JMIR Medical Education最新文献

{"title":"Shaping the Future of Digital Health Education in Canada: Prioritizing Competencies for Health Care Professionals Using the Quintuple Aim.","authors":"Glynda Rees, Lorelli Nowell, Tracie Risling","doi":"10.2196/75904","DOIUrl":"10.2196/75904","url":null,"abstract":"<p><strong>Unlabelled: </strong>The integration of digital health and informatics competencies into health care education in Canada is essential for preparing a workforce capable of leveraging health care technologies to enhance care delivery and patient outcomes. Despite significant advancements, the current educational landscape in digital health remains inconsistent, characterized by fragmented curricula and uneven competency attainment. Addressing these gaps requires an innovative reframing of digital health competencies guided by a robust, outcomes-oriented framework. These authors propose the Quintuple Aim as an effective framework for outlining and organizing digital health and informatics competencies, focusing simultaneously on improving patient experience, enhancing population health, reducing health care costs, improving health care provider experience, and advancing health equity. Each dimension of the Quintuple Aim provides a critical lens for identifying, prioritizing, and contextualizing core competencies. Within the \"patient experience\" aim, competencies prioritize patient-centered technology use, including digital literacy, privacy awareness, and the ability to empower patients through technology. \"Healthcare provider experience\" competencies prioritize usability, workflow integration, and strategies to mitigate technology-related burnout. Under \"population health,\" competencies emphasize data-driven decision-making, analytics, and health informatics to support effective public health interventions. Competencies associated with \"cost reduction\" focus on operational efficiency, resource optimization, and economic evaluation of digital health solutions. Finally, \"health equity\" competencies emphasize inclusivity, cultural safety, and the elimination of digital divides, ensuring equitable access to digital health technologies. Potential assessment strategies aligned with each competency area are highlighted, emphasizing formative and summative evaluations that include simulation-based assessments, real-world technology integration projects, and reflective practice portfolios. By applying the Quintuple Aim as a guiding structure, digital health education can achieve greater standardization, clarity, and alignment with health care system needs, while simultaneously allowing for tailored adaptations responsive to specific regional and institutional priorities. This paper introduces the Quintuple Aim as a guiding framework to comprehensively identify and organize core digital health and informatics competencies for health professional education.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e75904"},"PeriodicalIF":3.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Learning Styles Characterize Medical Students, Surgical Residents, Medical Staff, and General Surgery Teachers While Learning Surgery: Scoping Review.","authors":"Gabriela Gouvea Silva, Marco Antonio Ribeiro Filho, Carlos Dario da Silva Costa, Stela Regina Pedroso Vilela Torres de Carvalho, Joao Daniel de Souza Menezes, Matheus Querino da Silva, William Donega Martinez, Bruno Cardoso Goncalves, Natália Almeida de Arnaldo Silva Rodriguez Castro, Luiz Vianney Cidrão Nunes, Emerson Roberto Santos, Helena Landim Gonçalves Cristóvão, Alexandre Lins Werneck, Alex Bertolazzo Quitério, Sonia Maria Maciel Lopes, Denise Vaz-Oliani, Fernando Facio, Patrícia da Silva Fucuta, Alba Regina de Abreu Lima, Vania M S Brienze, Heloisa Cristina Caldas, Julio Cesar Andre","doi":"10.2196/66766","DOIUrl":"10.2196/66766","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Learning style is a biologically and developmentally imposed configuration of personal characteristics that makes the same teaching method effective for some and ineffective for others. Studies support a relationship between learning style and career choice, resulting in learning style patterns observed in distinct types of residency programs, which can also be applied to general surgery, from medical school to the latest stages of training. The methodologies, populations, and contexts of the few studies pertinent to the matter are very different from one another, and a scoping review on this theme will unequivocally enhance and organize what is already known.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The goal of this study is to identify and map out data from studies that report on learning styles in medical students, surgical residents, medical staff, and surgical teachers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The search strategy was performed on September 25, 2023, by a librarian and digital search strategy expert, through the descriptors \"learning, style\" and \"surgery.\" The databases consulted were Embase, SCOPUS, Web of Science, and PubMed through descriptors and their synonyms, according to MeSH (Medical Subject Headings). Of the 213 articles found, 135 articles remained after the exclusion of duplicates. The remaining 78 articles were analyzed by 3 of the researchers independently. A total of 27 articles were selected, and 2 articles were excluded because the full article was not found.