JMIR Medical Education最新文献

{"title":"Enhancing Psychiatry Training Using an Agentic AI Simulated Consultation Tool: Prospective Cohort Study.","authors":"Alice Rueda, Huda F Al-Shamali, Zack Cote, Niloy Roy, Reinhard Janssen-Aguilar, Jithin Joseph, Bazen Gashaw Teferra, Mohammad Amin Kamaleddin, Lisa Burback, Olga Winkler, Bill Kapralos, Andrei Torres, Divya Sharma, Sridhar Krishnan, Guillaume Dumas, Andrew Greenshaw, Alexandre Hudon, Sanjeev Sockalingam, Yanbo Zhang, Adam Dubrowski, Venkat Bhat","doi":"10.2196/88580","DOIUrl":"10.2196/88580","url":null,"abstract":"<p><strong>Background: </strong>Canadian psychiatry residents must demonstrate consultation competency, assessed using the standardized assessment of a clinical encounter report (STACER). However, opportunities to practice these skills and receive constructive assessment remain limited in clinical settings.</p><p><strong>Objective: </strong>This study aimed to evaluate the technical feasibility of an agentic AI system designed to support psychiatry residents' consultation competence through simulated patient encounters with a patient agent and structured feedback from a rater agent.</p><p><strong>Methods: </strong>We conducted a two-phase technical feasibility prospective single-arm cohort study of the STACER Agentic System, a large language model-based platform integrating a patient agent and a rater agent. Phase 1 involved automated evaluation of the patient agent using a psychiatrist agent across 227 synthetic major depressive disorder cases. Performance was assessed using DeepEval metrics (correctness, clarity, medical faithfulness, turn relevance, and role adherence) with descriptive statistics and 95% CIs. Phase 2 involved a preliminary user study with 14 convenience-sampled participants: a total of 5 members of the clinical research team and 9 psychiatry residents from the University of Alberta. Participants completed simulated diagnostic interviews and case presentations. Performance was evaluated using STACER-based scoring by the rater agent and 2 psychiatrists. Interrater reliability was assessed using intraclass correlation coefficients (α=.05). Participants rated realism, behavioral consistency, psychiatric nuance, and feedback utility using Likert scales and free-text answers.</p><p><strong>Results: </strong>The patient agent demonstrated high behavioral (51/56, 91.07%) and symptom fidelity (105/110, 95.45%), with strong automated performance (medical faithfulness mean 0.99, 95% CI 0.99-1.00; turn relevance 0.99, 95% CI 0.986-0.992). Participants rated simulations as psychiatrically plausible and diagnostically useful, particularly for depressive symptom representation, although rapport building was moderate (mean 2.78, SD 1.56 to mean 3.00, SD 1.41, out of 5.00) due to limited nonverbal cues. The rater agent generated structured STACER-aligned feedback with high intrarater consistency, especially at the section subtotal level. Interrater reliability with psychiatrists was poor at the item level (intraclass correlation coefficient range=0.25-0.49) but improved to good-to-excellent agreement at the section level for psychiatry resident sessions (intraclass correlation coefficient range=0.89-0.93). The rater agent's scores fell between those of the 2 psychiatrists for the clinical research team and were lower than both human raters for psychiatry residents.</p><p><strong>Conclusions: </strong>The STACER Agentic System demonstrates the technical feasibility of using agentic AI to simulate psychiatric consultations and deliver STACER-ali","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e88580"},"PeriodicalIF":13.9,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13387278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148550667","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":"User Acceptance of an AI-Powered Medical History-Taking Training System Among Undergraduate Medical Students: Mixed Methods Study.","authors":"Yang Liu, Yiying Zhu, Chujun Shi, Xian Lu, Liping Wu, Minghui Yue, Xiaolin Hong, Oudong Xia, Weishan Zhang","doi":"10.2196/101425","DOIUrl":"10.2196/101425","url":null,"abstract":"<p><strong>Background: </strong>AI-powered virtual patient systems provide medical students with repeatable practice environments for history-taking training. However, user acceptance of such systems and the experience dimensions associated with that acceptance lack mixed methods evidence.</p><p><strong>Objective: </strong>This study aimed to (1) examine the associations of system experience and learning experience/intrinsic motivation with overall acceptance among undergraduate medical students using an AI-powered medical history-taking training and evaluation system (AMTES), (2) explore user experience patterns through open-ended questions, and (3) integrate quantitative and qualitative findings to inform system refinement and pedagogical implementation.