Using Virtual Learning Environments to Improve the Quality and Availability of Clinical Examination Education for Medical Students

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Complete removal of medical students from clinical rotations and limitations on physical interactions drastically affected the in-person learning opportunities available and demanded innovation toward online education. Though COVID-19 restrictions have eased, limitations on in-person teaching will persist due to infection control measures, logistical difficulties, and with the expectation of other epidemics in the future (Marani et al., 2021). OSCE (Observed Structured Clinical Examination) Prep UK developed a virtual learning environment to characterise and mitigate the disruptive impact that the COVID-19 pandemic caused to medical education. The overarching goal was to provide an accessible, reproducible, and scalable platform to aid and enhance future medical student learning. Aim To assess and mitigate negative disruptions in undergraduate medical education caused by COVID-19, through a data driven interactive virtual learning environment. Methods Medical students completed online questionnaires detailing COVID-19 related training disruption. 29 hour-long interactive system-specific clinical examination tutorials were delivered with accompanying asynchronous online content. Pre-and-post session questionnaires assessed quantitative changes in knowledge and confidence, and qualitative perceptions of the value of the materials. Results 1693 participants completed the COVID-19 survey. >75% described reduced patient contact, bedside teaching, and clinical skills training. 69% reported university teaching gaps. <20% were satisfied with clinical skills training. 85% desired supplemental teaching. Over 689 attendances, >95% of participants, agreed that sessions: enhanced locally organised teaching; led to achieved learning outcomes and were relevant to their curriculum. Median increases in knowledge and confidence scores were 25% (IQR:0-60%, p<0.01) and 43% (IQR:23-81%, p<0.01) respectively. Discussion Medical students reported significant medical education disruptions and shortfalls due to COVID-19. OSCE Prep UK’s virtual learning environment was highly valued, in great demand and demonstrated significant improvements in student knowledge and confidence. This extremely scalable and reproducible educational model can continue to improve accessibility, efficiency, and efficacy of undergraduate medical education, particularly when physical resources are limited. Clinical e-Learning Online Medical SARS-COV2 Teaching Undergraduate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The COVID-19 pandemic significantly reduced in-person interaction, prompting the transition of many activities from the physical environment, into the virtual world (Ferguson et al., 2020 ; Mahase, 2020 ). Providing medical education normally delivered in-person, such as clinical examination training, was a particular challenge. This provided a unique opportunity for medical education to adapt and advance toward electronic learning. Online or electronic learning can be defined as the utilisation of online content to deliver, support and enhance both learning and teaching (Howlett et al., 2009 ). Virtual learning for medical students consists of case-based discussions, pre-recorded clinical scenarios, assignments, and webinars. Interactive clinical teaching is recognised as the most effective style of virtual teaching for enhancing knowledge, student engagement and positive feedback (Wilcha, 2020 ). Virtual learning has repeatedly been shown to offer advantages in flexibility and ease of access to electronic medical resources (Abi-Rafeh & Azzi, 2020 ; Sahi et al., 2020 ; Wilcha, 2020 ). Furthermore, it provides availability to teaching sessions from medical specialists irrespective of location and cost (Abi-Rafeh & Azzi, 2020 ; DEDEILIA et al., 2020 ), theoretically allowing for dissemination of leading medical education without geographical barriers. OSCE Prep UK applied these principles in developing virtual interactive clinical examination teaching sessions to enhance student knowledge and confidence, bridging the educational gap (Mian & Khan, 2020 ) with availability of virtual learning resources for medical students. Blooms taxonomy (Anderson & Sosniak, 1994 ) would suggest that the foundations of clinical examination are remembering components of various assessments, understanding examination techniques, acquiring knowledge on recognition of specific signs and interpretation of these to formulate differential diagnoses. These are pre-requisite capabilities for the successful evaluation of real patients, usually taught in-person on patients with or without relevant clinical signs. Paradoxically, the pandemic led to increased demand for effective clinical skills on the clinical frontline, with a simultaneous inadequacy of medical education, focused on acquiring and developing these skills which are pre-requisite to providing safe patientcare (DEDEILIA et al., 2020 ). Limitations on in-person training are likely to persist. Other epidemics are expected in future (Marani et al., 2021 ), and infection control aside, logistical factors can be restrictive. Identifying patients with relevant signs who are well enough and willing enough to be examined by multiple students is difficult and time consuming. Subsequent coordination of clinician, student and patient care requirements magnifies this problem. Increasing numbers of medical students trying to access clinical education in a progressively strained health service further compounds the problem (Medical-Schools-Council, 2021 .). Continuing to deliver all clinical examination training in-person will be increasingly resource intensive and will remain likely to be limited in accessibility, reproducibility, and scalability. Although virtually delivered content on clinical examination will never capture the visceral experience of real patient assessments, material on the subset of Bloom’s Taxonomy (Anderson & Sosniak, 1994 ) which is prerequisite to this may increase accessibility to education, alleviate pressures on resources, and give educators more time to spend on content which can only be delivered in person. Virtual learning environments The COVID-19 pandemic related disruption created an educational gap for medical students (Sahi et al., 2020 ). We aimed to characterise the disruptive impact of COVID-19 on undergraduate medical education, particularly clinical examination, using subjective feedback measures on competence and confidence in combination with an objective knowledge assessment. Development of a virtual learning environment aimed to mitigate the impact of this educational gap and evaluate its future potential in supporting traditional learning. Novel content tailored to student reported deficits was created and delivered via a decentralised, entirely online virtual learning environment, with synchronous interactive tutorials, and asynchronous accompanying materials. Continuous data driven methodology was employed to assess the effectiveness of the materials in achieving learning outcomes. Methods Setting and Participants ‘OSCE Prep UK’ ( OSCE Prep UK. An Interactive Online Virtual Learning Environment for Medical Education. , 2021.) was the recognised organisation undertaking this project that examined the impact of COVID-19 on undergraduate medical education and the effectiveness of virtually delivered clinical examination training. All data was collected between 20/01/2021-28/04/2021 and 18/01/2022-02/05/2022. 