Medical Education Online最新文献

{"title":"Digital health competences and AI beliefs as conditions for the practice of evidence-based medicine: a study of prospective physicians in Canada.","authors":"Gerit Wagner, Mickaël Ringeval, Louis Raymond, Guy Paré","doi":"10.1080/10872981.2025.2459910","DOIUrl":"10.1080/10872981.2025.2459910","url":null,"abstract":"<p><strong>Background: </strong>The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals' competences with digital health (dHealth) and artificial intelligence (AI) technologies.</p><p><strong>Objective: </strong>We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.</p><p><strong>Methods: </strong>A cross-sectional survey was administered online to students at the University of Montreal's medical school, which has approximately 1,400 enrolled students. The survey included questions on students' dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.</p><p><strong>Results: </strong>Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, <i>p</i> < 0.001), as well as students' perceptions of the role of AI in the future of medicine (β = 0.39, <i>p</i> < 0.001), which, in turn, also affect EBM (β = 0.19, <i>p</i> < 0.05).</p><p><strong>Conclusions: </strong>The study underscores the necessity of enhancing students' competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2459910"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11789221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the significance of medical humanities in shaping internship performance: insights from curriculum categories.","authors":"Chao Ting Chen, Anna Y Q Huang, Po-Hsun Hou, Ji-Yang Lin, His-Han Chen, Shiau-Shian Huang, Stephen J H Yang","doi":"10.1080/10872981.2024.2444282","DOIUrl":"10.1080/10872981.2024.2444282","url":null,"abstract":"<p><strong>Background: </strong>Medical Humanities (MH) curricula integrate humanities disciplines into medical education to nurture essential qualities in future physicians. However, the impact of MH on clinical competencies during formative training phases remains underexplored. This study aimed to determine the influence of MH curricula on internship performance.</p><p><strong>Methods: </strong>The academic records of 1364 medical students across 8 years of admission cohorts were analyzed. Performance in basic sciences, clinical skills, MH, and internship rotations were investigated, including the subgroup analysis of MH curricula. Ten-fold cross-validation machine learning models (support vector machines, logistic regression, random forest) were performed to predict the internship grades. In addition, multiple variables regression was done to know the independent impact of MH on internship grades.</p><p><strong>Results: </strong>MH showed the important roles in predicting internship performance in the machine learning model, with substantially reduced predictive accuracy after excluding MH variables (e.g. Area Under the Curve (AUC) declining from 0.781 to 0.742 in logistic regression). Multiple variables regression revealed that MH, after controlling for the scores of other subjects, has the highest odds ratio (OR: 1.29, <i>p</i> < 0.0001) on internship grades. MH explained 29.49% of the variance in internship grades as the primary variable in stepwise regression. In the subgroup analysis of MH curricula, Medical Sociology and Cultural Studies, as well as Communication Skills and Interpersonal Relationships, stood out with AUC values of 0.710 and 0.705, respectively, under logistic regression.</p><p><strong>Conclusion: </strong>MH had the strongest predictive association with clinical competence during formative internship training, beyond basic medical sciences. Integrating humanities merits greater prioritization in medical curricula to nurture skilled, compassionate physicians. Further research should investigate the longitudinal impacts of humanities engagement.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2444282"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143042157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Narrative comments in internal medicine clerkship evaluations: room to grow.","authors":"Christine Crumbley, Karen Szauter, Bernard Karnath, Lindsay Sonstein, L Maria Belalcazar, Sidra Qureshi","doi":"10.1080/10872981.2025.2471434","DOIUrl":"10.1080/10872981.2025.2471434","url":null,"abstract":"<p><p>The use of narrative comments in medical education poses a unique challenge: comments are intended to provide formative feedback to learners while also being used for summative grades. Given student and internal medicine (IM) grading committee concerns about narrative comment quality, we offered an interactive IM Grand Rounds (GR) session aimed at improving comment quality. We undertook this study to determine the quality of comments submitted by faculty and post-graduate trainees on students' IM Clerkship clinical assessments, and to explore the potential impact of our IM-GR. Archived comments from clerkship cohorts prior to and immediately following IM-GR were reviewed. Clinical clerkship assessment comments include three sections: Medical Student Performance Assessment (MSPE), Areas of Strength, and Areas for Improvement. We adapted a previously published comment assessment tool and identified the performance domain(s) discussed, inclusion of specific examples of student performance, evidence that the comment was based on direct observations, and, when applicable, the inclusion of actionable recommendations. Scoring was based on the number of domains represented and whether an example within that domain was provided (maximum score = 10). Analysis included descriptive statistics, t-test, and Pearson correlation coefficients. We scored 697 comments. Overall, section ratings were MSPE 2.51 (SD 1.52, range 0-9), Areas of Strength 1.53 (SD 1.09, range 0-6), and Areas for Improvement 1.27 (SD 1.06, range 0-8). Significant differences were noted after Grand Rounds only in the MSPE mean scores. Within domains, trends toward increased use of specific examples in the post-GR narratives were noted. Assessment of both the breadth and depth of the included comments revealed low-quality narratives offered by our faculty and resident instructors. A focused session on best practices in writing narratives offered minimal change in the overall narrative quality, although we did notice a trend toward the inclusion of explanative examples.