</p><p><strong>Methods: </strong>A cross-sectional convergent mixed methods design was used. A total of 66 undergraduate medical students at a Chinese medical college completed a postuse questionnaire after AMTES training. The primary outcome was an overall acceptance composite combining use intention, recommendation intention, and overall satisfaction. Associations with system experience and learning experience/intrinsic motivation were examined using linear regression with heteroscedasticity-consistent SEs (type 3) and bootstrap CIs. Sensitivity analyses included covariate adjustment, single-outcome models, a fractional logit model, and content-overlap sensitivity checks for the system-experience composite. Open-ended responses were analyzed using codebook-oriented thematic analysis and integrated with quantitative findings through a joint display.</p><p><strong>Results: </strong>Both system experience and learning experience/intrinsic motivation were positively associated with overall acceptance (standardized β=.526; P<.001 and standardized β=.377; P=.002, respectively; R<sup>2</sup>=0.662). Sensitivity analyses supported the robustness of both positive associations. Qualitative analysis showed that the most frequently nominated benefits clustered within the \"Practice Accessibility and Feedback Support\" theme, particularly self-directed practice (28/66, 42.4%) and immediate feedback (25/66, 37.9%). Refinement priorities clustered around 4 dialogue-quality and assessment dimensions, including semantic understanding, contextual consistency, conversational naturalness, and scoring logic, each cited by 21%-35% of respondents. The mixed methods joint display indicated contextual (same-source) alignment on dialogue and scoring concerns and suggested that perceived scoring-feedback discrepancies may be associated with lower student trust in the feedback function. This cross-dimensional pattern was not apparent from the quantitative model alone.</p><p><strong>Conclusions: </strong>In this exploratory cohort of undergraduate medical students, both system interaction quality and perceived learning value were positively associated with overall acceptance of AMTES, with system interaction quality showing","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":" ","pages":"e101425"},"PeriodicalIF":13.9,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13439043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370156","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":"Perceptions, Attitudes, and Use of AI by Medical Students: Mixed Methods Study.","authors":"Frédéric Paris, Vincent Garrouste, Laure Abensur Vuillaume","doi":"10.2196/91345","DOIUrl":"10.2196/91345","url":null,"abstract":"<p><strong>Background: </strong>AI is transforming medicine by enhancing care, reducing administrative tasks, and facilitating research. AI also raises many concerns, including a lack of clinical context awareness, data dependence, and the absence of ethical judgment. As future practitioners, medical students must be prepared for these changes. Most studies assessing students' attitudes and knowledge were conducted before AI became accessible and tailored to the needs of the population. Therefore, how medical students actually use AI remains largely unexplored.</p><p><strong>Objective: </strong>This study aimed to explore French medical students' perceptions, attitudes, and use of AI.</p><p><strong>Methods: </strong>A mixed methods study was conducted in 2025 among French medical students in their clerkship year. An online survey included open-ended questions about the definition of AI and feelings toward AI, a Likert scale item to assess specific attitudes, and multiple-choice questions about student characteristics. Quantitative analysis was performed using Kruskal-Wallis tests, chi-square tests, and exploratory multivariable linear regression to examine associations between AI knowledge, attitudes, and student characteristics. Qualitative thematic analysis was conducted inductively on open-text responses regarding perceptions of AI, feelings, training expectations, and use.</p><p><strong>Results: </strong>Of the 1377 responses received, 1342 were included. Students had a median age of 23 (IQR 22-24) years and were predominantly in their fifth year. Only 5% (67/1342) provided a correct definition of AI, while 57.4% (770/1342) gave incorrect responses. Attitudes toward AI were generally positive, with a median score of 7 (IQR 5-8). Students with unknown AI definitions had significantly lower attitude scores (P=.02), although the magnitude of the difference was small (ε²=0.005). In multivariable analysis, belonging to the unknown AI definition category remained associated with a lower general attitude toward AI score compared with the incorrect category (regression coefficient β=-0.72, 95% CI -1.17 to -0.28; P=.001). Regarding education, 48.5% (651/1342) of students preferred AI training outside the formal curriculum. Qualitative analysis revealed 5 themes: representation, nuanced optimism, critical consideration, replacement, and AI use. Students describe AI as a robot, an improved search engine, or an unlimited data source. Their nuanced optimism blends enthusiasm for efficient patient care and the opportunity to focus more on the patient relationship, with concerns about dehumanization, energy costs, and skill regression. Critical consideration underscores distrust from ethical dilemmas and data security risks. Replacement concerns arise from shifting professional roles, though many believe human empathy remains irreplaceable. Regarding AI use, students highlight its potential for administrative aid, personalized training, and