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 25 articles were included in the review. A total of 96% (n=24) of the articles used cognitive theories as their theoretical basis. Regarding learning style instruments, 36% (n=9) articles used the visual, aural, read, and kinesthetic learning method instrument, and 40% (n=10) articles chose Kolb's learning style inventory. The papers concentrate especially on the 2010s, and most of them are from North America (16/25, 64%) or Europe (6/25, 24%). The smallest study had 15 participants and the biggest had 1549 participants. The included studies primarily focused on surgical residents (21/25, 84%), with fewer targeting faculty and staff (9/25, 36%). The primary objectives of the studies were to investigate the relationship between learning styles and performance (15/25, 60%), gender differences (7/25, 28%), changes over time (4/25, 16%), and motivation (3/25, 12%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This scoping review reveals a limited and geographically concentrated body of research on learning styles in surgery education, primarily focusing on surgical residents and using Kolb's learning style inventory and visual, aural, read, and kinesthetic learning method instruments. Considerable gaps exist regarding geographical diversity and the study of medical staff and faculty. These findings underscore the need for future research with a broader scope to better inform educational strategies in surgery.","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e66766"},"PeriodicalIF":3.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Clinical Clerkship Mentor Using Generative AI and Evaluation of Its Effectiveness in a Medical Student Trial Compared to Student Mentors: 2-Part Comparative Study.","authors":"Hayato Ebihara, Hajime Kasai, Ikuo Shimizu, Kiyoshi Shikino, Hiroshi Tajima, Yasuhiko Kimura, Shoichi Ito","doi":"10.2196/76702","DOIUrl":"10.2196/76702","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;At the beginning of their clinical clerkships (CCs), medical students face multiple challenges related to acquiring clinical and communication skills, building professional relationships, and managing psychological stress. While mentoring and structured feedback are known to provide critical support, existing systems may not offer sufficient and timely guidance owing to the faculty's limited availability. Generative artificial intelligence, particularly large language models, offers new opportunities to support medical education by providing context-sensitive responses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop a generative artificial intelligence CC mentor (AI-CCM) based on ChatGPT and evaluate its effectiveness in supporting medical students' clinical learning, addressing their concerns, and supplementing human mentoring. The secondary objective was to compare AI-CCM's educational value with responses from senior student mentors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted 2 studies. In study 1, we created 5 scenarios based on challenges that students commonly encountered during CCs. For each scenario, 5 senior student mentors and AI-CCM generated written advice. Five medical education experts evaluated these responses using a rubric to assess accuracy, practical utility, educational appropriateness (5-point Likert scale), and safety (binary scale). In study 2, a total of 17 fourth-year medical students used AI-CCM for 1 week during their CCs and completed a questionnaire evaluating its usefulness, clarity, emotional support, and impact on communication and learning (5-point Likert scale) informed by the technology acceptance model.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;All results indicated that AI-CCM achieved higher mean scores than senior student mentors. AI-CCM responses were rated higher in educational appropriateness (4.2, SD 0.7 vs 3.8, SD 1.0; P=.001). No significant differences with senior student mentors were observed in accuracy (4.4, SD 0.7 vs 4.2, SD 0.9; P=.11) or practical utility (4.1, SD 0.7 vs 4.0, SD 0.9; P=.35). No safety concerns were identified in AI-CCM responses, whereas 2 concerns were noted in student mentors' responses. Scenario-specific analysis revealed that AI-CCM performed substantially better in emotional and psychological stress scenarios. In the student trial, AI-CCM was rated as moderately useful (mean usefulness score 3.9, SD 1.1), with positive evaluations for clarity (4.0, SD 0.9) and emotional support (3.8, SD 1.1). However, aspects related to feedback guidance (2.9, SD 0.9) and anxiety reduction (3.2, SD 1.0) received more neutral ratings. Students primarily consulted AI-CCM regarding learning workload and communication difficulties; few students used it to address emotional stress-related issues.