Medical Education Online最新文献

This study aimed to evaluate the impact of a teaching model combining massive open online course (MOOC) with virtual simulation experiments on clinical skills training. A quasi-experimental design was employed in the Second Clinical College of Wuhan University. The control group received traditional face-to-face teaching, while the experimental group received a teaching model combining MOOC instruction with virtual simulation experiments. After completing the course, both groups participated in a theoretical test and a Structured Clinical Skills Assessment to assess their mastery of clinical skills. Additionally, a questionnaire survey was conducted to assess the effectiveness of the experimental teaching model and to gauge student satisfaction. The experimental group's theoretical test scores were significantly higher than those of the control group, and the experimental group's Structured Clinical Skills Assessment scores were significantly higher than those of the control group, with statistically significant differences (p < 0.05). The post-course questionnaire survey of the experimental group showed that most students were satisfied with the teaching model. In the general surgery experiment course, the teaching model combining MOOC instruction with virtual simulation experiments is an effective teaching method that improves surgical skill performance and increases student satisfaction.

Introduction: Access to mentorship is frequently cited as a priority for doctors, however formal mentorship programmes throughout training are lacking for residents. Near-peer mentorship is a faculty-light option to potentially bridge this mentorship gap, however the literature in the clinical postgraduate setting is not comprehensive. We aim to evaluate the benefits and pitfalls of near-peer mentorship in a postgraduate setting.

Materials and methods: This study conducted a narrative review based on previously published frameworks. An exhaustive search of the literature was conducted using PubMed and OVID Medline. The findings were analysed using Framework synthesis, in which the pre-existing framework of benefits and pitfalls of near-peer mentorship were applied. After allocation to the frameworks' overarching themes, data was analysed thematically.

Results: Across 10 identified studies (one quantitative, three qualitative, six mixed-methods), near-peer mentorship was perceived to be beneficial (72-100% approval), with 85-99% of mentors and mentees desiring continuation of schemes at their units (seven UK-based, two Australian, one Canadian). The main themes identified were Mentee benefits, including careers advice and development of transferable skills; Mentor benefits, including leadership and organisational skill improvements; Organisational benefits, including reduced faculty workload and an enhanced sense of community. Pitfalls included a perceived lack of mentor expertise, shortage of time and resources, and unsupportive mentoring relationships.

Discussion: This research suggests that near-peer mentorship offers benefits for the mentee, mentor and organisation, if care is taken to mitigate the potential pitfalls. The main benefits versus traditional senior faculty mentorship derive from the concept of social and cognitive congruence, whereby a more closely relatable tutor is better able to tailor learning in terms understood by the tutee. Practical recommendations to optimise near-pear mentorship include mentorship pyramids, matching of pairings within the same clinical sites, and a hybrid matching approach with the option for mentee-led selection.

The late submission of assignments by medical students is a chronic problem. Students' characteristics and past academic performances are useful indicators for predicting delays during medical school. However, medical students' perspectives on factors that lead to delays in assignment submission remain unexplored. To address this issue, this study examines the factors that influence the late submission of assignments in a medical university setting from the perspective of medical students. This exploratory qualitative case study recruited 20 undergraduate medical students who had submitted their assignments late at a private Japanese medical university. Data were collected through face-to-face, semi-structured interviews and analyzed using thematic analysis from a constructivist paradigm. A qualitative analysis revealed the following three themes regarding the factors influencing the late submission of assignments: (1) factors related to the medical university curriculum, including its structure, course offerings, and academic requirements, (2) factors related to the instructions from and authority of faculty, and (3) situational factors that exacerbate delays in student assignment submissions. Medical students felt that the nature of the assignments, which were assigned with no apparent regard for student schedules and offered a limited evaluative metric, was often irrational. The students implicitly accepted this situation while being aware of the infeasible submission deadlines. Medical students may have an internal sense of the perceived irrationality of faculty-imposed assignments and the authority behind them, and may strategically neglect some in favor of efficient course completion. Faculty members should ensure their assignments are consistent with course contents.