29 approximately hour-long sessions were delivered weekly within these dates. All content was accessible online, making this a virtual, entirely decentralised endeavour. Medical students of all stages and all UK universities were eligible to attend, with no specific inclusion criteria. The only exclusion criterion was incomplete participant data, for example from those who completed the pre-session questionnaire, but subsequently did not attend the session, or did not complete the post-session survey. Consent for responses to be stored, analysed then anonymised and used for research/quality improvement purposes was gained each time any questionnaire was completed. Design and Delivery (Fig. ) Google Forms was the chosen platform to deliver pre- and post-session questionnaires. Individuals are required to sign-in to a unique Google account to complete the questionnaires, ensuring participants did not complete multiple questionnaire entries and allowed for pre- and post-session knowledge score pairing and subsequent analysis. COVID-19 Questionnaire Opinions on the impact of COVID-19 were assessed via an online questionnaire. Participants answered 7 questions, designed to encapsulate opinions on teaching disruptions, satisfaction with university provided teaching and self-assessed competence. Answers were given on a discrete Likert scale (Jamieson, 2017 ). All responses were included in analyses. Before Each Session Based on the COVID-19 survey responses, educational content was tailored to improving clinical examination understanding, proficiency and performance, through delivery of a wide array of system specific content. This was advertised to medical students across the UK via social media and a mailing list. ‘OSCE Prep UK’ ( OSCE Prep UK. An Interactive Online Virtual Learning Environment for Medical Education. , 2021.) was the organisation name associated with all activity. Within the advertisement for each individual session was a link to complete an online pre-session questionnaire. This gathered basic demographics, assessed topic specific knowledge (using 10 single best answer questions), and collected self-reported measures of confidence. Confidence was assessed on scales from 1–10 over 4 domains: anatomy and physiology knowledge; capability of performing the examination; ability to identify signs and capability of interpreting those signs. Topic specific questions were vetted by senior clinicians in the respective specialty to increase validity and reliability. During Each Session Junior doctors delivered free, online, live and interactive system-specific clinical examination tutorials via Microsoft Teams (Microsoft, 2022 ). Complexity was aimed the level of a graduating medical student. Sessions followed a modified structure of Peyton’s 4 stage approach (Peyton, 1998 ). During the sessions, examination technique was demonstrated, followed by discussion of theory to facilitate knowledge, understanding and interpretation of relevant signs. Participants then executed the examination. Attendance in pairs within COVID-19 ‘bubbles’ was encouraged to allow more realistic examination. Comprehension was facilitated with time allocated to answer questions from within the session. After Each Session Participants were invited to complete online post-session questionnaires, which gathered feedback on the session through free text and 8 questions designed to convey perceived quality and satisfaction, answered on a discrete Likert scale (Jamieson, 2017 ). Knowledge and confidence were reassessed using the same questions as the pre-session questionnaire, then linked that participant’s pre-session scores to facilitate paired analyses. Each session was accompanied by asynchronous content to enrich the virtual learning environment. This consisted detailed explanations of multiple choice questions and a YouTube ( OSCE Prep UK. ) channel with original demonstration videos. Question answers were only made available after completion of post-session quizzes. Prior to delivery, all content created by junior doctors was vetted for accuracy and relevance by seniors in the associated specialty, increasing the validity and reliability of knowledge assessment scores. Statistical Analyses A sample of convenience was used. No a-priori power calculations were undertaken, but post-hoc analyses demonstrated a large effect size in knowledge and confidence, with statistically significant findings and adequate power. Demographic data were represented using simple descriptive statistics. Where Likert scales (Jamieson, 2017 ) were used, the proportion of each answer category was visually represented using a stacked bar chart. Confidence scores were summed then converted to percentages aid interpretation. Pre-and-post session knowledge and confidence score distributions were analysed using the Shapiro-Wilk test (SHAPIRO SS, 1965 ;52(3–4):591–611. ) and were non-normal. They were therefore described using medians, interquartile ranges, and boxplots, then analysed using the Wilcoxon-Signed Rank test for paired data. Themes occurring from free-text feedback were summarised post-hoc. Statistically significant p values were defined as < 0.05. All analyses and data visualisations were performed using R version 4.1.1 software ("R: A language and environment for statistical computing," 2017.; Wickham, 2009 ). Results Student Opinions on the Impact of COVID-19 on Medical Education 1693 participants responded to the survey on the impact of COVID-19 on undergraduate medical education. 84 (5%) from year 1, 272 (16%) from year 2, 461 (27%) from year 3, 485 (29%) from year 4, 328 (19%) from year 5, 43 (3%) from intercalated years, and 20 (1%) ‘Other healthcare professionals’ completed the survey. There were participant responses from 35/45 UK undergraduate medical schools. Graphic representations of these data are shown in Figs. 2 and 3 . Over 78% of survey participants agreed that the pandemic had reduced face-to-face clinical skills teaching (1336) and bedside teaching (1320). 69% (1169) agreed that this translated to a gap in university provided teaching. Only 19% (322) were satisfied with the clinical skills training they were receiving from their university and over 88% (1492) agreed that supplemental teaching would be beneficial. 32% felt they could perform (548) and interpret (522) the examinations expected of them at their level. The proportion of responses to these questions did not change significantly between 2021 and 2022. These data are reflected in Fig. 4 . Quantitative Effect of Sessions on Knowledge and Confidence 689 participants completed the pre-session survey, attended the session, then completed the post-session survey, and were therefore included in these analyses. 29 (4%) from year 1, 93 (13%) from year 2, 237 (34%) from year 3, 183 (27%) from year 4, 121 (18%) from year 5, 18 (3%) from intercalated years, and 8 (1%) ‘Other healthcare professionals’ attended. There was attendance from 28/45 UK undergraduate medical schools. An average of 24 participants attended each session, ranging from 13 at the lowest to 47 in a single live session at the highest. 63% (435) of students returned after their first session, and 14% of the attendances were from students attending 10 or more of the 16 unique sessions. Quantitative Impact of Sessions Individually, all but one session (Hip Examination, p = 0.41) resulted in statistically and clinically significant increases in knowledge scores (p < 0.01), and all sessions led to a statistically and clinically significant increase in measures of confidence (p < 0.01). Overall, the median increase in knowledge score was 25% (IQR: 0–60%, p < 0.01) and confidence score was 43% (IQR: 23–81%, p < 0.01). These data are expressed below in Table 1 and Fig. 5 . Table 1: Quiz and Confidence Score Changes for Students Who Attended the Sessions Feedback From discrete Likert scale (Jamieson, 2017 ) responses, aggregated across all sessions, > 95% of participants agreed that sessions: enhanced locally organised teaching; led to achieved learning outcomes; were relevant; were pitched at an appropriate level; were enjoyable and were recommendable to a friend. There were no outlier individual sessions that deviated significantly from these proportions. This is summarised below in Fig. 6 . Thematic analysis of free text responses showed frequent expressions of gratitude, compliments on the quality and utility of sessions, requests for future content and occasional technical/call quality issues. One student commented ‘This really helps to bridge the gap between theory and clinical practice which the medical school is struggling to address at the moment. Thanks again!’. Teachers were provided with outputs from their teaching session, detailing number of attendees, changes in knowledge and confidence, and student feedback. All tutors agreed they would include this in their professional development portfolios. Discussion Demographics There was diverse representation of year of study and university in our cohort of participants, increasing generalisability to the overall population of UK medical students. ‘Other healthcare professionals’ such as trainee advanced nurse practitioners and physicians’ associates, also engaged, and expressed a strong desire for supplemental teaching. This separate population was not specifically targeted in advertisement but represents another demographic to which materials could be delivered in future. The scalability of the methodology means expansion to more medical students and other allied health professionals both within and outside of the UK is completely achievable without significantly increased resource use. Disruptions in Undergraduate Medical Education Due to COVID-19 The COVID-19 pandemic posed an existential challenge on medical education (Ferrel & Ryan, 2020 ; Sahi et al., 2020 ). Complete removal of medical students from clinical rotations and limitations on physical interactions drastically affected the in-person learning opportunities available and demanded innovation toward online education (Mian & Khan, 2020 ). A UK wide survey reported 43% of final year OSCEs were cancelled, 60% of assistantships were cancelled or postponed and 77.3% of electives were cancelled (Choi et al., 2020 ). These disruptions resulted in a statistically significant impact to final year students’ preparedness for foundation training, where 59.3% of students felt less prepared to start foundation training than expected (Choi et al., 2020 ). This underpins the paradoxical position junior doctors faced, whereby increased demand for effective clinical skills and clinical knowledge on the medical frontline was met with a clear inadequacy in medical education. This COVID-19 related disruptive impact is similarly recognised in our study findings. The majority of students reported reduced clinical skills/bedside teaching, dissatisfaction with university provided clinical skills training, a desire for supplemental teaching and low self-assessed measures of confidence and competence. Interestingly, there were no significant differences in this data after stratifying by year and comparing responses from 2021 with 2022, indicating either an ongoing disruption from the pandemic, and/or a pre-existing perceived requirement for content supplementary to what is provided by undergraduate medical curricula. Continuing to monitor student satisfaction trends, desire for supplemental teaching and self-assessed competence may help explain if perceived curricula shortfalls were purely pandemic related or due to pre-existing unmet needs. Impact of the Provided Materials This programme did not intend to replace the experiential learning associated with real patient evaluations. Instead, it aimed to deliver effective virtual materials on content which is prerequisite (Anderson & Sosniak, 1994 ) to real patient evaluations. This was with the goal of bridging educational gaps in clinical examination training that have arisen due to restricted physical contact and resources (Hilburg et al., 2020 ). A systematic review on the effectiveness of virtual teaching during the pandemic (Wilcha, 2020 ) found significant student support of e-learning, comprising of; subjective improvement in communication skills, clinical knowledge, clinical skills and professional growth. Other studies brought forward concerns for limitation in developing student skills, such as history taking and physical examination, due to lack of in-person teaching (Hilburg et al., 2020 ; Mian & Khan, 2020 ). Other highlighted concerns included lack of student focus, technical difficulties, and challenges in monitoring student engagement (Kaup et al., 2020 ). Overall, e-learning has been widely reported as a satisfactory alternative to in-person teaching for medical students during the pandemic (Sud et al., 2020 ; Wilcha, 2020 ). Our study supported this, with participants reporting a high demand for supplemental teaching materials, statistically significant increases in knowledge and confidence, overwhelmingly positive feedback on the relevance and value of this virtually led education programme, and strong student retention. These results are convincing indicators that the aim to mitigate the disruptive impact of COVID-19 on medical education was achieved. Virtual programmes like this are reproducible and scalable, which are factors that can be leveraged to increase efficiency by orders of magnitude. This could increase accessibility to education, reduce resource strains, and free up educators to create more high yield virtual content, or spend more time on areas which can only be taught in person. Limitations Topic ‘Knowledge’ was distilled into 10 multiple choice questions. Although small sampling may inaccurately capture global comprehension, this was a pragmatic compromise to avoid potentially off-putting methods such as overly detailed questionnaires or cumbersome to analyse free-text responses. Multiple choice questions are a valid assessment method (Tangianu et al., 2018 ), are equally effective to short answer free-text questions in assessing higher cognitive domains (Khan & Aljarallah, 2011 ) and more readily enabled data analysis. Although increases in self-reported measures of confidence were observed, this may not correlate with increased competence in objective assessments (Morgan & Cleave-Hogg, 2002 ). Longer term information retention was not investigated and presents a pertinent topic for future study. Relevance of content could have been further optimised via more dialogue with curriculum providers. However, despite diverse demographic engagement, participants consistently reported high relevance and appropriateness of the level of the content. Standardising complexity of content to a shared final outcome of graduating medical school, having doctors who recently completed the curriculum create and deliver materials, and senior relevance vetting likely facilitated this. Applications to the Wider Teaching Community and Future Work This digital infrastructure collected real-time data on COVID-19’s impact on medical education, session attendance, discrete measures of perceived session value, and requests for new topics. This enabled flexible and responsive tailoring of content to mitigate unmet needs and match supply with demand. The systems developed could continue to monitor student needs and dynamically respond in future. Similar principles could be applied to other teaching programmes. Session efficacy was analysed with excellent confounder control by pairing pre-and-post intervention parameters (Gleichmann, 2020 .). Employing this data driven approach to assess session performance could facilitate continuous quality improvement, where high performing sessions are both prioritised in their delivery and examined to identify themes and techniques which could be applied to lower performing sessions. Sessions with consistently below average effectiveness could also simply be replaced with new content. Detailed data collection also enabled the provision of highly valued professional portfolio evidence containing tangible metrics for tutors. Other programmes could use this data driven methodology to increase tutor engagement, optimise content relevance and maximise effectiveness over time. Virtual learning environments provided a means to continue medical education during the pandemic and have shown significant advantages in convenience and ease of access to learning beyond the confines of space and time (Sahi et al., 2020 ; Wilcha, 2020 ). The reproducibility of asynchronous teaching content offers relaxation on traditional teaching demand within an increasingly strained health service (Medical-Schools-Council, 2021 .; O’Doherty et al., 2018 ). However, limitations in e-learning do exist. There are technical difficulties associated and virtual learning environments are unable to recreate the visceral experience of real life clinical teaching (Sahi et al., 2020 ); in-person teaching offers first-hand experience of clinical signs and symptoms as well as the exposure to the dynamics of patient interaction and counselling. The medical education field is increasingly recognising the benefit of a blended learning approach (Vallée et al., 2020 ). Blended learning is a combination of face-to-face and virtual learning, drawing from the advantages of both virtual and traditional teaching modalities, shown to significantly improve knowledge when compared to traditional teaching alone (Vallée et al., 2020 ). Our results strongly support the ongoing complementation of traditional teaching with virtual learning to mitigate educational gaps and improve student confidence and competence, specifically in developing clinical examination skills. Conclusion A high proportion of medical students reported that COVID-19 reduced clinical contact, leading to curricula deficits, dissatisfaction, low self-assessed measures of clinical examination confidence and competence, and a demand for supplemental teaching. The OSCE Prep UK virtual learning environment was in high demand from students across the entire UK, demonstrated statistically and clinically meaningful increases in knowledge and confidence, and received overwhelmingly positive feedback on the value it delivered in supplementing university provided teaching. Hands-on assessments of patients will continue to be the gold standard for clinical examination training. However, when physical accessibility or resources are restricted, this virtually delivered methodology, covering a sub-set of content that would normally be delivered in person, could be effectively reproduced at scale to facilitate increased efficacy and efficiency of medical education. The COVID-19 pandemic related disruption to medical education demanded innovative solutions to mitigate the educational gap created by the reduction of in-person teaching. Supportive evidence from our study, and the growing body of literature, highlight the applicability of virtual learning to enhance traditional teaching methods and improve accessibility to medical education. The recognised desire for supplemental teaching and demonstrated benefit within clinical skills teaching reveal the potential