Medical Education Online最新文献

There is a growing trend to integrate scientific research training into undergraduate medical education, and mentoring styles are believed to help develop students' research capabilities. Critical thinking, attitude toward communication skills, and academic passion are important factors in enhancing students' research capabilities. This study aimed to explore the impact of exposure to scientific research and mentoring styles on medical undergraduates' critical thinking, attitude toward communication skills, and academic passion. This study surveyed 366 medical students from Central South China, assessing students' research involvement, mentoring style, self-perceived items of critical thinking, attitude toward communication skills, harmonious academic passion, and obsessive academic passion. Structural equation modeling (SEM) was used to model the structural relationships between the exposure to research and the self-perceived latent factors. SEM results showed that the model fit was acceptable (CFI = 0.903, RMSEA = 0.070, and SRMR = 0.060) based on the cutoff criteria used in empirical research. SEM analysis revealed that exposure to research significantly influenced critical thinking (β = 0.139, p < .01) and communication attitude (β = 0.258, p < .001) but did not affect academic passion. An inclusive mentoring style significantly enhanced critical thinking (β = 0.472, p < .001), communication attitude (β = 0.423, p < .001), and harmonious academic passion (β = 0.377, p < .001). Inclusive mentoring plays a crucial role in enhancing medical students' critical thinking, communication skills, and harmonious academic passion. Medical institutions should focus on strengthening mentoring programs to better support these outcomes.

The Nobel Prize is one of the most coveted awards in the world. The Nobel winners, also called Laureates, are invited to Stockholm to deliver their Nobel Lecture. Typically, this includes a historical and scientific overview of their discoveries, often enriched by anecdotes from their personal and professional lives. In this study, we explored more than two hundred Nobel Lectures to examine whether, how and whom newly crowned Nobel Prize winners explicitly mentioned as their mentors. We conducted an exploratory analysis of 208 Nobel Lectures in Physiology or Medicine delivered between 1901 and 2023 by using the search function to look for the keyword mentor. Only twenty Nobel Laureates have explicitly acknowledged their mentors in their Nobel Lectures. This recognition, which first occurred 73 years after the award's establishment, is more common among women, who are disproportionately underrepresented among awardees and often cited their postdoctoral advisors as mentors. The lack of overt recognition is surprising, especially considering the crucial role of mentorship in science, its broad societal value and the non-random patterns of Nobel mentoring relationships, where winners tend to have more Laureate ancestors, descendants and mentees. The word mentor appears more frequently in the Lasker Awards, although these were launched only in 1945 and feature brief 'Acceptance Remarks'. Even Oscar speeches, introduced nearly 30 years after the Nobel Prize and typically lasting less than a minute, mention mentors more frequently than Nobel Lectures do. This highlights an unexpected gap in the explicit acknowledgment of mentors in the context of the Nobel Prize, which we report and discuss for the first time.

Introduction: The number of medical schools in the United States (US) has rapidly increased over the past two decades, but it is unclear if these newer schools better address the needs of a diversifying population. We hypothesized that newer medical schools might be less encumbered by historical processes and power structures and, therefore, more successful in recruiting students more representative of the US population. This study assesses whether medical schools established since 2000 are advancing diversity compared to their predecessors.

Methods: Between October 1 and 14 December 2023, a cross-sectional study was conducted of all US allopathic and osteopathic medical schools that achieved accreditation and enrolled students by December 2023. School characteristics and matriculant demographics were collected from publicly available sources, including the 2022-2023 Medical School Admission Requirements website provided by the Association of American Medical Colleges and the American Association of Colleges of Osteopathic Medicine website. Descriptive statistics compared schools established before and after 2000.

Results: Sixty new medical schools were identified. Thirty-three (55%) are allopathic and 27 (45%) are osteopathic; 40 (66.7%) are private and 20 (33.3%) are public. Allopathic schools are primarily located in urban areas (21/33; 63.6%); osteopathic schools are in suburban areas (16/27; 59.3%). Mean annual tuition costs are $48,782.82 (standard error (SE) 2201.09) and $56,072.37 (SE: 2120.63) for in-state and out-of-state students, respectively. Out-of-state tuition, matriculant grade point average, and Medical College Admissions Test scores are significantly lower in newly established medical schools. More women entered medical school but the number of underrepresented students by race and ethnicity has not made substantial gains and continues to fail to represent the US population.

Conclusions: Geographic maldistribution, high tuition, and lack of student body diversity persist in newly accredited medical schools. Newly established medical schools are perpetuating many existing obstacles to diversifying the US physician workforce.