clinical support","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e91345"},"PeriodicalIF":13.9,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13384426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537263","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":"Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With Artificial Intelligence in Healthcare: A Systematic Review and Meta-Analysis.","authors":"Wei Zhang, Jiaxue Han, Xin Han, Hang Xu, Langkun Wang, Tianhai Lin, Ping Tan, Peng Zhang, Xiaonan Zheng","doi":"10.2196/89411","DOIUrl":"10.2196/89411","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is increasingly encountered in clinical care and medical education, but medical students' attitudes, perceptions, and self-reported familiarity have been assessed using heterogeneous survey instruments, AI referents, and response scales. Prior reviews often combined mixed health-profession populations or summarized central estimates without fully showing variation across settings.</p><p><strong>Objective: </strong>We synthesized quantitative evidence on medical students' AI-related attitudes, perceptions, and self-reported familiarity while examining construct harmonization, participant independence, heterogeneity, prediction intervals, risk of bias, and certainty of evidence.</p><p><strong>Methods: </strong>We searched PubMed/MEDLINE, Embase, Web of Science, Scopus, PsycINFO, and Cochrane CENTRAL from inception to April 1, 2026; supplementary Google Scholar and citation searching are described in the appendices. Eligible studies enrolled students in MD, MBBS, MBChB, or DO-equivalent medical programs or reported separable medical-student data from mixed samples. Proportion outcomes were harmonized into 9 domains and synthesized using random-effects meta-analysis with Freeman-Tukey double-arcsine transformation, between-study variance τ², Hartung-Knapp-Sidik-Jonkman/Knapp-Hartung-adjusted confidence intervals, and prediction intervals. Study-level subgroup analyses and meta-regressions were considered exploratory because of multiple testing, ecological confounding, and construct heterogeneity. Risk of bias was assessed with the JBI analytical cross-sectional checklist, and certainty was assessed with GRADE for proportional self-report outcomes. PROSPERO: CRD420251120543.</p><p><strong>Results: </strong>Ninety-six cross-sectional studies from 37 countries (>45,000 medical students) were included. Summary estimates suggested favorable attitudes but wide between-setting dispersion. Positive attitude toward AI was 76.9% (95% CI 72.2-81.4%; prediction interval 42.2-98.3%; I²=98.3%; 44 studies; N=20,806), perceived career benefit was 78.4% (95% CI 69.5-86.2%; prediction interval 45.3-98.3%; I²=98.0%; 16 studies; N=9,799), and support for curricular integration was 76.6% (95% CI 71.8-81.1%; prediction interval 47.8-96.1%; I²=97.2%; 38 studies; N=16,308). Concern about physician replacement was 39.9% (95% CI 33.6-46.5%; prediction interval 6.6-80.1%; I²=98.8%; 32 studies; N=16,642), willingness to learn about or adopt AI was 71.5% (95% CI 64.8-77.8%; prediction interval 37.9-95.4%; I²=97.7%; 22 studies; N=9,199), and ethical concerns were endorsed by 62.8% (95% CI 53.9-71.3%; prediction interval 21.9-95.0%; I²=98.8%; 28 studies; N=14,571). Self-reported familiarity/knowledge was 63.3% (95% CI 55.9-70.3%; prediction interval 7.8-100.0%; I²=99.5%; 52 studies; N=27,817), and trust in AI-assisted decisions was 50.6% (95% CI 28.5-72.6%; prediction interval 7.3-93.3%; I²=98.1%; 8 studies; N=3,007). Al","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":" ","pages":""},"PeriodicalIF":13.9,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gender Differences in Medical Students' Self-Assessment: Longitudinal Multi-Cohort Study.","authors":"Lennart Handke, Hendrik Friederichs","doi":"10.2196/91071","DOIUrl":"10.2196/91071","url":null,"abstract":"<p><strong>Background: </strong>Self-assessment is a key requirement for lifelong learning in medicine. Evidence from gender-related research indicates that important moderators affecting self-assessment are influenced by gender. Therefore, systematic gender differences in the accuracy of self-assessment may be assumed.</p><p><strong>Objective: </strong>The present study aimed to examine gender differences in medical students' self-assessment. Specifically, this study addressed two research questions: (1) Are there systematic gender differences in medical students' self-assessment accuracy? (2) What is the magnitude of these gender differences when accounting for academic progress and knowledge?</p><p><strong>Methods: </strong>Medical students from 3 cohorts at Medical School OWL were surveyed in 3 waves between April 2023 and April 2024 during the Progress Test Medicine. Prior to taking the test, students were asked to indicate the percentage of Progress Test Medicine questions that they expected to answer correctly in 5 knowledge areas. Self-assessment accuracy was calculated as the difference between the subjective self-assessment and the objective test score. Linear mixed models were used to analyze the influence of gender on students' self-assessment accuracy while accounting for academic progress and knowledge.