maintaining workflow continuity. Much of this work was characterized as invisible labor occurring behind the scenes and not captured in formal metrics. Participants also highlighted a structural tension between nurturing authors and stewarding limited resources, underscoring the challenges of sustaining both inclusivity and efficiency within a resource-constrained system. Discussion: This study offers a practice-informed framework for understanding EIC work as relational, strategic, and system-oriented. By making visible the often-invisible labor that underpins academic publishing, our findings challenge reductive conceptions of editorial work and highlight the need for training, support, and systemic changes to better align expectations for rigor, inclusivity, and sustainability.

Background: As AI use becomes more common in research, disclosure policies have emerged to ensure transparency and appropriateness. However, database research in other fields suggests that disclosure may lag behind AI use. Medical education journal editors report that submitted manuscripts rarely include AI-use disclosures, and they perceive a lack of clarity regarding when and how AI use should be disclosed. However, we lack objective evidence regarding the incidence and nature of AI-use disclosure in medical education. Methods: Using bibliometric methods, we searched a database of 24 leading medical education journals for articles published between January and July 2025 (n = 2,762 articles). Screening with Covidence software excluded 716 non-empirical and/or non-English language articles. The remainder (n = 2,046) were examined for the presence of AI-use disclosures, which were content-analyzed. Results: 2.5% of empirical articles (n = 51) had an AI disclosure statement. BMC Medical Education contained the most disclosures (24), followed by Medical Teacher (7) and Journal of Surgical Education (4). Forty-two articles were authored in non-native English-speaking countries, and 69.4% of all first authors had begun publishing in the past decade. Disclosures averaged 43 words and described use superficially: most commonly “editing” and “translation”. Of 18 named tools, ChatGPT was most common. Most disclosures explicitly attested to author responsibility for AI-produced material. Disclosures usually appeared in acknowledgements; those located in methods lacked responsibility attestation. Negative disclosures attesting that AI was not used were also present. Discussion: AI-use disclosures in medical education journals are rare and appear mostly in work from non-native English-speaking regions of the world. A shared disclosure practice is evident: name the tool and affirm author responsibility, but describe use superficially. This suggests a practice of “safe” disclosure that may be more performative than informative, therefore failing to satisfy the goal of ensuring transparent and ethical AI use in research.

Problem: 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. Approach: 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. Outcomes: 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%). Next Steps: 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.

In medical education, the number of knowledge syntheses has increased dramatically, reflecting their growth and influence on education practice, research, and policy. However, despite the availability of instruction on many of the steps of conducting knowledge syntheses, practical guidance for the critical step of data extraction is limited. Data extraction is the process of systematically identifying and collecting information from the studies included in a knowledge synthesis. Without clear guidance, data extraction can become flawed and overly time-consuming, ultimately jeopardizing the quality of the knowledge synthesis. This article addresses this gap by offering 12 practical tips for data extraction. The tips are grounded in the literature and informed by the authors' collective experience conducting and mentoring knowledge synthesis projects. Organized into two sections, creating a data extraction tool and operationalizing it, the tips provide actionable guidance on aligning extraction with research objectives, supporting a team-based approach, resolving discrepancies, and how to pilot a data extraction tool. Taken together, these tips aim to improve the rigor, efficiency, and reliability of knowledge synthesis in medical education.

Purpose: Clinical reasoning, an essential skill in medicine, is significantly influenced by emotions. Although the cognitive aspects of clinical reasoning have been extensively studied, the impact of emotions remains underexplored. This scoping review aimed to map the existing literature on the influence of emotions on clinical reasoning across medical education and practice. Method: The authors conducted a scoping review (March 11, 2022, and December 1, 2023) following Arksey and O’Malley’s framework of MEDLINE (Ovid), Embase (Elsevier), Web of Science, and PsycInfo (Ebsco) to identify relevant literature. All empirical studies examining the interplay of emotions and clinical reasoning on the continuum of medical education training were eligible for inclusion. Results: Sixty-one studies met the inclusion criteria. The findings indicate that negative and anxiety-provoking emotions, such as stress, fear, and uncertainty, are predominantly highlighted in the literature as influencing clinical reasoning. These emotions were associated with conservative clinical approaches, increased diagnostic errors, and suboptimal management decisions. Anxiogenic emotions showed mixed effects, sometimes leading to more thorough evaluations but also potentially prompting unnecessary interventions. Conversely, positive emotions and gut feelings, often cultivated through experience, were found to support effective clinical reasoning and potentially improve patient care by fostering adaptability and diagnostic accuracy. Conclusions: This review captures the integral role of emotions in clinical reasoning, impacting every facet of patient care. The nuanced interplay between emotions—both positive and negative—and clinical reasoning emphasizes that emotions not only arise as by products of clinical encounters but also actively shape clinical reasoning. These findings call for educational interventions that equip clinicians with emotional awareness and regulation strategies to enhance their clinical reasoning. Future research should explore the influence of positive emotions, investigate the effectiveness of emotional regulation interventions, and address gaps in understanding the contextual and cultural variability in emotional dimensions of clinical reasoning.