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2471434"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How much time do internal medicine residents spend on self-directed learning and on which resources: a multi-center study.","authors":"Shreya P Trivedi, Anthony R Artino, Adam Rodman, R Logan Jones, Jafar Al-Mondhiry, Timothy Rowe, Tyler Larsen, Sarai Ambert-Pompey, Devesh Rai, Ahmed Ghoneem, Nicholas Gowen, Melina Manolas, Martin Fried, Shrunjal Trivedi, Kelly L Graham","doi":"10.1080/10872981.2025.2501259","DOIUrl":"10.1080/10872981.2025.2501259","url":null,"abstract":"<p><p>Increased clinical demands and newer means of self-directed learning (SDL) necessitate an understanding of how medical residents are supporting their learning. To examine the patterns of SDL engagement among internal medicine residents, their attitudes and behaviors with various resources, and evaluate the relationship between the clinical learning environment (CLE) and the time residents allocate to SDL and types of resources. This cross-sectional study used a systematic questionnaire informed by previous qualitative research on SDL among internal medicine residents. Internal medicine (IM) residents from 10 residency programs across the United States participated, providing a diverse representation of geographical and institutional contexts. Residents were asked to estimate weekly hours spent on SDL during their last clinical rotation, on which resources, and then to rank the usefulness of each resource. The survey also measured several variables, including attitudes and behaviors after using the resource they perceived to be the most useful, and the influence of training level, residency program type, clinical rotation, and number of hours worked clinically per week on reported time spent on SDL and types of resources. The response rate was 69.5% (783/1,126). Residents dedicated a mean of 18.2 (SD 18.6) hours per week (median of 10.5 hours per week) to SDL. Community-based programs reported more hours of SDL. There was no difference in hours spent on SDL based on the last clinical rotation, number of hours worked clinically, or PGY level. Senior residents favored digital resources, like podcasts, and were less likely to use traditional resources, like textbooks than interns. Our findings underscore the substantial time residents devote to SDL. In light of these results, educators and healthcare systems will need to work together to better support residents in optimizing the complex clinical learning environment.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2501259"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.","authors":"Mohi Eldin Magzoub, Imran Zafar, Fadi Munshi, Fouzia Shersad","doi":"10.1080/10872981.2025.2532682","DOIUrl":"10.1080/10872981.2025.2532682","url":null,"abstract":"<p><p>Generating high quality MCQs is time consuming and expensive. Many strategies are applied to produce high quality items including sharing of item banks, training of item writers and automatic item generation (AIG). Generative AI, when used with precision, has proven to reduce significantly both cost and time without compromising quality. Medical educators encounter numerous obstacles when using AI to generate MCQs of good quality. We searched the fast and recent growing medical education literature for articles related to the use of AI in generating high quality MCQs. Additionally, the development of these tips was guided by our own institutional experience. <b> </b>We created 10 tips for MCQ generation using AI to assist MCQ item writers in both undergraduate and graduate medical education.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2532682"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parental medical background and pre-admission preparedness in China's medical student selection.","authors":"Jin Yang, Hongbin Wu","doi":"10.1080/10872981.2025.2534048","DOIUrl":"10.1080/10872981.2025.2534048","url":null,"abstract":"<p><strong>Introduction: </strong>While children of medical professionals are globally overrepresented in medical schools, evidence from China remains limited. This study examines parental medical background prevalence among Chinese medical undergraduates, its association with admission outcomes, and disparities in pre-admission preparedness within China's meritocratic National College Entrance Examination (NCEE) system - a critical context given its role as the primary gateway to higher education.</p><p><strong>Methods: </strong>Using data from the 2021 China Medical Student Survey (CMSS), a nationally representative sample of 19,299 clinical medical students was analyzed. Linear and logistic regression models were employed to assess the relationship between parental medical background and admission outcomes/pre-admission preparedness, controlling for socio-demographic covariates (e.g. gender, urban/rural residency, family income) and institutional/provincial fixed effects.