for virtual learning environments within other medical educational sub-sets. Implementing blended virtual and traditional learning methodology offers a promisingly effective approach for the future of medical education. Declarations Declarations The authors report no conflict of interest The standards of the Declaration of Helsinki were maintained throughout. There were no commercial associations with this project No sources of funding were associated with this project. Ethical Approval: NHS Research & Ethics Committee deemed no review of this project was required. Informed consent was gained for responses to be stored, analysed, anonymised, then used for research and quality improvement purposes each time data was collected. Author Contribution DH: Conception of research, Data acquisition, data analysis, data interpretation, reporting, reviewingMR: Literature review, data interpretation, reporting Acknowledgement Many thanks to the tutors listed, who helped create and deliver content. This work would not have been possible without them:Dr J Nicol, Dr R Thompson, Dr E Smellie, Dr E Halcrow, Dr P Hart, Dr Michael Ratcliffe,Dr D Broadhurst, Dr S Byrne, Dr D Borysov, Dr N Jayasuriya & Dr A Doughty Data Availability The datasets generated and analysed during the current study, including pre- and post-virtual learning session test scores, are available from the corresponding author on reasonable request. The data are not publicly available due to privacy and ethical considerations, as they contain educational performance information that could potentially identify individual medical students. Requests for data access should be directed to Michael Ratcliffe ( [email protected] ), and will be considered on a case-by-case basis subject to appropriate ethical approval and data sharing agreements to protect participant confidentiality. References Abi-Rafeh, J., & Azzi, A. J. (2020). Emerging role of online virtual teaching resources for medical student education in plastic surgery: COVID-19 pandemic and beyond. Journal of plastic, reconstructive & aesthetic surgery : JPRAS , 73 (8), 1575-1592. https://doi.org/10.1016/j.bjps.2020.05.085 Anderson, L. W., & Sosniak, L. A. (1994). Bloom's taxonomy . Univ. Chicago Press Chicago, IL, USA. Choi, B., Jegatheeswaran, L., Minocha, A., Alhilani, M., Nakhoul, M., & Mutengesa, E. (2020). The impact of the COVID-19 pandemic on final year medical students in the United Kingdom: a national survey. BMC Medical Education , 20 (1), 206. https://doi.org/10.1186/s12909-020-02117-1 DEDEILIA, A., SOTIROPOULOS, M. G., HANRAHAN, J. G., JANGA, D., DEDEILIAS, P., & SIDERIS, M. (2020). Medical and Surgical Education Challenges and Innovations in the COVID-19 Era: A Systematic Review. In Vivo , 34 (3 suppl), 1603-1611. https://doi.org/10.21873/invivo.11950 Ferguson, N., Laydon, D., & Nedjati-Gilani, G. (2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. In. Imperial College London. Ferrel, M. N., & Ryan, J. J. (2020). The impact of COVID-19 on medical education. Cureus , 12 (3). Gleichmann, N. (2020.). Paired vs Unpaired T-Test: Differences, Assumptions and Hypotheses. In. Hilburg, R., Patel, N., Ambruso, S., Biewald, M. A., & Farouk, S. S. (2020). Medical Education During the Coronavirus Disease-2019 Pandemic: Learning From a Distance. Advances in Chronic Kidney Disease , 27 (5), 412-417. https://doi.org/10.1053/j.ackd.2020.05.017 Howlett, D., Vincent, T., Gainsborough, N., Fairclough, J., Taylor, N., Cohen, J., & Vincent, R. (2009). Integration of a case-based online module into an undergraduate curriculum: what is involved and is it effective? E-learning and digital media , 6 (4), 372-384. Jamieson, S. (2017). Likert scale. In. Encyclopedia Britannica. Kaup, S., Jain, R., Shivalli, S., Pandey, S., & Kaup, S. (2020). Sustaining academics during COVID-19 pandemic: The role of online teaching-learning. Indian Journal of Ophthalmology , 68 (6), 1220-1221. https://doi.org/10.4103/ijo.IJO_1241_20 Khan, M. U., & Aljarallah, B. M. (2011). Evaluation of Modified Essay Questions (MEQ) and Multiple Choice Questions (MCQ) as a tool for Assessing the Cognitive Skills of Undergraduate Medical Students. Int J Health Sci (Qassim) , 5 (1), 39-43. Mahase, E. (2020). Covid-19: UK starts social distancing after new model points to 260 000 potential deaths. BMJ , 368 , m1089. https://doi.org/10.1136/bmj.m1089 Marani, M., Katul, G. G., Pan, W. K., & Parolari, A. J. (2021). Intensity and frequency of extreme novel epidemics. Proceedings of the National Academy of Sciences , 118 (35), e2105482118. https://doi.org/doi:10.1073/pnas.2105482118 Medical-Schools-Council. (2021.). The expansion of medical student numbers in the United Kingdom. In. London. Mian, A., & Khan, S. (2020). Medical education during pandemics: a UK perspective. BMC Medicine , 18 (1), 100. https://doi.org/10.1186/s12916-020-01577-y Microsoft. (2022). Microsoft Teams version 1.5. In. Morgan, P. J., & Cleave-Hogg, D. (2002). Comparison between medical students' experience, confidence and competence. Medical Education , 36 (6), 534-539. https://doi.org/https://doi.org/10.1046/j.1365-2923.2002.01228.x OSCE Prep UK. https://www.youtube.com/channel/UCbejPHqcmQmRd4yQhByHiDg] OSCE Prep UK. An Interactive Online Virtual Learning Environment for Medical Education. (2021.). https://www.facebook.com/OSCEPrepUK/] O’Doherty, D., Dromey, M., Lougheed, J., Hannigan, A., Last, J., & McGrath, D. (2018). Barriers and solutions to online learning in medical education – an integrative review. BMC Medical Education , 18 (1), 130. https://doi.org/10.1186/s12909-018-1240-0 Peyton, J. R. (1998). Teaching & learning in medical practice . Manticore Europe Limited. R: A language and environment for statistical computing. (2017.). In. Vienna, Austria. Sahi, P. K., Mishra, D., & Singh, T. (2020). Medical Education Amid the COVID-19 Pandemic. Indian Pediatrics , 57 (7), 652-657. https://doi.org/10.1007/s13312-020-1894-7 SHAPIRO SS, W. M. (1965;52(3–4):591–611. ). An analysis of variance test for normality (complete samples). . In. Sud, R., Sharma, P., Budhwar, V., & Khanduja, S. (2020). Undergraduate ophthalmology teaching in COVID-19 times: Students' perspective and feedback. Indian Journal of Ophthalmology , 68 (7), 1490-1491. https://doi.org/10.4103/ijo.IJO_1689_20 Tangianu, F., Mazzone, A., Berti, F., Pinna, G., Bortolotti, I., Colombo, F., . . . Nardi, R. (2018). Are multiple-choice questions a good tool for the assessment of clinical competence in Internal Medicine? Italian Journal of Medicine , 12 (2), 88-96. https://doi.org/10.4081/itjm.2018.980 Vallée, A., Blacher, J., Cariou, A., & Sorbets, E. (2020). Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis [Review]. J Med Internet Res , 22 (8), e16504. https://doi.org/10.2196/16504 Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. In: Springer-Verlag New York Wilcha, R.-J. (2020). Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review [Review]. JMIR Med Educ , 6 (2), e20963. https://doi.org/10.2196/20963 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Editor invited by journal 30 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8540692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591849756,"identity":"18a11042-1d87-4762-8b43-92a60a45d0e5","order_by":0,"name":"David Hewitt","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Hewitt","suffix":""},{"id":591849757,"identity":"08530987-ff63-48b7-8059-5c6fa1f9c156","order_by":1,"name":"Michael Ratcliffe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3QP0vDQBzG8V8JXJaUrE/oH1+BcCWQCgq+lTsCdXXMIHpFSBbBNeCb8C2Ew7oEuioukUInh4wOVTzTRSFNcXO473BcIJ/hOSKb7T/mEDPn0faj4mOf3LmiiqinugmaGwkeBsorFIkuQr8IGQJBneQwc9bVe4LxNFsWtTjn8IPVvBaJHilXP9y3kEiz6eSmRDgsYweCcwR38hqi1KHyZrOnVuIx9FPInGJmtmwu+YtMSaZaKnjRLhJ8fOIq91ffhOP0udhPBn0zGYi3hKO3j7BoMFxgkmMVNltQNlvOwnTXlqVeB28XJwfw5Wtdb8yLZY/mkhyPbl29aCMdsb/9brPZbLYffQHvaGBMu6dMtgAAAABJRU5ErkJggg==","orcid":"","institution":"Wishaw General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Ratcliffe","suffix":""}],"badges":[],"createdAt":"2026-01-07 11:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8540692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8540692/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049603,"identity":"b7cacdda-5e87-44b5-8e02-5a9205674eac","added_by":"auto","created_at":"2026-02-20 07:43:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVirtual Learning Environment Design\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/4fe092dfdb83f2ce5d77128f.png"},{"id":102862255,"identity":"4566d917-73f9-4d23-b179-b84f58e1e800","added_by":"auto","created_at":"2026-02-17 16:16:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDemographics of Participants who Responded to the Impact of COVID-19 on Undergraduate Medical Education Questionnaire\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/bf3919fa044d0d8e4b042ba9.png"},{"id":102862259,"identity":"1ebbf6f9-fbd9-4236-b7b7-148657f59b81","added_by":"auto","created_at":"2026-02-17 