JMIR Medical Education最新文献

{"title":"Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study.","authors":"Vasudha L Bhavaraju, Sarada Panchanathan, Brigham C Willis, Pamela Garcia-Filion","doi":"10.2196/53337","DOIUrl":"10.2196/53337","url":null,"abstract":"<p><strong>Background: </strong>Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 (COVID-19) pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee's experiences using existing tools.</p><p><strong>Objective: </strong>This study aims to demonstrate a feasible and efficient method to capture electronic health record (EHR) data that measure the volume and variety of pediatric resident clinical experiences from a continuity clinic; generate individual-, class-, and graduate-level benchmark data; and create a visualization for learners to quickly identify gaps in clinical experiences.</p><p><strong>Methods: </strong>This pilot was conducted in a large, urban pediatric residency program from 2016 to 2022. Through consensus, 5 pediatric faculty identified diagnostic groups that pediatric residents should see to be competent in outpatient pediatrics. Information technology consultants used International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding with each diagnostic group to extract EHR patient encounter data as an indicator of exposure to the specific diagnosis. The frequency (volume) and diagnosis types (variety) seen by active residents (classes of 2020-2022) were compared with class and graduated resident (classes of 2016-2019) averages. These data were converted to percentages and translated to a radar chart visualization for residents to quickly compare their current clinical experiences with peers and graduates. Residents were surveyed on the use of these data and the visualization to identify training gaps.</p><p><strong>Results: </strong>Patient encounter data about clinical experiences for 102 residents (N=52 graduates) were extracted. Active residents (n=50) received data reports with radar graphs biannually: 3 for the classes of 2020 and 2021 and 2 for the class of 2022. Radar charts distinctly demonstrated gaps in diagnoses exposure compared with classmates and graduates. Residents found the visualization useful in setting clinical and learning goals.</p><p><strong>Conclusions: </strong>This pilot describes an innovative method of capturing and presenting data about resident clinical experiences, compared with peer and graduate benchmarks, to identify learning gaps that may result from disruptions or modifications in medical training. This methodology can be aggregated across specialties and institutions and potentially inform competence-based medical education.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e53337"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11559912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591681","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":"ChatGPT-4 Omni Performance in USMLE Disciplines and Clinical Skills: Comparative Analysis.","authors":"Brenton T Bicknell, Danner Butler, Sydney Whalen, James Ricks, Cory J Dixon, Abigail B Clark, Olivia Spaedy, Adam Skelton, Neel Edupuganti, Lance Dzubinski, Hudson Tate, Garrett Dyess, Brenessa Lindeman, Lisa Soleymani Lehmann","doi":"10.2196/63430","DOIUrl":"10.2196/63430","url":null,"abstract":"<p><strong>Background: </strong>Recent studies, including those by the National Board of Medical Examiners, have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of LLM performance in specific medical content areas, thus limiting an assessment of their potential utility in medical education.</p><p><strong>Objective: </strong>This study aimed to assess and compare the accuracy of successive ChatGPT versions (GPT-3.5, GPT-4, and GPT-4 Omni) in USMLE disciplines, clinical clerkships, and the clinical skills of diagnostics and management.</p><p><strong>Methods: </strong>This study used 750 clinical vignette-based multiple-choice questions to characterize the performance of successive ChatGPT versions (ChatGPT 3.5 [GPT-3.5], ChatGPT 4 [GPT-4], and ChatGPT 4 Omni [GPT-4o]) across USMLE disciplines, clinical clerkships, and in clinical skills (diagnostics and management). Accuracy was assessed using a standardized protocol, with statistical analyses conducted to compare the models' performances.</p><p><strong>Results: </strong>GPT-4o achieved the highest accuracy across 750 multiple-choice questions at 90.4%, outperforming GPT-4 and GPT-3.5, which scored 81.1% and 60.0%, respectively. GPT-4o's highest performances were in social sciences (95.5%), behavioral and neuroscience (94.2%), and pharmacology (93.2%). In clinical skills, GPT-4o's diagnostic accuracy was 92.7% and management accuracy was 88.8%, significantly higher than its predecessors. Notably, both GPT-4o and GPT-4 significantly outperformed the medical student average accuracy of 59.3% (95% CI 58.3-60.3).</p><p><strong>Conclusions: </strong>GPT-4o's performance in USMLE disciplines, clinical clerkships, and clinical skills indicates substantial improvements over its predecessors, suggesting significant potential for the use of this technology as an educational aid for medical students. These findings underscore the need for careful consideration when integrating LLMs into medical education, emphasizing the importance of structured curricula to guide their appropriate use and the need for ongoing critical analyses to ensure their reliability and effectiveness.