</p><p><strong>Results: </strong>Children of medical professionals were significantly overrepresented (11.60% vs. 0.34% national physician-population ratio). Parental medical background did not predict advantages in NCEE scores or admission to long-term programs. However, paternal medical background was associated with higher pre-admission preparedness in clinical practice (β = 0.199, <i>p</i> < 0.05), health and society (β = 0.205, <i>p</i> < 0.01), professionalism (β = 0.130, <i>p</i> < 0.05), and a greater likelihood of understanding the major (OR = 0.724, <i>p</i> < 0.01), while maternal background only correlated with understanding of the major (OR = 0.623, <i>p</i> < 0.01).</p><p><strong>Conclusions: </strong>In the context of China's NCEE-based student selection system, parental medical background has no direct influence on admission results, yet intergenerational disparities in preparedness persist. To foster substantive equity, China's meritocratic system could integrate targeted interventions (e.g. pre-med mentorship for disadvantaged students). These findings underscore the global imperative to balance meritocracy with policies addressing structural inequities in medical student selection.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2534048"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12265103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models in medical education: a comparative cross-platform evaluation in answering histological questions.","authors":"Volodymyr Mavrych, Einas M Yousef, Ahmed Yaqinuddin, Olena Bolgova","doi":"10.1080/10872981.2025.2534065","DOIUrl":"10.1080/10872981.2025.2534065","url":null,"abstract":"<p><p>Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, provides a unique domain for evaluating AI capabilities in medical education. To evaluate and compare the performance of five current LLMs: GPT-4.1, Claude 3.7 Sonnet, Gemini 2.0 Flash, Copilot, and DeepSeek R1 on correctly answering medical histology multiple choice questions (MCQs). This cross-sectional comparative study used 200 USMLE-style histology MCQs across 20 topics. Each LLM completed all the questions in three separate attempts. Performance metrics included accuracy rates, test-retest reliability (ICC), and topic-specific analysis. Statistical analysis employed ANOVA with post-hoc Tukey's tests and two-way mixed ANOVA for system-topic interactions. All LLMs achieved exceptionally high accuracy (Mean 91.1%, SD 7.2). Gemini performed best (92.0%), followed by Claude (91.5%), Copilot (91.0%), GPT-4 (90.8%), and DeepSeek (90.3%), with no significant differences between systems (<i>p</i> > 0.05). Claude showed the highest reliability (ICC = 0.931), followed by GPT-4 (ICC = 0.882). Complete accuracy and reproducibility (100%) were detected in Histological Methods, Blood and Hemopoiesis, and Circulatory System, while Muscle tissue (76.0%) and Lymphoid System (84.7%) presented the greatest challenges. LLMs demonstrate exceptional accuracy and reliability in answering histological MCQs, significantly outperforming other medical disciplines. Minimal inter-system variability suggests technological maturity, though topic-specific challenges and reliability concerns indicate the continued need for human expertise. These findings reflect rapid AI advancement and identify histology as particularly suitable for AI-assisted medical education.<b>Clinical trial number</b>: The clinical trial number is not pertinent to this study as it does not involve medicinal products or therapeutic interventions.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2534065"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12258195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144620851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interprofessional teaching rounds in medical education: improving clinical problem-solving ability and interprofessional collaboration skills.","authors":"Peiwen Yang, Ting Xiong, Xiyuan Dong, Shulin Yang, Jing Yue","doi":"10.1080/10872981.2025.2451269","DOIUrl":"10.1080/10872981.2025.2451269","url":null,"abstract":"<p><p>Interprofessional teaching rounds are a practical application of interprofessional education in bedside teaching, yet there is a lack of research on how interprofessional teaching rounds should be implemented into medical education. This study aimed to describe our experience in developing and implementing interprofessional teaching rounds during a clerkship rotation for medical students, and compares its strengths and weaknesses relative to traditional teaching rounds. Medical students were assigned to either the interprofessional teaching round group (<i>n</i> = 24) or the traditional teaching round group (<i>n</i> = 25), and each group participated in their assigned type of teaching round. A quiz including medical knowledge of gynecological and obstetric diseases was used to assess the students' diagnostic and treatment abilities after teaching rounds. Additionally, a survey was conducted among students to evaluate whether the interprofessional teaching rounds were helpful. The results showed that when using interprofessional teaching rounds, the test score for medical knowledge related to the diagnosis and treatment of gynecological and obstetric diseases was significantly higher than the traditional teaching round group (85.5 ± 11.2 vs 78.3 ± 12.5, <i>p</i> = 0.038). Additionally, the interprofessional teaching rounds significantly enhanced understanding of clinical application, identification, and appropriate problem-solving in cases, as well as examination of different disciplinary aspects of a case, and improvement of interdisciplinary collaboration skills compared to traditional teaching rounds. Our study demonstrates that interprofessional teaching rounds can serve as an effective teaching method for enhancing medical students' ability to collaborate interprofessionally and to solve clinical problems comprehensively.