</p><p><strong>Results: </strong>A total of 165 students with 404 data points participated in this study (269/404, 66.6% women; 135/404, 33.4% men; mean age 21.96, SD 3.61 years). Across all models, women rated themselves significantly less accurately than men (adjusted P values ranged from <.001 to .01). The observed gender effect ranged from -6.08 to -3.74 percentage points.</p><p><strong>Conclusions: </strong>The results indicated systematic gender differences in medical students' self-assessment in favor of men, with a magnitude comparable to the average knowledge acquired in an entire semester of study. In view of the potentially negative consequences of inaccurate self-assessment, targeted support for realistic self-assessment during medical studies may be particularly beneficial for women.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e91071"},"PeriodicalIF":13.9,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13378408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473279","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":"AI-Simulated Patients for Training Shared Decision-Making: Feasibility Study in Medical Education.","authors":"Lea Herschbach, Teresa Festl-Wietek, Alessandra Sonanini, Friederike Holderried, Tobias Albrecht, Bernd Herrmann, Anne Herrmann-Werner","doi":"10.2196/100467","DOIUrl":"10.2196/100467","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Shared decision-making (SDM) is a key element of patient-centered care; however, opportunities for structured and scalable SDM training remain limited in both medical education and clinical practice. Advances in AI have enabled chatbot-based simulations that may support repeated practice and provide automated feedback on SDM-related communication behaviors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to (1) evaluate the agreement between AI-generated and human ratings of SDM performance, (2) compare SDM-related performance and attitudes between medical students and physicians, and (3) explore changes in communicative self-efficacy (CSE) following chatbot-based SDM practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a feasibility study using a pre-post mixed methods design. Medical students and physicians practiced SDM in a 20-minute chat-based interaction with an AI-simulated patient, followed by automated feedback on SDM performance. Prior to the interaction, participants' attitudes toward SDM (IcanSDM) and their CSE (Self-Efficacy in Patient-Centeredness Questionnaire [SEPCQ-24-GER]) were assessed; CSE was reevaluated after the intervention. SDM performance was measured using the validated Observing Patient Involvement in Decision Making (OPTION-12) scale, with the AI evaluating participants' performance from the patient's perspective. These AI-generated ratings were then compared with human ratings to evaluate agreement and rating quality. To further investigate discrepancies between AI and human ratings, a qualitative analysis of selected cases was conducted. Additional measures included demographics, perceived authenticity, and benefits of the interaction. Quantitative analyses included group comparisons, pre-post analyses, and psychometric evaluation of the OPTION-12 scale in an AI-mediated setting. The study was preregistered on the Open Science Framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 58 participants were included in the analysis, 22 (37.9%) of whom were students, 34 (58.2%) were physicians, and 2 did not declare. Both groups reported similarly positive attitudes toward SDM, but medical students (n=22; mean 28.67, SD 4.32) achieved higher OPTION-12 performance scores than physicians (n=34; mean 22.79, SD 8.14). CSE showed a slight decrease following the intervention. Agreement between AI-generated and human OPTION-12 total scores was high (intraclass correlation coefficient=0.86, df=2,1; P&lt;.001), although AI ratings were systematically higher. The OPTION-12 scale in the AI setting demonstrated good internal consistency (Cronbach α=0.88; mean interitem correlation r=0.43). Participants perceived the AI-simulated interaction as authentic and potentially useful for SDM training.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This feasibility study provides preliminary and exploratory insights into the use of AI-simulated patients for SDM training in medical education. The findings contribute to the","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e100467"},"PeriodicalIF":13.9,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13424757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473261","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":"Systemic Barriers to Curriculum Adaptation for Rapidly Changing Knowledge in Medical Education: Qualitative Study.","authors":"Martin Baumgartner, Matthias Stadler, Matthias Samwald, Martin R Fischer","doi":"10.2196/96244","DOIUrl":"10.2196/96244","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;For this study, digitalization in medicine was used as an illustrative case to investigate how medical curricula respond to rapidly changing knowledge. Digitalization is transforming the way medicine and health care are provided and experienced. Experts have suggested various topics for medical curricula to keep pace with rapidly evolving knowledge. However, adapting these curricula remains a lengthy process that often lacks an interdisciplinary approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The perspectives of curriculum governing bodies and the boards responsible for curriculum operations at 2 medical universities were examined regarding the need for curriculum changes to account for digitalization in medicine, as well as the difficulties in adapting the curriculum to ever-growing knowledge. We identify and suggest ways to achieve more agile curriculum development.