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e63430"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11611793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591666","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":"The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.","authors":"Sauliha Rabia Alli, Soaad Qahhār Hossain, Sunit Das, Ross Upshur","doi":"10.2196/51446","DOIUrl":"10.2196/51446","url":null,"abstract":"<p><strong>Unlabelled: </strong>In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient's problem, performing the physical examination, interpreting the findings of diagnostic tests, or proposing a management plan. The reasons for this uncertainty are widespread, including the lack of knowledge about the patient, individual physician limitations, and the limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence (AI) tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, AI tools may have the ability to improve data collection on diseases, patient beliefs, values, and preferences, thereby allowing more time for physician-patient communication. By using methods not previously considered, these tools hold the potential to reduce the uncertainty in medicine, such as those arising due to the lack of clinical information and provider skill and bias. Despite this possibility, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of AI on medical uncertainty and discuss practical approaches to teaching the use of AI tools in medical schools and residency training programs, including AI ethics, real-world skills, and technological aptitude.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e51446"},"PeriodicalIF":3.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11554287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575636","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":"Transforming the Future of Digital Health Education: Redesign of a Graduate Program Using Competency Mapping.","authors":"Michelle Mun, Sonia Chanchlani, Kayley Lyons, Kathleen Gray","doi":"10.2196/54112","DOIUrl":"10.2196/54112","url":null,"abstract":"<p><strong>Unlabelled: </strong>Digital transformation has disrupted many industries but is yet to revolutionize health care. Educational programs must be aligned with the reality that goes beyond developing individuals in their own professions, professionals wishing to make an impact in digital health will need a multidisciplinary understanding of how business models, organizational processes, stakeholder relationships, and workforce dynamics across the health care ecosystem may be disrupted by digital health technology. This paper describes the redesign of an existing postgraduate program, ensuring that core digital health content is relevant, pedagogically sound, and evidence-based, and that the program provides learning and practical application of concepts of the digital transformation of health. Existing subjects were mapped to the American Medical Informatics Association Clinical Informatics Core Competencies, followed by consultation with leadership to further identify gaps or opportunities to revise the course structure. New additions of core and elective subjects were proposed to align with the competencies. Suitable electives were chosen based on stakeholder feedback and a review of subjects in fields relevant to digital transformation of health. The program was revised with a new title, course overview, course intended learning outcomes, reorganizing of core subjects, and approval of new electives, adding to a suite of professional development offerings and forming a structured pathway to further qualification. Programs in digital health must move beyond purely informatics-based competencies toward enabling transformational change. Postgraduate program development in this field is possible within a short time frame with the use of established competency frameworks and expert and student consultation.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e54112"},"PeriodicalIF":3.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559031","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":"A Pilot Project to Promote Research Competency in Medical Students Through Journal Clubs: Mixed Methods Study.","authors":"Mert Karabacak, Zeynep Ozcan, Burak Berksu Ozkara, Zeynep Sude Furkan, Sotirios Bisdas","doi":"10.2196/51173","DOIUrl":"10.2196/51173","url":null,"abstract":"<p><strong>Background: </strong>Undergraduate medical students often lack hands-on research experience and fundamental scientific research skills, limiting their exposure to the practical aspects of scientific investigation. The Cerrahpasa Neuroscience Society introduced a program to address this deficiency and facilitate student-led research.</p><p><strong>Objective: </strong>The primary goal of this initiative was to enhance medical students' research output by enabling them to generate and publish peer-reviewed papers within the framework of this pilot project. The project aimed to provide an accessible, global model for research training through structured journal clubs, mentorship from experienced peers, and resource access.