Purpose: Students with disabilities remain disproportionately underrepresented in undergraduate medical education (UME). This scoping review synthesizes the literature on undergraduate medical students with disabilities in the United States to identify the purpose of the literature and map how disability is portrayed. Method: The authors searched 8 databases for relevant publications published between January 2008 through August 2024. Publication inclusion was determined using a 2-phase title/abstract screening process and full-text review conducted by 2 independent authors. Data extraction focused on characterizing the publication, followed by an analysis of the publications’ goals and how disability was portrayed. Results: The authors identified 10,491 publications; 81 were included, most of which were journal articles ( n = 78). In 23 publications, at least one author disclosed their identity as disabled. Five main goals were identified in the publication purpose statements, including describing ( n = 50), calling for action ( n = 25), evaluating ( n = 24), arguing ( n = 14), and creating ( n = 14). Overall, disability was portrayed as (1) diversity, (2) deficiency, (3) inherently connected with accommodation and technical standards, and (4) a place of habitual omissions. Two studies discussed disability in relation to intersectional identities, and 2 used gender or race in multivariable models. No studies explicitly focused on UME and disabled students of color, disability and gender diversity, or disabled LGBQ+ students. Conclusions: This scoping review identified that disability is portrayed from an abled viewpoint as race- and gender-neutral, static, and as diversity, deficit, or a legal construct. Future researchers should consider how disability is conceptualized and the use of intersectional frameworks.

Introduction: The field of medical education (ME) has grown substantially over the past decades, yet questions remain about its scope and boundaries. This study examines how research topics and institutional collaborations have evolved in ME from 2000 to 2019. Methods: Adopting a post-positivist stance and using bibliometric network analyses, we examined metadata from 31,338 publications across 22 core ME journals indexed in the Web of Science. We analyzed trends in institutional collaboration and the development of research themes. Extracted metadata included authors’ institutional affiliations and KeyWords Plus (n = 18,218). Bibliometric analyses were conducted using VOSviewer, a widely used tool for network mapping. We generated co-authorship networks to trace institutional collaboration and co-word networks to identify thematic clusters. Results: Co-authorship networks revealed increasing collaboration, with U.S. institutions remaining central and Canadian and Dutch institutions gaining prominence. Co-word analyses identified three stable clusters—teaching and learning, quantitative, and psychosocial—with teaching and learning dominant across all periods and the quantitative cluster expanding in recent years. Discussion: Findings show the consolidation of teaching and learning as the foundation of ME, alongside diversification through quantitative and psychosocial themes. Growing collaborations suggest the field’s maturation, though geographic imbalances persist. Limitations include reliance on a restricted set of Web of Science journals, which overrepresent English-language and highly cited publications, and the use of KeyWords Plus as a proxy for themes. This study offers an evidence-based mapping of ME’s evolution and provides a framework for future research on the interdisciplinary and global dynamics of the field.

PROBLEM: 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 either search too widely across thousands of irrelevant journals, or too narrowly by relying on PubMed’s Medical Subject Headings (MeSH). In our tests, MeSH missed 34% of medical education papers. APPROACH: We developed the MEC (Medical Education Corpus), the first dedicated collection of medical education papers, through a three-step process. First, using the Core-Periphery model, we created the MEJ (Medical Education Journals), a collection of three groups of journals based on participation and influence in medical education discourse: the MEJ-Core (formerly the MEJ-24, 24 journals), MEJ-Adjacent (127 journals), and MEJ-Peripheral (a theoretical group). Second, we developed and evaluated a machine learning model, the MEC Classifier, trained on 4,032 manually labeled papers to identify medical education content. Finally, we applied the MEC Classifier to extract medical education papers from the MEJ-Core and MEJ-Adjacent journals. OUTCOMES: The MEC currently contains 119,137 medical education papers from the MEJ-Core (54,927 papers) and MEJ-Adjacent journals (64,210 papers). In our evaluation using 1,358 test papers, the MEC Classifier demonstrated significantly improved sensitivity compared to MeSH (90% vs 66%, p = 0.001), even while maintaining a similar positive predictive value (82% vs 81%). NEXT STEPS: The MEC provides a focused, searchable corpus that enables medical education scholars to more easily join conversations in the field. Scholars can now 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 MEC is freely available in the Supplement and is regularly updated on MedEdMentor ( mededmentor.org ), where we will also incorporate community feedback to further improve and expand the corpus.

Introduction: Improving health care quality and patient safety are top priorities for the medical field. Robust continuing medical education (CME) programs represent major interventions to effectively teach quality improvement (QI) principles to practicing physicians. In particular, eLearning, a term describing online and distance learning interventions using digital tools, provides a means for CME interventions to reach broader audiences. Although there has been a focus on CME addressing QI, no knowledge synthesis has focused specifically on eLearning interventions. The purpose of this review was to examine the current landscape of eLearning interventions in QI-focused CME. Methods: We conducted a scoping review using the framework developed by Arksey and O'Malley as revised by Levac. We searched five databases and identified 2467 prospective publications, which two authors independently screened for inclusion. From each included article, two authors independently extracted data on the instructional modalities and QI tools used and met regularly to achieve consensus. Results: Twenty-one studies were included. Most studies used blended instruction (n = 12) rather than solely eLearning interventions. Salient findings included the importance of coaching from QI experts and institutional support for planning and implementing eLearning interventions. Lack of protected time and resources for participants were identified as barriers to participation in CME activities, with small practices being disproportionately affected. Discussion: Partnerships between CME developers and sponsoring organizations are vital in creating sustainable eLearning interventions for QI-focused CME. Remote coaching can be an effective strategy to provide ongoing support to geographically separated learners.