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study consists of a qualitative analysis of governing policy frameworks and a qualitative study involving 14 video interviews. The interviews were performed with members of university curriculum governing bodies and the boards responsible for curriculum operations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;All participants agreed that digitalization will reshape the medical profession by reducing physical contact, enhancing data-driven communication, and streamlining administrative processes. They highlighted the need for graduates to acquire digital literacy, critical evaluation skills, and a basic understanding of data and statistics. Yet, despite being designed as an integrated program, most participants noted that curricula have become fragmented over time due to a lack of coordination between curriculum modules. Furthermore, current processes lead to a siloed perspective, where limited coordination between modules makes it difficult to implement new knowledge holistically. This lack of intermodule alignment emerged as a key barrier to coherent curricular change. Learning objectives were identified as a promising but underutilized tool for monitoring content, aligning modules, and ensuring that emerging topics such as digitalization are integrated consistently.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Most participants agreed that current processes for monitoring and updating curricula are not efficiently designed, tending to be too static and focusing primarily on the advancement of subject-specific medical knowledge. To prepare current and future students for a rapidly changing world, curriculum processes should evolve from static, fragmented structures to more agile, integrated systems. By mapping the survey results to the curriculum development frameworks of Kern and Harden, we find that the challenge lies not so much in adding new content, but rather in designing curriculum processes that achieve a holistic overview. Strengthening the use of learning objectives as a dynamic monitoring and alignment tool offers a concrete opp","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e96244"},"PeriodicalIF":13.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457061","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 Training for Lay Health Care Workers' Knowledge and Skills in HIV Index Case Testing: Cluster Randomized Trial.","authors":"Tapiwa A Tembo, Nora E Rosenberg, Katie Mollan, Maria Kim, Sarah E Rutstein, Angella M Mkandawire, Mike J Chitani, Caroline Kumbuyo, Duncan Phiri, Mtisunge Mphande, Elijah Kavuta, Samuel Chilala, Jiayu Wang, Saeed Ahmed, Katherine Simon, Linda-Gail Bekker","doi":"10.2196/89942","DOIUrl":"10.2196/89942","url":null,"abstract":"<p><strong>Background: </strong>Task shifting in low-resource settings requires lay health care workers (HCWs) to provide a variety of health services, such as HIV index case testing, whereby sexual partners and family members of people living with HIV are offered HIV testing. For this, lay HCWs require adequate specialized training. Digital technologies hold promise for training lay HCWs in low-resource settings, but their impacts on improving knowledge, attitudes, and skills are not understood.</p><p><strong>Objective: </strong>This study evaluates the impact of digital training on lay HCWs' knowledge, attitudes, and skills to provide HIV index case testing.</p><p><strong>Methods: </strong>We recruited lay HCWs from 34 health facilities in Malawi. We conducted a 2-arm cluster randomized controlled trial from 2022 to 2023, evaluating the impact of a digital training approach. Health facilities (clusters) were randomized 1:2 to the enhanced or standard arms. Lay HCWs in both arms received the standard in-person index case testing training. In addition, lay HCWs in the enhanced arm received tablet-guided training. Knowledge acquisition was measured using multiple-choice questionnaires administered before and after training. Attitudinal gains were assessed through a questionnaire with Likert scale responses before and after training. Between-arm mean differences were evaluated using generalized estimating equations. Skills (fidelity to index and contact testing protocols) were measured using 15-item checklists. Fidelity scores were compared between the enhanced and standard arms by estimating mean differences and 95% CIs using generalized estimating equations.