The decision to trust a learner to actively participate in care is a fundamental part of clinical training. However, concerns about patient safety mean that clinical practice often presents newly qualified doctors with situations they are unable to manage independently. 'Entrustable professional activities' (EPAs) have been proposed as a possible solution to this unpreparedness. Understanding how undergraduate GP teachers make entrustment decisions, which is a gap in the existing literature, may be an appropriate first step towards understanding whether EPAs can be applied to undergraduate primary care. To explore teachers' experiences of entrustment decision-making in an undergraduate primary care context. This is an interpretive phenomenological study. Data were collected through semi-structured interviews with final year GP teachers. Interviews were audio recorded, transcribed and subjected to thematic analysis. Five GP teachers were interviewed. Five themes and twenty-four subthemes were produced. These captured participants' experiences of entrustment decision-making, as well as the factors influencing these decisions. The teacher's personal approach to risk was a key consideration. The experience of GP teachers is also shaped by their context and challenges such as managing complexity and uncertainty that are inherent to the GP role. Entrustment decision-making is complex and is experienced individually by different teachers, resulting in a range of approaches to supervision. Despite these differences there are various common factors that impact the decision of whether to entrust. This study allows several recommendations to be made that may enable us to move towards entrustment of undergraduate students in a primary care context.

Large language models (LLMs) are increasingly used in healthcare and medical education, but their performance on institution-authored multiple-choice questions (MCQs), particularly with negative marking, remains unclear. To compare the examination performance of five contemporary LLMs with enrolled medical students on final multiple-choice (MCQ-style) course exams across four clinical courses. We conducted a comparative cross-sectional study at Miguel Hernández University (Spain) in 2025. Final exams in Infectious Diseases, Neurology, Respiratory Medicine, and Cardiovascular Medicine were administered under routine conditions in Spanish. Five LLMs (OpenAI o1, GPT-4o, DeepSeek R1, Microsoft Copilot, and Google Gemini 1.5 Flash) completed all MCQs in two independent runs. Scores were averaged and test-retest was estimated with Gwet's AC1. Student scores (n = 442) were summarized as mean ± SD or median (IQR). Pairwise differences between models were explored with McNemar's test; student-LLM contrasts were descriptive. Across courses, LLMs consistently exceeded the student median and, in several instances, the highest student score. Mean LLM courses scores ranged 7.46-9.88, versus student means 4.28-7.32. OpenAI o1 achieved the highest mean in three courses; Copilot led in Cardiovascular Medicine (text-only subset due to image limitations). All LLMs answered every MCQ and short term test-retest agreement was high (AC1 0.79-1.00). Aggregated across courses, LLMs averaged 8.75 compared with 5.76 for students. On department-set Spanish MCQ exams with negative marking, LLMs outperformed enrolled medical students, answered every item, and showed high short-term reproducibility. These findings support cautious, faculty-supervised use of LLMs as adjuncts to MCQ assessment (e.g. automated pretesting, feedback). Confirmation across institutions, languages, and image-rich formats, and evaluation of educational impact beyond accuracy are needed.

The rapid evolution of artificial intelligence (AI) and its growing role in clinical settings have made AI education a priority in undergraduate medical education. To support this, AI curricula must align with existing medical education frameworks while addressing AI's distinctive characteristics. This article outlines twelve actionable tips to guide the development and implementation of such curricula. These include defining the purpose and scope of AI education within the broader context of existing competency frameworks and digital health. The curriculum should be structured to allow for progressive deepening and integration of content, prioritizing key elements. Additionally, sustainable AI education depends on securing institutional resources, providing learners with authentic experiences, and ensuring continuous evaluation and improvement of the curriculum. Together, these approaches aim to help medical schools prepare students to practice effectively in a future where AI is a core component of medical practice.

In this study, we conducted a descriptive study to evaluate the quality of KFQs generated by OpenAI's o3 model. We developed a reusable generic prompt for KFQ generation, designed in alignment with the Medical Council of Canada's KFQ development guidelines. We also created an evaluation metric to systematically assess the quality of the KFQs based on the KFQ development guideline. Twenty unique cardiology-focused KFQs were created using recent European Society of Cardiology guidelines as reference. Each KFQ was independently assessed by two cardiology experts using the quality checklist, with disagreements resolved by a third reviewer. Descriptive statistics were used to summarize checklist compliance and final acceptability ratings. Of the 20 KFQs, 3 (15%) were rated 'Accept as is' and 17 (85%) 'Accept with minor revisions'; none required major revisions or were rejected. The overall compliance rate across checklist criteria was 93.7%, with perfect scores in domains such as key feature definition, scenario plausibility, and alignment between questions and scenarios. Lower performance was observed for inclusion of genuinely harmful 'killer' responses (50%), plausibility of distractors (77.8%), and active language use in phrasing the question (80%). The findings showed that an LLM, guided by a structured prompt, can generate KFQs that closely adhere to established quality standards, with most requiring only minor refinements. While expert review remains essential to ensure clinical accuracy and patient safety, AI-assisted workflows have strong potential to streamline KFQ development and enhance the scalability of CDM assessment in medical education.