16:16:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eYear of Study of Participants who Responded to the Impact of COVID-19 on Undergraduate Medical Education Questionnaire\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/dc3a2eef4eaa50ebd157f248.png"},{"id":102963544,"identity":"e76e1831-7496-43b3-959e-dcff95ff1bab","added_by":"auto","created_at":"2026-02-19 04:18:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100339,"visible":true,"origin":"","legend":"\u003cp\u003eSurvey Responses on the Impact of COVID-19 on Medical Education\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/84d5afb4fe31d57949c78182.png"},{"id":102862257,"identity":"bebfaf3e-cfbc-41e1-8745-c23fd9bc4260","added_by":"auto","created_at":"2026-02-17 16:16:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132536,"visible":true,"origin":"","legend":"\u003cp\u003eQuiz and Confidence Score Changes for Students Who Attended the Sessions\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/bdbd0684d52183d35376beb6.png"},{"id":102963682,"identity":"97aad61d-f285-4075-94ef-83d5dce7e2d9","added_by":"auto","created_at":"2026-02-19 04:20:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":101485,"visible":true,"origin":"","legend":"\u003cp\u003eSession Feedback Responses\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/2a724268a253e240cb4e5e9d.png"},{"id":103051547,"identity":"9ed33af3-4873-44c8-8c65-b12c8921d00e","added_by":"auto","created_at":"2026-02-20 08:00:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1322342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8540692/v1/591e4147-bcd3-4881-bf93-050982b43353.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Virtual Learning Environments to Improve the Quality and Availability of Clinical Examination Education for Medical Students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic significantly reduced in-person interaction, prompting the transition of many activities from the physical environment, into the virtual world (Ferguson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mahase, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Providing medical education normally delivered in-person, such as clinical examination training, was a particular challenge. This provided a unique opportunity for medical education to adapt and advance toward electronic learning.\u003c/p\u003e \u003cp\u003eOnline or electronic learning can be defined as the utilisation of online content to deliver, support and enhance both learning and teaching (Howlett et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Virtual learning for medical students consists of case-based discussions, pre-recorded clinical scenarios, assignments, and webinars. Interactive clinical teaching is recognised as the most effective style of virtual teaching for enhancing knowledge, student engagement and positive feedback (Wilcha, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Virtual learning has repeatedly been shown to offer advantages in flexibility and ease of access to electronic medical resources (Abi-Rafeh \u0026amp; Azzi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sahi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wilcha, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, it provides availability to teaching sessions from medical specialists irrespective of location and cost (Abi-Rafeh \u0026amp; Azzi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; DEDEILIA et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), theoretically allowing for dissemination of leading medical education without geographical barriers.\u003c/p\u003e \u003cp\u003eOSCE Prep UK applied these principles in developing virtual interactive clinical examination teaching sessions to enhance student knowledge and confidence, bridging the educational gap (Mian \u0026amp; Khan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) with availability of virtual learning resources for medical students.\u003c/p\u003e \u003cp\u003eBlooms taxonomy (Anderson \u0026amp; Sosniak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) would suggest that the foundations of clinical examination are remembering components of various assessments, understanding examination techniques, acquiring knowledge on recognition of specific signs and interpretation of these to formulate differential diagnoses. These are pre-requisite capabilities for the successful evaluation of real patients, usually taught in-person on patients with or without relevant clinical signs.\u003c/p\u003e \u003cp\u003eParadoxically, the pandemic led to increased demand for effective clinical skills on the clinical frontline, with a simultaneous inadequacy of medical education, focused on acquiring and developing these skills which are pre-requisite to providing safe patientcare (DEDEILIA et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimitations on in-person training are likely to persist. Other epidemics are expected in future (Marani et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and infection control aside, logistical factors can be restrictive. Identifying patients with relevant signs who are well enough and willing enough to be examined by multiple students is difficult and time consuming. Subsequent coordination of clinician, student and patient care requirements magnifies this problem. Increasing numbers of medical students trying to access clinical education in a progressively strained health service further compounds the problem (Medical-Schools-Council, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.).\u003c/p\u003e \u003cp\u003eContinuing to deliver all clinical examination training in-person will be increasingly resource intensive and will remain likely to be limited in accessibility, reproducibility, and scalability. Although virtually delivered content on clinical examination will never capture the visceral experience of real patient assessments, material on the subset of Bloom’s Taxonomy (Anderson \u0026amp; Sosniak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) which is prerequisite to this may increase accessibility to education, alleviate pressures on resources, and give educators more time to spend on content which can only be delivered in person.\u003c/p\u003e\n\u003ch3\u003eVirtual learning environments\u003c/h3\u003e\n\u003cp\u003eThe COVID-19 pandemic related disruption created an educational gap for medical students (Sahi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We aimed to characterise the disruptive impact of COVID-19 on undergraduate medical education, particularly clinical examination, using subjective feedback measures on competence and confidence in combination with an objective knowledge assessment. Development of a virtual learning environment aimed to mitigate the impact of this educational gap and evaluate its future potential in supporting traditional learning. Novel content tailored to student reported deficits was created and delivered via a decentralised, entirely online virtual learning environment, with synchronous interactive tutorials, and asynchronous accompanying materials. Continuous data driven methodology was employed to assess the effectiveness of the materials in achieving learning outcomes.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eSetting and Participants\u003c/h2\u003e\u003cp\u003e‘OSCE Prep UK’ (\u003cem\u003eOSCE Prep UK. An Interactive Online Virtual Learning Environment for Medical Education.\u003c/em\u003e, 2021.) was the recognised organisation undertaking this project that examined the impact of COVID-19 on undergraduate medical education and the effectiveness of virtually delivered clinical examination training. All data was collected between 20/01/2021-28/04/2021 and 18/01/2022-02/05/2022. 29 approximately hour-long sessions were delivered weekly within these dates. All content was accessible online, making this a virtual, entirely decentralised endeavour.\u003c/p\u003e\u003cp\u003eMedical students of all stages and all UK universities were eligible to attend, with no specific inclusion criteria. The only exclusion criterion was incomplete participant data, for example from those who completed the pre-session questionnaire, but subsequently did not attend the session, or did not complete the post-session survey. Consent for responses to be stored, analysed then anonymised and used for research/quality improvement purposes was gained each time any questionnaire was completed.