JMIR Medical Education最新文献

Unlabelled: The COVID-19 pandemic has had a significant impact on the mental health of college students worldwide. As colleges shifted to online instruction, students faced disruptions and increased stressors, leading to a decline in mental health that appears to continue in the postpandemic era. To alleviate this problem, academic institutions have implemented various interventions to address mental health issues; however, many of these interventions focus on a single approach and lack diverse delivery methods. This viewpoint introduces the concept of a multimodal self-care online course, Life101: Mental and Physical Self-Care, and discusses the potential effectiveness of such an intervention in improving students' well-being. The course combines evidence-based interventions and incorporates interactive lectures, workshops, and guest speakers. Pre- and postcourse surveys were conducted over a span of 4 academic terms to evaluate the impact of this course on the well-being and self-care practices of students. The survey data suggest positive outcomes in students taking Life101, including the adoption of healthier habits, reduced stress levels, and increased knowledge and practice of self-care techniques. Life101 represents a novel multimodality intervention to address the epidemic of mental health issues faced by students today. By implementing similar evidence-based multimodal didactic curricula across campuses, academic institutions may be able to better equip students to navigate challenges and promote their overall well-being.

Unlabelled: Higher education institutions, including medical schools, increasingly rely on fundraising to bridge funding gaps and support their missions. This paper presents a viewpoint on data-driven strategies in fundraising, outlining a 4-step approach for effective planning while considering ethical implications. It outlines a 4-step approach to creating an effective, end-to-end, data-driven fundraising plan, emphasizing the crucial stages of data collection, data analysis, goal establishment, and targeted strategy formulation. By leveraging internal and external data, schools can create tailored outreach initiatives that resonate with potential donors. However, the fundraising process must be grounded in ethical considerations. Ethical challenges, particularly in fundraising with grateful medical patients, necessitate transparent and honest practices prioritizing donors' and beneficiaries' rights and safeguarding public trust. This paper presents a viewpoint on the critical role of data-driven strategies in fundraising for medical education. It emphasizes integrating comprehensive data analysis with ethical considerations to enhance fundraising efforts in medical schools. By integrating data analytics with fundraising best practices and ensuring ethical practice, medical institutions can ensure financial support and foster enduring, trust-based relationships with their donor communities.

Unlabelled: To assess the utility of wearable cameras in medical examinations, we created a physician-view video-based examination question and explanation, and the survey results indicated that these cameras can enhance the evaluation and educational capabilities of medical examinations.

Background: Patient safety is a fundamental aspect of health care practice across global health systems. Safe practices, which include incident reporting systems, have proven valuable in preventing the recurrence of safety incidents. However, the accessibility of this tool for health care discipline students is not consistent, limiting their acquisition of competencies. In addition, there is no tools to familiarize students with analyzing safety incidents. Gamification has emerged as an effective strategy in health care education.

Objective: This study aims to develop an incident reporting system tailored to the specific needs of health care discipline students, named Safety Incident Report System for Students. Secondary objectives included studying the performance of different groups of students in the use of the platform and training them on the correct procedures for reporting.

Methods: This was an observational study carried out in 3 phases. Phase 1 consisted of the development of the web-based platform and the incident registration form. For this purpose, systems already developed and in use in Spain were taken as a basis. During phase 2, a total of 223 students in medicine and nursing with clinical internships from universities in Argentina, Brazil, Colombia, Ecuador, and Spain received an introductory seminar and were given access to the platform. Phase 3 ran in parallel and involved evaluation and feedback of the reports received as well as the opportunity to submit the students' opinion on the process. Descriptive statistics were obtained to gain information about the incidents, and mean comparisons by groups were performed to analyze the scores obtained.

Results: The final form was divided into 9 sections and consisted of 48 questions that allowed for introducing data about the incident, its causes, and proposals for an improvement plan. The platform included a personal dashboard displaying submitted reports, average scores, progression, and score rankings. A total of 105 students participated, submitting 147 reports. Incidents were mainly reported in the hospital setting, with complications of care (87/346, 25.1%) and effects of medication or medical products (82/346, 23.7%) being predominant. The most repeated causes were related confusion, oversight, or distractions (49/147, 33.3%) and absence of process verification (44/147, 29.9%). Statistically significant differences were observed between the mean final scores received by country (P<.001) and sex (P=.006) but not by studies (P=.47). Overall, participants rated the experience of using the Safety Incident Report System for Students positively.

Conclusions: This study presents an initial adaptation of reporting systems to suit the needs of students, introducing a guided and inspiring framework that has garnered positive acceptance among students. Through this endeavor, a p

Background: The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.

Objective: The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.

Methods: We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.

Results: Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.

Conclusions: This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the developm

Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out.

Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot.

Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated.

Results: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference -0.51, 95% CI -0.54 to -0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference -0.06, 95% CI -0.08 to -0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001).