</p><p><strong>Methods: </strong>In January 2022, a total of 30 volunteer students from various Turkish medical schools participated in this course-based undergraduate research experience program. Students self-organized into 2 groups according to their preferred study type: original research or systematic review. Two final-year students with prior research experience led the project, developing training modules using selected materials. The project was implemented entirely online, with participants completing training modules before using their newly acquired theoretical knowledge to perform assigned tasks.</p><p><strong>Results: </strong>Based on student feedback, the project timeline was adjusted to allow for greater flexibility in meeting deadlines. Despite these adjustments, participants successfully completed their tasks, applying the theoretical knowledge they had gained to their respective assignments. As of April 2024, the initiative has culminated in 3 published papers and 3 more under peer review. The project has also seen an increase in student interest in further involvement and self-paced learning.</p><p><strong>Conclusions: </strong>This initiative leverages globally accessible resources for research training, effectively fostering research competency among participants. It has successfully demonstrated the potential for undergraduates to contribute to medical research output and paved the way for a self-sustaining, student-led research program. Despite some logistical challenges, the project provided valuable insights for future implementations, showcasing the potential for students to engage in meaningful, publishable research.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e51173"},"PeriodicalIF":3.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559030","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":"A SIMBA CoMICs Initiative to Cocreating and Disseminating Evidence-Based, Peer-Reviewed Short Videos on Social Media: Mixed Methods Prospective Study.","authors":"Maiar Elhariry, Kashish Malhotra, Kashish Goyal, Marco Bardus, Punith Kempegowda","doi":"10.2196/52924","DOIUrl":"10.2196/52924","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Social media is a powerful platform for disseminating health information, yet it is often riddled with misinformation. Further, few guidelines exist for producing reliable, peer-reviewed content. This study describes a framework for creating and disseminating evidence-based videos on polycystic ovary syndrome (PCOS) and thyroid conditions to improve health literacy and tackle misinformation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The study aims to evaluate the creation, dissemination, and impact of evidence-based, peer-reviewed short videos on PCOS and thyroid disorders across social media. It also explores the experiences of content creators and assesses audience engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This mixed methods prospective study was conducted between December 2022 and May 2023 and comprised five phases: (1) script generation, (2) video creation, (3) cross-platform publication, (4) process evaluation, and (5) impact evaluation. The SIMBA-CoMICs (Simulation via Instant Messaging for Bedside Application-Combined Medical Information Cines) initiative provides a structured process where medical concepts are simplified and converted to visually engaging videos. The initiative recruited medical students interested in making visually appealing and scientifically accurate videos for social media. The students were then guided to create video scripts based on frequently searched PCOS- and thyroid-related topics. Once experts confirmed the accuracy of the scripts, the medical students produced the videos. The videos were checked by clinical experts and experts with lived experience to ensure clarity and engagement. The SIMBA-CoMICs team then guided the students in editing these videos to fit platform requirements before posting them on TikTok, Instagram, YouTube, and Twitter. Engagement metrics were tracked over 2 months. Content creators were interviewed, and thematic analysis was performed to explore their experiences.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The 20 videos received 718 likes, 120 shares, and 54,686 views across all platforms, with TikTok (19,458 views) and Twitter (19,678 views) being the most popular. Engagement increased significantly, with follower growth ranging from 5% on Twitter to 89% on TikTok. Thematic analysis of interviews with 8 out of 38 participants revealed 4 key themes: views on social media, advice for using social media, reasons for participating, and reflections on the project. Content creators highlighted the advantages of social media, such as large outreach (12 references), convenience (10 references), and accessibility to opportunities (7 references). Participants appreciated the nonrestrictive participation criteria, convenience (8 references), and the ability to record from home using prewritten scripts (6 references). Further recommendations to improve the content creation experience included awareness of audience demographics (9 references), sharing content on multiple platforms ","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e52924"},"PeriodicalIF":4.