Learning self-efficacy (SE) assesses how learners understand and evaluate their ability to polish their learning process. Learning clinical medicine requires prolonged training, traditionally premised on longitudinal immersion in patient care. Such a process is domain-specific, whereas learning SE for clinical education remains under-explored. Unidimensional assessment is insufficient for capturing the inherent capabilities upon which well-trained physicians provide care. We aimed to establish a multi-dimensional learning SE questionnaire for clinical education among undergraduate medical students, evaluating the structure validity, followed by assessing the dimensionality of different models. Medical students of 2^nd to 4^th grades from Taiwan in 2022-2023 completed a multi-dimensional medical learning SE (MLSE) questionnaire, including four factors for basic science learning (conceptual understanding (CU), higher-order cognitive skills (HC), practical work (PW), and everyday application (EA)), and three for clinical mastery performance (medical communication (MC), evidence-based medicine (EBM), and Professionalism)). We tested factors' intercorrelation, used exploratory and confirmatory factor analysis (EFA/CFA) for structure and validity assessment, and compared the fitness and dimensionality between models. Twenty-four items grouped into seven independent factors (3, 3, 4, 3, 5, 3, and 3 items in CU, HC, PW, EA, MC, EBM, and Professionalism, respectively) were established and finalized, with sufficient fitness, good convergent and construct validities. All MLSE factors significantly correlated (0.49-0.87; p < 0.001), demonstrating good convergent and discriminant validity. We established six models (first-order uncorrelated or correlated construct, one to three second-order dimensions ('basic medical SE', 'clinical medical SE', 'Cognition', or 'Application' of different structures), and a final model 7 containing four second-order dimensions (Cognition, Application, MC, and clinical medical SE) exhibiting adequate model fitness and measured learning SE satisfactorily. Our MLSE model structure disclosed vital SE factors with intercorrelations associated with medical students' learning processes during clinical education. Polishing these dimensions may help promote their learning SE.

With ongoing climate change and other major human-induced changes to the biosphere, there is a greater need to improve future healthcare providers' environmental health (EH) literacy. As of 2022, 45% of U.S. MD programs lacked a required EH curriculum. A self-assembled group of four medical students conceptualized and planned this pilot study to characterize matriculating medical students' EH knowledge and attitudes. This group also developed EH content for integration into a preexisting 18-month Case-Based Learning (CBL) curriculum to enhance small-group discussion and learning. Matriculating medical students were invited to participate in an anonymous cross-sectional survey assessing EH literacy and the need for an EH-specific curriculum in August 2023. Concurrently, the student group analyzed 44 cases in the current CBL curriculum and searched PubMed and the PEHSU Climate Resources for Health Education for pertinent topics from the case review. The group then formulated learning objectives and discussion questions for the facilitator guide for 30 cases, with expert review by curriculum faculty members. 70 of 200 students (35%) fully completed a survey about EH literacy. Eighty percent of students reported no previous coursework pertinent to EH, with most students demonstrating a basic understanding of the concept. Students reported low confidence in counseling patients regarding pertinent EH matters and a limited understanding of social determinants of health pertinent to the local area. In 30 identified medical conditions across 10 disciplines, 57 new objectives were developed to address environmental exposures, infectious diseases, climate change, and local implications. Increasing EH literacy among medical students represents a high-impact educational need. This pilot study, conceived and led by medical students, successfully characterized the EH knowledge gap among medical students and integrated novel discipline-specific learning objectives and discussion points into a pre-existing CBL curriculum. This model may easily be adapted to other institutions' curricula.

We surveyed faculty and students at a large urban medical school to assess their awareness, usage patterns, and perceived barriers to AI adoption, aiming to identify opportunities for meaningful integration of AI into medical education. We developed a custom survey and distributed it to all medical students (Years 1-4) and a selected group of faculty involved in the MD curriculum. We used descriptive statistics to analyze quantitative data and conducted content analysis on open-ended responses. A total of 128 faculty and 138 students completed the survey. Most participants self-identified as novice AI users and reported limited awareness and infrequent use of AI tools for professional or academic tasks. They cited lack of knowledge, limited time, and unclear benefits as key barriers. Both groups called for training, ethical guidance, and institutional support to facilitate AI integration into medical education. Faculty and students expressed similar needs for targeted AI education, though they emphasized different aspects. In response, our school has conducted a faculty training session and has accelerated identifying opportunities to integrate AI into the curriculum.