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AI-CCM has the potential to serve as a supplementary educational partner during CCs, offering comparable support to that of sen","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e76702"},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing and Improving Study Skills Support in Medical Education Through a Student-Staff Partnership: Mixed Methods Approach.","authors":"Nicole Tay, Anaïs Deere, Dhivya Ilangovan, Carys F E Phillips, Emma Kelley","doi":"10.2196/65053","DOIUrl":"10.2196/65053","url":null,"abstract":"<p><strong>Background: </strong>The necessity for self-regulated, lifelong learners in the rapidly evolving field of medicine underscores the importance of effective study skills. Efforts to support students with these skills have had positive outcomes but are often limited in scope and accessibility, with a tendency to target groups facing immediate challenges.</p><p><strong>Objective: </strong>This study aimed to explore the student perspective on study skills support at University College London Medical School through a student-staff partnership, with the goal of guiding future improvements.</p><p><strong>Methods: </strong>A mixed methods approach was adopted using an anonymous questionnaire and focus groups. After analyzing questionnaire responses using descriptive statistics to refine focus group questions, focus groups were conducted to delve deeper into identified issues. Transcripts were analyzed thematically using inductive coding.</p><p><strong>Results: </strong>In total, 116 students completed the questionnaire in full and 6 students participated in 2 focus groups. The questionnaire revealed that 68% (68/100) of respondents felt that they never received study skills support at University College London Medical School. Preferred methods of support included small group sessions (56/100, 56%) and topics like examination preparation (83/100, 83%) and study skills specific to medicine (72/100, 72%). Focus group themes were the lack of current study skills support, delivery of study skills support, specific study skills for medical school, personalized approach to support needed, and accessing support. Findings informed the co-creation of study skills resources.</p><p><strong>Conclusions: </strong>Overall, the findings highlight the need for strategically incorporating study skills support at medical school, emphasizing early and consistent promotion and tailored delivery methods.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e65053"},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12408056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Gender Perspectives in Medical Education: Latent Semantic Analysis of Israeli First-Year Medical Students' Reflections.","authors":"Rola Khamisy-Farah, Raymond Farah, Haneen Jabaly-Habib, Yara Nakhleh Francis, Nicola Luigi Bragazzi","doi":"10.2196/78371","DOIUrl":"10.2196/78371","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Gender is increasingly recognized as a crucial determinant of health and health care delivery. Integrating gender-sensitive content into medical education is essential for cultivating socially responsive, culturally competent, and clinically effective physicians of the future. However, limited research has examined how medical students conceptualize gender in clinical contexts, particularly through their own reflective narratives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study explores the thematic landscape of gender-related perceptions among first-year medical students in Israel following a mandatory course in gender medicine. Using latent semantic analysis (LSA), we examined how students reflected on gendered dimensions of health care and how these reflections varied by gender and ethnicity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;First-year medical students enrolled in the four-year path of medicine in Israel participated in a compulsory gender medicine course and were invited to submit anonymous written reflections. A total of 83 students (n=52, 63%, females; n=31, 37%, males; n=68, 82%, Jewish; and n=15, 18%, Arab) submitted responses, which were preprocessed and analyzed using LSA. The texts were lemmatized and vectorized to construct a term-document matrix, followed by singular value decomposition for dimensionality reduction. Ten latent topics were extracted, and thematic labels were assigned through an inductive, consensus-based coding procedure. Subgroup analyses were conducted by gender and ethnicity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;LSA identified 10 distinct topics, accounting for 56.6% of the total variance in the overall sample. The most dominant theme was Gendered Patient-Doctor Interactions (eigenvalue=121.188; 28.1% variance; 527 terms; 75 documents), followed, in terms of variance, by Gender-Specific Diseases and Health Concerns (5.7%) and Cultural and Religious Influences on Health Care (4.3%). Reflections from female students introduced 3 unique themes: Gendered Help-Seeking and Familial Roles (2.8%), Gender and Health Education (2.5%), and Gendered Communication and Advocacy (2.2%). Male students uniquely discussed Perceived Gender Bias in Clinical and Research Settings (3.8%) and the Legal and Ethical Dimensions of Reproductive Health Care (3.3%). Among Jewish students, additional themes included Population-Level Framing of Gendered Conditions (3.7%) and Gendered Youth Expectations (2.1%). Arabic students contributed culturally specific themes, such as Modesty and Cultural Norms (8.6%), Paternal Authority and Structural Discrimination (6.3%), and Reproductive Vulnerability (3.6%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Thematic patterns in student reflections suggest that gender medicine curricula are effective in fostering critical engagement with diverse gendered realities in clinical care. The emergence of culturally grounded and gender-specific themes underscores the importance of tailoring educationa","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":" ","pages":"e78371"},"PeriodicalIF":3.