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548074","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":"Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study.","authors":"Wenjuan Tao, Jinming Yang, Xing Qu","doi":"10.2196/57132","DOIUrl":"10.2196/57132","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future health care providers, play a crucial role in shaping the future of health care. However, little is known about the utilization of, perceptions on, and intention to use AI chatbots among medical students in China.</p><p><strong>Objective: </strong>This study aims to explore the utilization of, perceptions on, and intention to use generative AI chatbots among medical students in China, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. By conducting a national cross-sectional survey, we sought to identify the key determinants that influence medical students' acceptance of AI chatbots, thereby providing a basis for enhancing their integration into medical education. Understanding these factors is crucial for educators, policy makers, and technology developers to design and implement effective AI-driven educational tools that align with the needs and expectations of future health care professionals.</p><p><strong>Methods: </strong>A web-based electronic survey questionnaire was developed and distributed via social media to medical students across the country. The UTAUT was used as a theoretical framework to design the questionnaire and analyze the data. The relationship between behavioral intention to use AI chatbots and UTAUT predictors was examined using multivariable regression.</p><p><strong>Results: </strong>A total of 693 participants were from 57 universities covering 21 provinces or municipalities in China. Only a minority (199/693, 28.72%) reported using AI chatbots for studying, with ChatGPT (129/693, 18.61%) being the most commonly used. Most of the participants used AI chatbots for quickly obtaining medical information and knowledge (631/693, 91.05%) and increasing learning efficiency (594/693, 85.71%). Utilization behavior, social influence, facilitating conditions, perceived risk, and personal innovativeness showed significant positive associations with the behavioral intention to use AI chatbots (all P values were <.05).</p><p><strong>Conclusions: </strong>Chinese medical students hold positive perceptions toward and high intentions to use AI chatbots, but there are gaps between intention and actual adoption. This highlights the need for strategies to improve access, training, and support and provide peer usage examples to fully harness the potential benefits of chatbot technology.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e57132"},"PeriodicalIF":3.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11533383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509604","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":"Evaluating the Effectiveness of an Online Course on Pediatric Malnutrition for Syrian Health Professionals: Qualitative Delphi Study.","authors":"Amal Sahyouni, Imad Zoukar, Mayssoon Dashash","doi":"10.2196/53151","DOIUrl":"10.2196/53151","url":null,"abstract":"<p><strong>Background: </strong>There is a shortage of competent health professionals in managing malnutrition. Online education may be a practical and flexible approach to address this gap.</p><p><strong>Objective: </strong>This study aimed to identify essential competencies and assess the effectiveness of an online course on pediatric malnutrition in improving the knowledge of pediatricians and health professionals.</p><p><strong>Methods: </strong>A focus group (n=5) and Delphi technique (n=21 health professionals) were used to identify 68 essential competencies. An online course consisting of 4 educational modules in Microsoft PowerPoint (Microsoft Corp) slide form with visual aids (photos and videos) was designed and published on the Syrian Virtual University platform website using an asynchronous e-learning system. The course covered definition, classification, epidemiology, anthropometrics, treatment, and consequences. Participants (n=10) completed a pretest of 40 multiple-choice questions, accessed the course, completed a posttest after a specified period, and filled out a questionnaire to measure their attitude and assess their satisfaction.</p><p><strong>Results: </strong>A total of 68 essential competencies were identified, categorized into 3 domains: knowledge (24 competencies), skills (29 competencies), and attitudes (15 competencies). These competencies were further classified based on their focus area: etiology (10 competencies), assessment and diagnosis (21 competencies), and management (37 competencies). Further, 10 volunteers, consisting of 5 pediatricians and 5 health professionals, participated in this study over a 2-week period. A statistically significant increase in knowledge was observed among participants following completion of the online course (pretest mean 24.2, SD 6.1, and posttest mean 35.2, SD 3.3; P<.001). Pediatricians demonstrated higher pre- and posttest scores compared to other health care professionals (all P values were <.05). Prior malnutrition training within the past year positively impacted pretest scores (P=.03). Participants highly rated the course (mean satisfaction score >3.0 on a 5-point Likert scale), with 60% (6/10) favoring a blended learning approach.