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2451269"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11749145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diversity, inclusion, and bias in Continuing Medical Education activities: lessons learned from participant evaluations.","authors":"Melissa D Bregger, Celia Laird O'Brien, Oluwateniola E Brown, Linda Suleiman, Sheryl A Corey, Clara J Schroedl","doi":"10.1080/10872981.2025.2525170","DOIUrl":"10.1080/10872981.2025.2525170","url":null,"abstract":"<p><strong>Purpose: </strong>Recommendations to ensure diverse, equitable, and inclusive content in Continuing Medical Education (CME) have been developed, however, learners' perception of these efforts are unknown. Learner recognition of biased or non-inclusive content and satisfaction with activity diversity provides insight into the success of bias mitigation efforts during CME planning and delivery. This study's objective was to evaluate the types of bias identified by learners, and to evaluate learners' perception of inclusivity and satisfaction with the diversity of CME activities.</p><p><strong>Study design: </strong>This study was a retrospective mixed methods analysis of post-activity evaluation comments from 210 CME activities and 5,284 evaluations at a large Accreditation Council for Continuing Medical Education (ACCME)-accredited academic healthcare system from September 1, 2022 to December 31, 2023.</p><p><strong>Results: </strong>Learners were satisfied with speaker and content diversity in 98.9% of activities. The qualitative analysis included 967 comments and demonstrated four main categories of perceived bias or lack of diversity identified by the CME activity learners: 1) Bias related to social identity factors, of which racial, ethnic, and gender bias were the most common forms identified by learners; 2) Lack of diversity in speakers, content and delivery; 3) Resistance to bias and inclusion evaluation questions; and 4) Commercial/industry bias. Further, some learners noted the instructional design of certain activities was not inclusive of all learners.</p><p><strong>Conclusion: </strong>These findings suggest that some CME activity learners perceive various forms of bias and lack of inclusivity and diversity despite efforts to review and mitigate bias in the planning and delivery of CME. While most CME activity learners were satisfied with speaker and content diversity, the data can inform more targeted efforts during the CME planning phase that focus on speaker and content diversity and screening for bias that goes beyond traditional industry/commercial bias.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2525170"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneity of professional goals among residents enrolled in a palliative care track: results of a national online survey in France.","authors":"François Chaumier, Denis Angoulvant, Emmanuel Gyan, Laurent Calvel","doi":"10.1080/10872981.2025.2520380","DOIUrl":"10.1080/10872981.2025.2520380","url":null,"abstract":"<p><strong>Background: </strong>Palliative care (PC) is recognized as a universal right, aimed at improving the quality of life for patients and their families facing life-threatening conditions. Training healthcare professionals, particularly physicians, is crucial for high-quality PC. Currently, France lacks a Palliative Medicine residency or fellowship (PMR-F), offering only a Palliative Care tracks (PCT) for voluntary postgraduate students. The aim of this study was to describe motivations and career plans of students enrolled in the PCT and to identify the proportion of those who would have preferred a PMR-F if it had been available.</p><p><strong>Methods: </strong>A national online survey was conducted between April and August 2024 among 128 students enrolled in PCT. A 12-item questionnaire, using a 10-point Likert scale, was designed and pilot-tested by PC educators and former students. The questionnaire was sent to identify their motivations and career plans.</p><p><strong>Results: </strong>The response rate was 76% (97/128). For 76% (74/97) of students, the purpose was to acquire skills complementary to their original specialty. While 48% (47/97) also aimed to gain skills for future specialist PC practice, only 10% (10/97) enrolled due to the lack of a specialized certificate. Career plans varied, with 30% intending to practice in their original discipline and 31% in PC facilities. Finally, 23% of students aiming to work in specialized PC facilities planned to continue their training with a continuing medical education program in PM (7/30).</p><p><strong>Discussion: </strong>The findings align with the official objectives of a track, emphasizing complementary skills acquisition. Our study reveals the coexistence of a variety of professional goals and projects within the same class of residents, which does not seem relevant for the same training program. It highlights the opportunity for setting up, in addition to PCT, a specialized PM curriculum in France, to better address specialized training needs for future PM experts.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2520380"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}