\u003c/p\u003e\n\u003ch3\u003eDesign and Delivery (Fig. )\u003c/h3\u003e\n\u003cp\u003eGoogle Forms was the chosen platform to deliver pre- and post-session questionnaires. Individuals are required to sign-in to a unique Google account to complete the questionnaires, ensuring participants did not complete multiple questionnaire entries and allowed for pre- and post-session knowledge score pairing and subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eCOVID-19 Questionnaire\u003c/h3\u003e\n\u003cp\u003eOpinions on the impact of COVID-19 were assessed via an online questionnaire. Participants answered 7 questions, designed to encapsulate opinions on teaching disruptions, satisfaction with university provided teaching and self-assessed competence. Answers were given on a discrete Likert scale (Jamieson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All responses were included in analyses.\u003c/p\u003e\n\u003ch3\u003eBefore Each Session\u003c/h3\u003e\n\u003cp\u003eBased on the COVID-19 survey responses, educational content was tailored to improving clinical examination understanding, proficiency and performance, through delivery of a wide array of system specific content. This was advertised to medical students across the UK via social media and a mailing list. \u0026lsquo;OSCE Prep UK\u0026rsquo; (\u003cem\u003eOSCE Prep UK. An Interactive Online Virtual Learning Environment for Medical Education.\u003c/em\u003e, 2021.) was the organisation name associated with all activity. Within the advertisement for each individual session was a link to complete an online pre-session questionnaire. This gathered basic demographics, assessed topic specific knowledge (using 10 single best answer questions), and collected self-reported measures of confidence. Confidence was assessed on scales from 1\u0026ndash;10 over 4 domains: anatomy and physiology knowledge; capability of performing the examination; ability to identify signs and capability of interpreting those signs. Topic specific questions were vetted by senior clinicians in the respective specialty to increase validity and reliability.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDuring Each Session\u003c/h2\u003e \u003cp\u003eJunior doctors delivered free, online, live and interactive system-specific clinical examination tutorials via Microsoft Teams (Microsoft, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Complexity was aimed the level of a graduating medical student. Sessions followed a modified structure of Peyton\u0026rsquo;s 4 stage approach (Peyton, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). During the sessions, examination technique was demonstrated, followed by discussion of theory to facilitate knowledge, understanding and interpretation of relevant signs. Participants then executed the examination. Attendance in pairs within COVID-19 \u0026lsquo;bubbles\u0026rsquo; was encouraged to allow more realistic examination. Comprehension was facilitated with time allocated to answer questions from within the session.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAfter Each Session\u003c/h3\u003e\n\u003cp\u003eParticipants were invited to complete online post-session questionnaires, which gathered feedback on the session through free text and 8 questions designed to convey perceived quality and satisfaction, answered on a discrete Likert scale (Jamieson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Knowledge and confidence were reassessed using the same questions as the pre-session questionnaire, then linked that participant\u0026rsquo;s pre-session scores to facilitate paired analyses.\u003c/p\u003e \u003cp\u003eEach session was accompanied by asynchronous content to enrich the virtual learning environment. This consisted detailed explanations of multiple choice questions and a YouTube (\u003cem\u003eOSCE Prep UK.\u003c/em\u003e) channel with original demonstration videos. Question answers were only made available after completion of post-session quizzes. Prior to delivery, all content created by junior doctors was vetted for accuracy and relevance by seniors in the associated specialty, increasing the validity and reliability of knowledge assessment scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eA sample of convenience was used. No a-priori power calculations were undertaken, but post-hoc analyses demonstrated a large effect size in knowledge and confidence, with statistically significant findings and adequate power. Demographic data were represented using simple descriptive statistics. Where Likert scales (Jamieson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used, the proportion of each answer category was visually represented using a stacked bar chart. Confidence scores were summed then converted to percentages aid interpretation. Pre-and-post session knowledge and confidence score distributions were analysed using the Shapiro-Wilk test (SHAPIRO SS, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1965\u003c/span\u003e;52(3\u0026ndash;4):591\u0026ndash;611. ) and were non-normal. They were therefore described using medians, interquartile ranges, and boxplots, then analysed using the Wilcoxon-Signed Rank test for paired data. Themes occurring from free-text feedback were summarised post-hoc. Statistically significant p values were defined as \u0026lt;\u0026thinsp;0.05. All analyses and data visualisations were performed using R version 4.1.1 software (\"R: A language and environment for statistical computing,\" 2017.; Wickham, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudent Opinions on the Impact of COVID-19 on Medical Education\u003c/h2\u003e \u003cp\u003e1693 participants responded to the survey on the impact of COVID-19 on undergraduate medical education. 84 (5%) from year 1, 272 (16%) from year 2, 461 (27%) from year 3, 485 (29%) from year 4, 328 (19%) from year 5, 43 (3%) from intercalated years, and 20 (1%) \u0026lsquo;Other healthcare professionals\u0026rsquo; completed the survey. There were participant responses from 35/45 UK undergraduate medical schools. Graphic representations of these data are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOver 78% of survey participants agreed that the pandemic had reduced face-to-face clinical skills teaching (1336) and bedside teaching (1320). 69% (1169) agreed that this translated to a gap in university provided teaching. Only 19% (322) were satisfied with the clinical skills training they were receiving from their university and over 88% (1492) agreed that supplemental teaching would be beneficial. 32% felt they could perform (548) and interpret (522) the examinations expected of them at their level. The proportion of responses to these questions did not change significantly between 2021 and 2022. These data are reflected in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Effect of Sessions on Knowledge and Confidence\u003c/h2\u003e \u003cp\u003e689 participants completed the pre-session survey, attended the session, then completed the post-session survey, and were therefore included in these analyses. 29 (4%) from year 1, 93 (13%) from year 2, 237 (34%) from year 3, 183 (27%) from year 4, 121 (18%) from year 5, 18 (3%) from intercalated years, and 8 (1%) \u0026lsquo;Other healthcare professionals\u0026rsquo; attended. There was attendance from 28/45 UK undergraduate medical schools. An average of 24 participants attended each session, ranging from 13 at the lowest to 47 in a single live session at the highest. 63% (435) of students returned after their first session, and 14% of the attendances were from students attending 10 or more of the 16 unique sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Impact of Sessions\u003c/h2\u003e \u003cp\u003eIndividually, all but one session (Hip Examination, p\u0026thinsp;=\u0026thinsp;0.41) resulted in statistically and clinically significant increases in knowledge scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and all sessions led to a statistically and clinically significant increase in measures of confidence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Overall, the median increase in knowledge score was 25% (IQR: 0\u0026ndash;60%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and confidence score was 43% (IQR: 23\u0026ndash;81%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These data are expressed below in Table\u0026nbsp;1 and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;1: Quiz and Confidence Score Changes for Students Who Attended the Sessions\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003cimg 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\" width=\"746\" height=\"730\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFeedback\u003c/h2\u003e \u003cp\u003eFrom discrete Likert scale (Jamieson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) responses, aggregated across all sessions, \u0026gt;\u0026thinsp;95% of participants agreed that sessions: enhanced locally organised teaching; led to achieved learning outcomes; were relevant; were pitched at an appropriate level; were enjoyable and were recommendable to a friend. There were no outlier individual sessions that deviated significantly from these proportions. This is summarised below in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThematic analysis of free text responses showed frequent expressions of gratitude, compliments on the quality and utility of sessions, requests for future content and occasional technical/call quality issues. One student commented \u0026lsquo;This really helps to bridge the gap between theory and clinical practice which the medical school is struggling to address at the moment. Thanks again!