Conclusions: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.

Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse.

Objective: This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses.

Methods: We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio.

Results: GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience.

Conclusions: GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Fur

Background: Multiple-choice examinations are frequently used in German dental schools. However, details regarding the used item types and applied scoring methods are lacking.

Objective: This study aims to gain insight into the current use of multiple-choice items (ie, questions) in summative examinations in German undergraduate dental training programs.

Methods: A paper-based 10-item questionnaire regarding the used assessment methods, multiple-choice item types, and applied scoring methods was designed. The pilot-tested questionnaire was mailed to the deans of studies and to the heads of the Department of Operative/Restorative Dentistry at all 30 dental schools in Germany in February 2023. Statistical analysis was performed using the Fisher exact test (P<.05).

Results: The response rate amounted to 90% (27/30 dental schools). All respondent dental schools used multiple-choice examinations for summative assessments. Examinations were delivered electronically by 70% (19/27) of the dental schools. Almost all dental schools used single-choice Type A items (24/27, 89%), which accounted for the largest number of items in approximately half of the dental schools (13/27, 48%). Further item types (eg, conventional multiple-select items, Multiple-True-False, and Pick-N) were only used by fewer dental schools (≤67%, up to 18 out of 27 dental schools). For the multiple-select item types, the applied scoring methods varied considerably (ie, awarding [intermediate] partial credit and requirements for partial credit). Dental schools with the possibility of electronic examinations used multiple-select items slightly more often (14/19, 74% vs 4/8, 50%). However, this difference was statistically not significant (P=.38). Dental schools used items either individually or as key feature problems consisting of a clinical case scenario followed by a number of items focusing on critical treatment steps (15/27, 56%). Not a single school used alternative testing methods (eg, answer-until-correct). A formal item review process was established at about half of the dental schools (15/27, 56%).

Conclusions: Summative assessment methods among German dental schools vary widely. Especially, a large variability regarding the use and scoring of multiple-select multiple-choice items was found.

Unlabelled: The continued demand for digital health requires that providers adapt thought processes to enable sound clinical decision-making in digital settings. Providers report that lack of training is a barrier to providing digital health care. Physical examination techniques and hands-on interventions must be adjusted in safe, reliable, and feasible ways to provide digital care, and decision-making may be impacted by modifications made to these techniques. We have proposed a framework to determine whether a procedure can be modified to obtain a comparable result in a digital environment or whether a referral to in-person care is required. The decision-making framework was developed using program outcomes of a digital physical therapy platform and aims to alleviate barriers to delivering digital care that providers may experience. This paper describes the unique considerations a provider must make when collecting background information, selecting and executing procedures, assessing results, and determining whether they can proceed with clinical care in digital settings.

Background: Over the last decade, there has been growing interest in inverted classroom teaching (ICT) and its various forms within the education sector. Physiology is a core course that bridges basic and clinical medicine, and ICT in physiology has been sporadically practiced to different extents globally. However, students' and teachers' responses and feedback to ICT in physiology are diverse, and the effectiveness of a modified ICT model integrated into regular teaching practice in physiology courses is difficult to assess objectively and quantitatively.

Objective: This study aimed to explore the current status and development direction of ICT in physiology in basic medical education using bibliometric visual analysis of the related literature.

Methods: A bibliometric analysis of the ICT-related literature in physiology published between 2000 and 2023 was performed using CiteSpace, a bibliometric visualization tool, based on the Web of Science database. Moreover, an in-depth review was performed to summarize the application of ICT in physiology courses worldwide, along with identification of research hot spots and development trends.

Results: A total of 42 studies were included for this bibliometric analysis, with the year 2013 marking the commencement of the field. University staff and doctors working at affiliated hospitals represent the core authors of this field, with several research teams forming cooperative relationships and developing research networks. The development of ICT in physiology could be divided into several stages: the introduction stage (2013-2014), extensive practice stage (2015-2019), and modification and growth stage (2020-2023). Gopalan C is the author with the highest citation count of 5 cited publications and has published 14 relevant papers since 2016, with a significant surge from 2019 to 2022. Author collaboration is generally limited in this field, and most academic work has been conducted in independent teams, with minimal cross-team communication. Authors from the United States published the highest number of papers related to ICT in physiology (18 in total, accounting for over 43% of the total papers), and their intermediary centrality was 0.24, indicating strong connections both within the country and internationally. Chinese authors ranked second, publishing 8 papers in the field, although their intermediary centrality was only 0.02, suggesting limited international influence and lower overall research quality. The topics of ICT in physiology research have been multifaceted, covering active learning, autonomous learning, student performance, teaching effect, blended teaching, and others.

Conclusions: This bibliometric analysis and literature review provides a comprehensive overview of the history, development process, and future direction of the field of ICT in physiology. These findings can help t