The overturning of Roe v. Wade in Dobbs v. Jackson Women's Health Organization (2022) has led to varying abortion laws across states in the U.S., potentially influencing medical education choices. While some research has examined abortion policy changes and medical residencies and fellowship decisions, none has investigated abortion policy and medical school decisions. This study aims to determine whether pre-medical students' personal views on abortion influence their willingness to attend medical schools in states where abortion is illegal at all stages EXCEPT to save the life of the mother. A cross-sectional survey was distributed to pre-medical organizations at the two largest four-year institutions in each U.S. state and the District of Columbia, except Wyoming, which has only one four-year institution. The survey collected demographic data, political affiliation, intended medical specialty, and personal stance on abortion. Participants indicated their willingness to attend medical school in states with different types of abortion policies. There were 182 completed surveys from participants in 20 different states. Analysis showed that students who believed abortion is acceptable and should be legal at all stages were significantly less willing to attend medical school in states where abortion is illegal EXCEPT to save the life of the mother. Abortion policy may influence medical school decisions among pre-medical students, which may have long-term implications for physician distribution, particularly in states with restrictive abortion laws. Future research should explore how these trends impact healthcare workforce shortages and access to reproductive care.

Despite the use of structured competency frameworks and supporting educational tools, medical residents often continue to struggle with personalising their learning, managing feedback, and balancing professional demands with personal well-being. These persistent challenges suggest that conventional support systems may not fully address the nuanced needs of residents, contributing to feelings of disconnection and stress during training. The Resident Talks Podcast was developed to provide residents with accessible insights and practical tools to support their personal and professional development, particularly in areas such as work-life balance, stress management, and feedback integration. The podcast was developed in the Netherlands by a research group studying EPA-based residency training. It was inspired by insights from their qualitative studies and shaped in collaboration with residents. Social media engagement ensured the content remained relevant to their evolving needs. Topics ranged from professional identity, feedback culture and mental health to more sensitive or underrepresented issues such as pregnancy, diversity and addiction. Each 30-minute episode was distributed across platforms like Spotify and Apple Podcasts to maximise accessibility. To date, the podcast has released 21 episodes and garnered over 14,700 downloads. Feedback highlights its practical value and relevance in addressing issues that are not always openly discussed in formal training. The Resident Talks Podcast demonstrates the potential of innovative media to supplement resident support systems, addressing personal and professional challenges in a flexible and accessible way. Further research is needed to evaluate whether and how this format provides meaningful support in daily residency practice.

Since the establishment of the Flexnerian model of medical education, single degree (MD) physician-scientists have significantly advanced biomedical research and clinical medicine. However, institutional emphasis has predominantly favoured dual-degree MD/PhD Medical Scientist Training Programs (MSTPs). Recognizing that many successful academic physician-scientists do not hold PhDs, there remains a critical need for structured research training and mentorship targeting single degree students. The University of California Davis School of Medicine developed the Academic Research Careers for Medical Doctors (ARC-MD) program as part of a broader institutional strategy to accelerate innovation, aligning closely with an institutional framework emphasizing thematic breadth and reciprocal interactions between basic and clinical departments. ARC-MD strategically integrates a five-year research-intensive pathway into the traditional four-year MD curriculum, annually enrolling 4-8 students. The program includes an introductory pre-matriculation course, a longitudinal curriculum spanning medical school, focused mentorship fostering interdisciplinary collaboration, a dedicated research year emphasizing reciprocal exchanges between clinical and basic science research, professional identity formation, and financial support through tuition scholarships and stipends. Since its inception in 2019, ARC-MD has enrolled 41 students and graduated two cohorts. Program evaluation surveys were administered in 2024 and 2025 across all five years of training. Student-reported research self-efficacy, measured using the Clinical Research Appraisal Inventory, was lowest among newly matriculating students (pre-program) and increased in subsequent years. Student-reported identity as physician-scientists and student assessments of mentors also showed overall improvement across training levels, though trajectories were not strictly linear.