\u0026rsquo;.\u003c/p\u003e \u003cp\u003eTeachers were provided with outputs from their teaching session, detailing number of attendees, changes in knowledge and confidence, and student feedback. All tutors agreed they would include this in their professional development portfolios.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDemographics\u003c/h2\u003e \u003cp\u003eThere was diverse representation of year of study and university in our cohort of participants, increasing generalisability to the overall population of UK medical students. \u0026lsquo;Other healthcare professionals\u0026rsquo; such as trainee advanced nurse practitioners and physicians\u0026rsquo; associates, also engaged, and expressed a strong desire for supplemental teaching. This separate population was not specifically targeted in advertisement but represents another demographic to which materials could be delivered in future. The scalability of the methodology means expansion to more medical students and other allied health professionals both within and outside of the UK is completely achievable without significantly increased resource use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDisruptions in Undergraduate Medical Education Due to COVID-19\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic posed an existential challenge on medical education (Ferrel \u0026amp; Ryan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sahi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Complete removal of medical students from clinical rotations and limitations on physical interactions drastically affected the in-person learning opportunities available and demanded innovation toward online education (Mian \u0026amp; Khan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A UK wide survey reported 43% of final year OSCEs were cancelled, 60% of assistantships were cancelled or postponed and 77.3% of electives were cancelled (Choi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These disruptions resulted in a statistically significant impact to final year students\u0026rsquo; preparedness for foundation training, where 59.3% of students felt less prepared to start foundation training than expected (Choi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This underpins the paradoxical position junior doctors faced, whereby increased demand for effective clinical skills and clinical knowledge on the medical frontline was met with a clear inadequacy in medical education.\u003c/p\u003e \u003cp\u003eThis COVID-19 related disruptive impact is similarly recognised in our study findings. The majority of students reported reduced clinical skills/bedside teaching, dissatisfaction with university provided clinical skills training, a desire for supplemental teaching and low self-assessed measures of confidence and competence. Interestingly, there were no significant differences in this data after stratifying by year and comparing responses from 2021 with 2022, indicating either an ongoing disruption from the pandemic, and/or a pre-existing perceived requirement for content supplementary to what is provided by undergraduate medical curricula. Continuing to monitor student satisfaction trends, desire for supplemental teaching and self-assessed competence may help explain if perceived curricula shortfalls were purely pandemic related or due to pre-existing unmet needs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImpact of the Provided Materials\u003c/h2\u003e \u003cp\u003eThis programme did not intend to replace the experiential learning associated with real patient evaluations. Instead, it aimed to deliver effective virtual materials on content which is prerequisite (Anderson \u0026amp; Sosniak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) to real patient evaluations. This was with the goal of bridging educational gaps in clinical examination training that have arisen due to restricted physical contact and resources (Hilburg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA systematic review on the effectiveness of virtual teaching during the pandemic (Wilcha, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found significant student support of e-learning, comprising of; subjective improvement in communication skills, clinical knowledge, clinical skills and professional growth. Other studies brought forward concerns for limitation in developing student skills, such as history taking and physical examination, due to lack of in-person teaching (Hilburg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mian \u0026amp; Khan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Other highlighted concerns included lack of student focus, technical difficulties, and challenges in monitoring student engagement (Kaup et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, e-learning has been widely reported as a satisfactory alternative to in-person teaching for medical students during the pandemic (Sud et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wilcha, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our study supported this, with participants reporting a high demand for supplemental teaching materials, statistically significant increases in knowledge and confidence, overwhelmingly positive feedback on the relevance and value of this virtually led education programme, and strong student retention. These results are convincing indicators that the aim to mitigate the disruptive impact of COVID-19 on medical education was achieved. Virtual programmes like this are reproducible and scalable, which are factors that can be leveraged to increase efficiency by orders of magnitude. This could increase accessibility to education, reduce resource strains, and free up educators to create more high yield virtual content, or spend more time on areas which can only be taught in person.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eTopic \u0026lsquo;Knowledge\u0026rsquo; was distilled into 10 multiple choice questions. Although small sampling may inaccurately capture global comprehension, this was a pragmatic compromise to avoid potentially off-putting methods such as overly detailed questionnaires or cumbersome to analyse free-text responses. Multiple choice questions are a valid assessment method (Tangianu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), are equally effective to short answer free-text questions in assessing higher cognitive domains (Khan \u0026amp; Aljarallah, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and more readily enabled data analysis. Although increases in self-reported measures of confidence were observed, this may not correlate with increased competence in objective assessments (Morgan \u0026amp; Cleave-Hogg, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Longer term information retention was not investigated and presents a pertinent topic for future study. Relevance of content could have been further optimised via more dialogue with curriculum providers. However, despite diverse demographic engagement, participants consistently reported high relevance and appropriateness of the level of the content. Standardising complexity of content to a shared final outcome of graduating medical school, having doctors who recently completed the curriculum create and deliver materials, and senior relevance vetting likely facilitated this.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eApplications to the Wider Teaching Community and Future Work\u003c/h2\u003e \u003cp\u003eThis digital infrastructure collected real-time data on COVID-19\u0026rsquo;s impact on medical education, session attendance, discrete measures of perceived session value, and requests for new topics. This enabled flexible and responsive tailoring of content to mitigate unmet needs and match supply with demand. The systems developed could continue to monitor student needs and dynamically respond in future. Similar principles could be applied to other teaching programmes.\u003c/p\u003e \u003cp\u003eSession efficacy was analysed with excellent confounder control by pairing pre-and-post intervention parameters (Gleichmann, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e.). Employing this data driven approach to assess session performance could facilitate continuous quality improvement, where high performing sessions are both prioritised in their delivery and examined to identify themes and techniques which could be applied to lower performing sessions. Sessions with consistently below average effectiveness could also simply be replaced with new content. Detailed data collection also enabled the provision of highly valued professional portfolio evidence containing tangible metrics for tutors. Other programmes could use this data driven methodology to increase tutor engagement, optimise content relevance and maximise effectiveness over time.