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12432470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Literacy Training for Digitalization Officers (\"Digi-Managers\") in Outpatient Medical and Psychotherapeutic Care: Conceptualization and Longitudinal Evaluation of a Certificate Course.","authors":"Anne Mainz, Timo Neunaber, Paula Cara D'Agnese, Alexander Eid, Tanja Galla, Christoph Ellers, Sven Meister","doi":"10.2196/70843","DOIUrl":"https://doi.org/10.2196/70843","url":null,"abstract":"<p><strong>Background: </strong>Digital tools, services, and information in patient care demand new competencies in outpatient care, and the workforce is faced with the need to deal with digitalization.</p><p><strong>Objective: </strong>In a targeted certificate course (Certification of Digitalization Officers in Medical Practices and Psychotherapeutic Practices, Digi-Manager), medical assistants are trained to serve as digitalization officers, enabling them to implement the requirements of digitalized health care within their practices.</p><p><strong>Methods: </strong>As part of an accompanying study, the course is evaluated by the participants, and the change in their digital literacy is recorded. We measured different knowledge, skills, and attitude dimensions at 3 different times-before, during, and after the course-and used ANOVA to examine significant changes.</p><p><strong>Results: </strong>Digi-Managers started the course with an already high self-assessment of their digital literacy. Skills and knowledge increased significantly in all categories (cognitive, technical, ethical, and health information) from the initial to the final measurement, as did self-confidence in the use of general software and hardware. Positive attitude remained stable over the training period, and the course was rated very positively by participants across all areas.</p><p><strong>Conclusions: </strong>Training programs on digital topics for health care professionals are necessary, and this certification course is a role model for successful further education through a mixture of theoretical knowledge transfer and practical application. Especially, the use of a digital maturity model and a digital laboratory was a unique and useful feature. Further research needs to go into alternative assessment methods of digital literacy, as the results suggest that self-assessment measures self-efficacy and confidence, rather than pure competence. Nevertheless, the increase in self-assessed competence suggests that the training was successful.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e70843"},"PeriodicalIF":3.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Large Language Model-Based System for Medical History-Taking Training: Prospective Multicase Study on Evaluation Stability, Human-AI Consistency, and Transparency.","authors":"Yang Liu, Chujun Shi, Liping Wu, Xiule Lin, Xiaoqin Chen, Yiying Zhu, Haizhu Tan, Weishan Zhang","doi":"10.2196/73419","DOIUrl":"https://doi.org/10.2196/73419","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;History-taking is crucial in medical training. However, current methods often lack consistent feedback and standardized evaluation and have limited access to standardized patient (SP) resources. Artificial intelligence (AI)-powered simulated patients offer a promising solution; however, challenges such as human-AI consistency, evaluation stability, and transparency remain underexplored in multicase clinical scenarios.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and validate the AI-Powered Medical History-Taking Training and Evaluation System (AMTES), based on DeepSeek-V2.5 (DeepSeek), to assess its stability, human-AI consistency, and transparency in clinical scenarios with varying symptoms and difficulty levels.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed AMTES, a system using multiple strategies to ensure dialog quality and automated assessment. A prospective study with 31 medical students evaluated AMTES's performance across 3 cases of varying complexity: a simple case (cough), a moderate case (frequent urination), and a complex case (abdominal pain). To validate our design, we conducted systematic baseline comparisons to measure the incremental improvements from each level of our design approach and tested the framework's generalizability by implementing it with an alternative large language model (LLM) Qwen-Max (Qwen AI; version 20250409), under a zero-modification condition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 31 students practiced with our AMTES. During the training, students generated 8606 questions across 93 history-taking sessions. AMTES achieved