</p><p><strong>Conclusions: </strong>In total, 68 essential competencies are required for pediatricians to manage children who are malnourished. The online course effectively improved knowledge acquisition among health care professionals, with high participant satisfaction and approval of the e-learning environment.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e53151"},"PeriodicalIF":3.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802437","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":"Naloxone Coprescribing and the Prevention of Opioid Overdoses: Quasi-Experimental Metacognitive Assessment of a Novel Education Initiative.","authors":"Michael Enich, Cory Morton, Richard Jermyn","doi":"10.2196/54280","DOIUrl":"10.2196/54280","url":null,"abstract":"<p><strong>Background: </strong>Critical evaluation of naloxone coprescription academic detailing programs has been positive, but little research has focused on how participant thinking changes during academic detailing.</p><p><strong>Objective: </strong>The dual purposes of this study were to (1) present a metacognitive evaluation of a naloxone coprescription academic detailing intervention and (2) describe the application of a metacognitive evaluation for future medical education interventions.</p><p><strong>Methods: </strong>Data were obtained from a pre-post knowledge assessment of a web-based, self-paced intervention designed to increase knowledge of clinical and organizational best practices for the coprescription of naloxone. To assess metacognition, items were designed with confidence-weighted true-false scoring. Multiple metacognitive scores were calculated: 3 content knowledge scores and 5 confidence-weighted true-false scores. Statistical analysis examined whether there were significant differences in scores before and after intervention. Analysis of overall content knowledge showed significant improvement at posttest.</p><p><strong>Results: </strong>There was a significant positive increase in absolute accuracy of participant confidence judgments, confidence in correct probability, and confidence in incorrect probability (all P values were <.05). Overall, results suggest an improvement in content knowledge scores after intervention and, metacognitively, suggest that individuals were more confident in their answer choices, regardless of correctness.</p><p><strong>Conclusions: </strong>Implications include the potential application of metacognitive evaluations to assess nuances in learner performance during academic detailing interventions and as a feedback mechanism to reinforce learning and guide curricular design.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e54280"},"PeriodicalIF":3.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523263","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":"Social Media Usage for Medical Education and Smartphone Addiction Among Medical Students: National Web-Based Survey.","authors":"Thomas Clavier, Emma Chevalier, Zoé Demailly, Benoit Veber, Imad-Abdelkader Messaadi, Benjamin Popoff","doi":"10.2196/55149","DOIUrl":"10.2196/55149","url":null,"abstract":"<p><strong>Background: </strong>Social media (SoMe) have taken a major place in the medical field, and younger generations are increasingly using them as their primary source to find information.</p><p><strong>Objective: </strong>This study aimed to describe the use of SoMe for medical education among French medical students and assess the prevalence of smartphone addiction in this population.</p><p><strong>Methods: </strong>A cross-sectional web-based survey was conducted among French medical students (second to sixth year of study). The questionnaire collected information on SoMe use for medical education and professional behavior. Smartphone addiction was assessed using the Smartphone Addiction Scale Short-Version (SAS-SV) score.</p><p><strong>Results: </strong>A total of 762 medical students responded to the survey. Of these, 762 (100%) were SoMe users, spending a median of 120 (IQR 60-150) minutes per day on SoMe; 656 (86.1%) used SoMe for medical education, with YouTube, Instagram, and Facebook being the most popular platforms. The misuse of SoMe in a professional context was also identified; 27.2% (207/762) of students posted hospital internship content, and 10.8% (82/762) searched for a patient's name on SoMe. Smartphone addiction was prevalent among 29.1% (222/762) of respondents, with a significant correlation between increased SoMe use and SAS-SV score (r=0.39, 95% CI 0.33-0.45; P<.001). Smartphone-addicted students reported a higher impact on study time (211/222, 95% vs 344/540, 63.6%; P<.001) and a greater tendency to share hospital internship content on social networks (78/222, 35.1% vs 129/540, 23.8%; P=.002).</p><p><strong>Conclusions: </strong>Our findings reveal the extensive use of SoMe for medical education among French medical students, alongside a notable prevalence of smartphone addiction. These results highlight the need for medical schools and educators to address the responsible use of SoMe and develop strategies to mitigate the risks associated with excessive use and addiction.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"10 ","pages":"e55149"},"PeriodicalIF":3.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509603","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}