</p><p><strong>Results: </strong>We enrolled 306 lay HCWs, with 125 (40.8%) in the enhanced arm and 181 (59.2%) in the standard arm. In the enhanced arm, there was 100% completion of the digital portion, 98% (123/125) completion of the face-to-face tablet-guided portion, and 81% (101/125) to 93% (116/125) completion of quality improvement sessions. Knowledge improved by 4.4% (95% CI 0.7%-8.2%) more in the enhanced arm than in the standard arm (P=.02). Attitudes toward digital training improved by 4.5% (95% CI 0.3%-7.0%) more in the enhanced arm than in the standard arm (P=.03). Lay HCWs' fidelity to index client counseling protocols was 30.5 (95% CI 26.0%-35.0%; P<.001) percentage points higher in the enhanced arm than in the standard arm. Fidelity to contact client counseling protocols was 24.0 (95% CI 20.6%-27.3%; P<.001) percentage points higher in the enhanced arm than in the standard arm.</p><p><strong>Conclusions: </strong>Digital training improved lay HCWs' knowledge, attitudes, and skills surrounding index case testing counseling. These findings support digital training as a useful strategy for strengthening the capacity of lay HCWs in low-resource contexts.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e89942"},"PeriodicalIF":13.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457101","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":"Synchronous Remote Teaching for Neurological Examination Training in Fifth-Year Medical Students: Quasi-Experimental Study.","authors":"Chih-Shan Huang, Kung-Pei Tang, Ming-Shun Wu","doi":"10.2196/77034","DOIUrl":"10.2196/77034","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The use of remote teaching in medical education has increased since the COVID-19 pandemic. However, the effectiveness of synchronous remote teaching for specific psychomotor components of the neurological examination, such as tendon reflex assessment, remains underexplored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the short-term effectiveness of synchronous remote teaching compared with traditional in-person teaching for training medical students in tendon reflex examination skills and related neurological examination knowledge.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This quasi-experimental study enrolled 110 fifth-year medical students between August 2022 and April 2024, who were assigned to either synchronous remote teaching (n=46) or in-person teaching (n=64). The participants completed pre- and postcourse knowledge tests and an objective structured clinical examination (OSCE) assessing tendon reflex examination skills. Motivational beliefs related to self-regulated learning and technology acceptance were measured at baseline. Group differences were analyzed using t tests, Mann-Whitney U tests, and correlational analyses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Posttest knowledge scores were higher in the synchronous remote teaching group than in the in-person group (mean difference 0.86, 95% CI 0.21-1.51; Cohen d=0.50; P=.01), indicating a modest effect size. Both groups demonstrated substantial improvements in tendon reflex OSCE performance; however, the between-group difference in OSCE score improvement was not statistically significant (mean difference -3.64; 95% CI -9.36 to 2.08; Cohen d=-0.26; P=.21). Within the synchronous remote teaching group, higher motivational beliefs were positively correlated with OSCE score gains (Pearson r=0.34; P=.04; n=46), suggesting a potential role of self-regulated learning-related motivational processes. These exploratory findings should be interpreted with caution.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Under the conditions of this study, synchronous remote teaching was associated with modest improvements in knowledge outcomes and with OSCE-assessed tendon reflex examination skill gains that were comparable to those of traditional in-person teaching, without statistically significant between-group differences in psychomotor performance. These findings do not establish durable skill acquisition and should not be interpreted as evidence of equivalence or superiority of synchronous remote teaching over in-person instruction. Unlike many prior remote medical education studies that primarily focused on asynchronous learning or self-reported outcomes, this study incorporated synchronous interactive instruction with objective OSCE-based psychomotor assessment. These findings contribute to the emerging literature on synchronous remote clinical skills education, although future randomized and longitudinal studies are needed to evaluate long-term skill retention and broader app","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e77034"},"PeriodicalIF":13.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457099","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 National Exploration of Medical Students' Views Surrounding Skin Color Terminology in Undergraduate Education.","authors":"Niamh Theresa McSwiney, Eliza Hutchison, Stephanie Lax, Paul Leighton, Hannah Wainman","doi":"10.2196/92420","DOIUrl":"10.2196/92420","url":null,"abstract":"<p><strong>Unlabelled: </strong>We aimed to capture UK medical students' preferences regarding skin color terminology in medical education and in relation to how they describe their own skin tone, to better understand how we can develop more inclusive, diverse medical language for both educational and clinical settings.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":"12 ","pages":"e92420"},"PeriodicalIF":13.9,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449410","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}