high dialog accuracy: 98.6% (SD 1.5%) for cough, 99.0% (SD 1.1%) for frequent urination, and 97.9% (SD 2.2%) for abdominal pain, with contextual appropriateness exceeding 99%. The system's automated assessments demonstrated exceptional stability and high human-AI consistency, supported by transparent, evidence-based rationales. Specifically, the coefficients of variation (CV) were low across total scores (0.87%-1.12%) and item-level scoring (0.55%-0.73%). Total score consistency was robust, with the intraclass correlation coefficients (ICCs) exceeding 0.923 across all scenarios, showing strong agreement. The item-level consistency was remarkably high, consistently above 95%, even for complex cases like abdominal pain (95.75% consistency). In systematic baseline comparisons, the fully-processed system improved ICCs from 0.414/0.500 to 0.923/0.972 (moderate and complex cases), with all CVs ≤1.2% across the 3 cases. A zero-modification implementation of our evaluation framework with an alternative LLM (Qwen-Max) achieved near-identical performance, with the item-level consistency rates over 94.5% and ICCs exceeding 0.89. Overall, 87% of students found AMTES helpful, and 83% expressed a desire to use it again in the future.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our data showed that AMTES demonstrates significant educational value through its L","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e73419"},"PeriodicalIF":3.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-Learning for Pediatric Emergency Department Staff in Point-of-Care Electroencephalogram Interpretation: Prospective Cohort Study.","authors":"Leopold Simma, Maurice Henri Schneeberger, Stefanie von Felten, Michelle Seiler, Georgia Ramantani, Bigna Katrin Bölsterli","doi":"10.2196/69395","DOIUrl":"https://doi.org/10.2196/69395","url":null,"abstract":"<p><strong>Background: </strong>Status epilepticus (SE) represents a critical pediatric emergency necessitating prompt treatment and monitoring. The diagnosis of nonconvulsive SE and the monitoring of convulsive SE require electroencephalogram (EEG) recordings. The integration of simplified point-of-care EEG may improve care in pediatric emergency departments.</p><p><strong>Objective: </strong>This study aims to assess the efficacy of an electronic EEG self-learning module for improving the interpretation of normal cortical activity, artifacts, and seizure patterns in point-of-care EEG by pediatric emergency medicine (PEM) providers.</p><p><strong>Methods: </strong>This prospective cohort study was conducted in a tertiary academic pediatric emergency department and primarily targeted senior medical staff while also engaging junior medical staff and registered nurses. A novel EEG e-learning module trained participants to identify normal cortical activity, artifacts, and seizure patterns. The study comprised pretest, posttest, and 3-month retention assessments to evaluate the EEG total score as its primary outcome and basic EEG knowledge and confidence measures as secondary outcomes. Outcomes were analyzed using mixed-effects proportional odds logistic regression models.</p><p><strong>Results: </strong>Of 102 PEM providers invited, 61 individuals participated (25 senior medical staff, 15 junior medical staff, and 21 registered nurses), and 29 finished the 3-tiered study. In finishers, the EEG total score (max=12 points), indicative of accurate EEG classification, increased substantially between pretest and posttest from a median of 7 (IQR 5-8) to 10 (IQR 7-11) points, corresponding with an increase in the odds of achieving higher EEG total scores at the posttest (odds ratio 24.18, 95% CI 7.398-79.043, P<.001). At the retention test, the EEG total score remained elevated, although to a lesser extent (median 8 points [IQR 6-9]). Similar trends were observed in secondary outcomes.</p><p><strong>Conclusions: </strong>The implementation of an e-learning EEG module improved the ability of PEM providers to interpret EEGs. This study highlights the feasibility of imparting basic EEG skills to nonexperts through targeted educational interventions. However, the sustained retention of such skills requires improvement, emphasizing the necessity for ongoing refresher training.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e69395"},"PeriodicalIF":3.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceptance of AI-Powered Chatbots Among Physiotherapy Students: International Cross-Sectional Study.","authors":"Salwa B El-Sobkey, Kerolous Ishak Kelini, Mahmoud ElKholy, Tayseer Abdeldayem, Mariam Abdallah, Dina Al-Amir Mohamed, Aya Fawzy, Yomna F Ahmed, Ayman El Khatib, Hind Khalid, Balkhis Banu Shaik, Ana Anjos, Mutasim D Alharbi, Karim Fathy, Khaled Takey","doi":"10.2196/76574","DOIUrl":"10.2196/76574","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence-powered chatbots (AI-PCs) are increasingly integrated into educational settings, including health care disciplines. Despite their potential to enhance learning, limited research has investigated physiotherapy (PT) students' acceptance of this technology.</p><p><strong>Objective: </strong>This study aims to assess undergraduate PT students' acceptance of AI-PCs and to identify personal, academic, and technological factors influencing their acceptance.