The assessment of short-answer questions (SAQs) in medical education is resource-intensive, requiring significant expert time. Large Language Models (LLMs) offer potential for automating this process, but their efficacy in specialized medical education assessment remains understudied. To evaluate the capability of five LLMs to grade medical SAQs compared to expert human graders across four distinct medical disciplines. This study analyzed 804 student responses across anatomy, histology, embryology, and physiology. Three faculty members graded all responses. Five LLMs (GPT-4.1, Gemini, Claude, Copilot, DeepSeek) evaluated responses twice: first using their learned representations to generate their own grading criteria (A1), then using expert-provided rubrics (A2). Agreement was measured using Cohen's Kappa and Intraclass Correlation Coefficient (ICC). Expert-expert agreement was substantial across all questions (average Kappa: 0.69, ICC: 0.86), ranging from moderate (SAQ2: 0.57) to almost perfect (SAQ4: 0.87). LLM performance varied dramatically by question type and model. The highest expert-LLM agreement was observed for Claude on SAQ3 (Kappa: 0.61) and DeepSeek on SAQ2 (Kappa: 0.53). Providing expert criteria had inconsistent effects, significantly improving some model-question combinations while decreasing others. No single LLM consistently outperformed others across all domains. LLM strictness in grading unsatisfactory responses varied substantially from experts. LLMs demonstrated domain-specific variations in grading capabilities. The provision of expert criteria did not consistently improve performance. While LLMs show promise for supporting medical education assessment, their implementation requires domain-specific considerations and continued human oversight.

In the current medical environment, Chinese medical students have fewer and fewer opportunities for practice at the internship stage, which leads to a serious lack of clinical skills to meet the demands of the real world of work. In this study, we used a practical training scenario simulation teaching method oriented to strengthen clinical skills, and systematically compared the teaching effectiveness with that of traditional internship teaching models. This study included 40 5-year clinical medicine interns who practiced in the Department of Neurology at our hospital. They were randomly divided into a control group and an observation group, with 20 participants in each group. The control group adopted the bedside teaching, and the observation group adopted the practical training scenario simulation teaching method. Students in both groups were assessed using Mini-CEX scores on comprehensive qualities related to clinical skills, including physical examination of the nervous system, humanistic care, communication skills, organizational skills, etc And then, their learning outcomes are assessed in the form of after-department examination. After the internship, all the scores of teaching effect of students in the observation group were significantly higher than those of the control group, especially in terms of doctor-patient communication, physical examination of the nervous system, localization and nature determination, organizational skills, and overall performance (P < 0.05). The results of students' exit exams, students' and teachers' teaching satisfaction of the observation group were significantly better than those of the control group (P < 0.05). This method has an obvious promotion effect on the improvement of medical students' clinical skills. The method significantly improves the performance of medical students during the internship stage, and results in higher teaching satisfaction for teachers and students.

The lack of physicians in rural areas is a universal problem. To increase the attractiveness of rural practice for medical students, the contribution of medical schools is undisputed. However, much of the evidence on interventions before and during undergraduate education comes from countries with large areas and low population density like Australia and Canada. In Germany, selective admission to medical studies for students who agree to become rural general practitioners is still a new concept. The aim of this study was to assess the sociodemographic characteristics, attitudes and career aspirations of the rural doctor quota students from one medical school in Germany compared to their non-quota counterparts. For this cross-sectional study, a paper-based anonymous questionnaire was distributed to all first year medical students at Leipzig University in two consecutive study years.Descriptive analyses and group differences were calculated using SPSS. The response rate was 87.3% with n = 604 completed questionnaires and 40 (6.6%) students self-classified as rural doctor quota students. Quota students grew up in rural areas significantly more often than their counterparts and had more working experience in the medical field. General practice was the preferred career option for 64.1% (25/39, versus 2.7% [15/549] of non-quota students). Working self-employed in one's own medical practice was the preferred option for 71.1% (27/38) of quota students (vs. 28.0% [153/546] of non-quota students). Quota students valued a broad spectrum of patients, a long-term doctor-patient relationship, employee management and prestige more highly than their fellow students. Students from the rural doctor quota largely exhibit characteristics and attitudes that are compatible with future rural practice, despite showing little differences in sociodemographic items such as age and marital status. Not all students agree with the program objective. To demonstrate an impact on the health services, longitudinal data is necessary to monitor career choices over time.

Large language models (LLMs) such as ChatGPT and Gemini are increasingly used to generate educational content in medical education, including multiple-choice questions (MCQs), but their effectiveness compared to expert-written questions remains underexplored, particularly in anatomy. We conducted a cross-sectional, mixed-methods study involving Year 2-4 medical students at Qatar University, where participants completed and evaluated three anonymized MCQ sets-authored by ChatGPT, Google-Gemini, and a clinical anatomist-across 17 quality criteria. Descriptive and chi-square analyses were performed, and optional feedback was reviewed thematically. Among 48 participants, most rated the three MCQ sources as equally effective, although ChatGPT was more often preferred for helping students identify and confront their knowledge gaps through challenging distractors and diagnostic insight, while expert-written questions were rated highest for deeper analytical thinking. A significant variation in preferences was observed across sources (χ² (64) = 688.79, p < .001). Qualitative feedback emphasized the need for better difficulty calibration and clearer distractors in some AI-generated items. Overall, LLM-generated anatomy MCQs can closely match expert-authored ones in learner-perceived value and may support deeper engagement, but expert review remains critical to ensure clarity and alignment with curricular goals. A hybrid AI-human workflow may provide a promising path for scalable, high-quality assessment design in medical education.