</p><p><strong>Methods: </strong>Over a 4-month period, a cross-sectional survey was conducted across 7 PT programs in 5 countries. Eligible participants were national undergraduate PT students. The technology acceptance model (TAM)-based questionnaire was used for capturing perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual behavioral use of AI-PCs. The influence of personal, academic, and technological factors was examined. Descriptive and inferential statistics were conducted.</p><p><strong>Results: </strong>The mean total TAM score was 3.59 (SD 0.82), indicating moderate acceptance. Of the 1066 participants, 375 (35.2%) showed high acceptance, 650 (60.9%) moderate, and 41 (3.9%) low. Prior experience with artificial intelligence (AI) tools emerged as the strongest predictor of acceptance (β=.43; P<.001), followed by university affiliation (ANOVA P<.001). Cumulative grade point average percentage was positively correlated with TAM score (r=0.135; P<.001) but was not a significant predictor in regression (P=.23). Age (P=.54), sex (P=.56), academic level (P=.26), and current use of AI-PCs (P=.10) were not significant predictors.</p><p><strong>Conclusions: </strong>PT students demonstrated moderate acceptance of AI-PCs. Prior technological experience was the strongest predictor, underscoring the importance of early exposure to AI tools. Educational institutions should consider integrating AI technologies to enhance students' familiarity and foster positive attitudes toward their use.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e76574"},"PeriodicalIF":3.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144883929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Trends in Cadaver Donation and Medical Education Research: Bibliometric Analysis Based on VOSviewer and CiteSpace.","authors":"Xianxian Zhou, Hua Xiong, Yi Wen, Fang Li, Dexi Hu","doi":"10.2196/71935","DOIUrl":"10.2196/71935","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The cadaver serves as a crucial resource in medical education, research, and clinical practice, as well as a vital foundation for fundamental medical experimental teaching.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to use bibliometric analysis to create a knowledge map of cadaver donation in medical education, identify global trends, anticipate future research directions, and offer a foundation for upcoming investigations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Articles and review papers concerning cadaver donation and medical education, with a final search cutoff of January 10, 2025, were systematically retrieved from the Web of Science Core Collection database. Two reviewers carefully examined the initial set of articles based on titles and abstracts to exclude irrelevant ones. A quadratic regression model was used to examine the annual publication data. The model's goodness of fit was assessed using the R2 value, and the statistical significance of the findings was determined through the P valu. The selected publications were then analyzed and visualized for country, institution, author, reference, journal, and keywords using CiteSpace 6.3R3, VOSviewer 1.6.19, and the Online Analysis Platform of the Literature Metrology Database.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The quadratic regression model yielded the equation Y=0.1586X²-633.9X+633395, indicating a substantial increase in the number of publications over time (R2=0.9575, P&lt;.05). The model forecasts that the publication count will reach 107 by 202. This upward trend is statistically significant, highlighting a notable rise in research interest and activity within this field over time. The United States was a major contributor, accounting for 21.2% (303/1114) of all publications. In terms of continents and faiths, Europe and Christianity contributed the most, while McGill University and The University of Sydney were the leading institutions. Prominent authors in this field included De Caro Raffaele, Macchi Veronica, Porzionato Andrea, Stecco Carla, and Dhanani Sonny. The most frequently cocited reference was \"Bodies for Anatomy Education in Medical Schools: An Overview of the Sources of Cadavers Worldwide.\" The journal Anatomical Sciences Education published the most articles in this area and received the highest citation count. Cluster analysis of keywords revealed that \"kidney transplantation,\" \"gross anatomy education,\" and \"brain death\" were key research topics, while burst analysis of keywords identified \"public perception\" and \"anatomical science\" as emerging areas of investigation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This research presents a distinctive bibliometric approach to cadaver donation within medical education, setting it apart from previous studies by delivering an extensive global overview of trends and influential contributors in this domain. The results emphasize the increasing global interest and collaborative efforts surrounding cadaver don","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"11 ","pages":"e71935"},"PeriodicalIF":3.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12369992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}