\u003c/p\u003e \u003cp\u003eVirtual learning environments provided a means to continue medical education during the pandemic and have shown significant advantages in convenience and ease of access to learning beyond the confines of space and time (Sahi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wilcha, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The reproducibility of asynchronous teaching content offers relaxation on traditional teaching demand within an increasingly strained health service (Medical-Schools-Council, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e.; O\u0026rsquo;Doherty et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, limitations in e-learning do exist. There are technical difficulties associated and virtual learning environments are unable to recreate the visceral experience of real life clinical teaching (Sahi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); in-person teaching offers first-hand experience of clinical signs and symptoms as well as the exposure to the dynamics of patient interaction and counselling.\u003c/p\u003e \u003cp\u003eThe medical education field is increasingly recognising the benefit of a blended learning approach (Vall\u0026eacute;e et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Blended learning is a combination of face-to-face and virtual learning, drawing from the advantages of both virtual and traditional teaching modalities, shown to significantly improve knowledge when compared to traditional teaching alone (Vall\u0026eacute;e et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our results strongly support the ongoing complementation of traditional teaching with virtual learning to mitigate educational gaps and improve student confidence and competence, specifically in developing clinical examination skills.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA high proportion of medical students reported that COVID-19 reduced clinical contact, leading to curricula deficits, dissatisfaction, low self-assessed measures of clinical examination confidence and competence, and a demand for supplemental teaching. The OSCE Prep UK virtual learning environment was in high demand from students across the entire UK, demonstrated statistically and clinically meaningful increases in knowledge and confidence, and received overwhelmingly positive feedback on the value it delivered in supplementing university provided teaching. Hands-on assessments of patients will continue to be the gold standard for clinical examination training. However, when physical accessibility or resources are restricted, this virtually delivered methodology, covering a sub-set of content that would normally be delivered in person, could be effectively reproduced at scale to facilitate increased efficacy and efficiency of medical education.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic related disruption to medical education demanded innovative solutions to mitigate the educational gap created by the reduction of in-person teaching. Supportive evidence from our study, and the growing body of literature, highlight the applicability of virtual learning to enhance traditional teaching methods and improve accessibility to medical education. The recognised desire for supplemental teaching and demonstrated benefit within clinical skills teaching reveal the potential for virtual learning environments within other medical educational sub-sets. Implementing blended virtual and traditional learning methodology offers a promisingly effective approach for the future of medical education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003eThe authors report no conflict of interest\u003c/p\u003e \u003cp\u003eThe standards of the Declaration of Helsinki were maintained throughout.\u003c/p\u003e \u003cp\u003eThere were no commercial associations with this project\u003c/p\u003e \u003cp\u003eNo sources of funding were associated with this project.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval:\u003c/strong\u003e \u003cp\u003eNHS Research \u0026amp; Ethics Committee deemed no review of this project was required. Informed consent was gained for responses to be stored, analysed, anonymised, then used for research and quality improvement purposes each time data was collected.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDH: Conception of research, Data acquisition, data analysis, data interpretation, reporting, reviewingMR: Literature review, data interpretation, reporting\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eMany thanks to the tutors listed, who helped create and deliver content. This work would not have been possible without them:Dr J Nicol, Dr R Thompson, Dr E Smellie, Dr E Halcrow, Dr P Hart, Dr Michael Ratcliffe,Dr D Broadhurst, Dr S Byrne, Dr D Borysov, Dr N Jayasuriya \u0026amp; Dr A Doughty\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study, including pre- and post-virtual learning session test scores, are available from the corresponding author on reasonable request. The data are not publicly available due to privacy and ethical considerations, as they contain educational performance information that could potentially identify individual medical students. Requests for data access should be directed to Michael Ratcliffe ([email protected]), and will be considered on a case-by-case basis subject to appropriate ethical approval and data sharing agreements to protect participant confidentiality.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbi-Rafeh, J., \u0026amp; Azzi, A. J. (2020). Emerging role of online virtual teaching resources for medical student education in plastic surgery: COVID-19 pandemic and beyond. \u003cem\u003eJournal of plastic, reconstructive \u0026amp; aesthetic surgery : JPRAS\u003c/em\u003e,\u003cem\u003e 73\u003c/em\u003e(8), 1575-1592. https://doi.org/10.1016/j.bjps.2020.05.085 \u003c/li\u003e\n\u003cli\u003eAnderson, L. W., \u0026amp; Sosniak, L. A. (1994). \u003cem\u003eBloom\u0026apos;s taxonomy\u003c/em\u003e. Univ. Chicago Press Chicago, IL, USA. \u003c/li\u003e\n\u003cli\u003eChoi, B., Jegatheeswaran, L., Minocha, A., Alhilani, M., Nakhoul, M., \u0026amp; Mutengesa, E. (2020). The impact of the COVID-19 pandemic on final year medical students in the United Kingdom: a national survey. \u003cem\u003eBMC Medical Education\u003c/em\u003e,\u003cem\u003e 20\u003c/em\u003e(1), 206. https://doi.org/10.1186/s12909-020-02117-1 \u003c/li\u003e\n\u003cli\u003eDEDEILIA, A., SOTIROPOULOS, M. G., HANRAHAN, J. G., JANGA, D., DEDEILIAS, P., \u0026amp; SIDERIS, M. (2020). Medical and Surgical Education Challenges and Innovations in the COVID-19 Era: A Systematic Review. \u003cem\u003eIn Vivo\u003c/em\u003e,\u003cem\u003e 34\u003c/em\u003e(3 suppl), 1603-1611. https://doi.org/10.21873/invivo.11950 \u003c/li\u003e\n\u003cli\u003eFerguson, N., Laydon, D., \u0026amp; Nedjati-Gilani, G. (2020). Impact of non-pharmaceutical interventions (NPIs)\u003c/li\u003e\n\u003cli\u003eto reduce COVID-19 mortality and healthcare demand. In. Imperial College London.\u003c/li\u003e\n\u003cli\u003eFerrel, M. N., \u0026amp; Ryan, J. J. (2020). The impact of COVID-19 on medical education. \u003cem\u003eCureus\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(3). \u003c/li\u003e\n\u003cli\u003eGleichmann, N. (2020.). Paired vs Unpaired T-Test: Differences, Assumptions and Hypotheses. In.\u003c/li\u003e\n\u003cli\u003eHilburg, R., Patel, N., Ambruso, S., Biewald, M. A., \u0026amp; Farouk, S. S. (2020). Medical Education During the Coronavirus Disease-2019 Pandemic: Learning From a Distance. \u003cem\u003eAdvances in Chronic Kidney Disease\u003c/em\u003e,\u003cem\u003e 27\u003c/em\u003e(5), 412-417. https://doi.org/10.1053/j.ackd.2020.05.017 \u003c/li\u003e\n\u003cli\u003eHowlett, D., Vincent, T., Gainsborough, N., Fairclough, J., Taylor, N., Cohen, J., \u0026amp; Vincent, R. (2009). Integration of a case-based online module into an undergraduate curriculum: what is involved and is it effective? \u003cem\u003eE-learning and digital media\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e(4), 372-384. \u003c/li\u003e\n\u003cli\u003eJamieson, S. (2017). Likert scale. In. Encyclopedia Britannica.\u003c/li\u003e\n\u003cli\u003eKaup, S., Jain, R., Shivalli, S., Pandey, S., \u0026amp; Kaup, S. (2020). Sustaining academics during COVID-19 pandemic: The role of online teaching-learning. \u003cem\u003eIndian Journal of Ophthalmology\u003c/em\u003e,\u003cem\u003e 68\u003c/em\u003e(6), 1220-1221. https://doi.org/10.4103/ijo.IJO_1241_20 \u003c/li\u003e\n\u003cli\u003eKhan, M. U., \u0026amp; Aljarallah, B. M. (2011). Evaluation of Modified Essay Questions (MEQ) and Multiple Choice Questions (MCQ) as a tool for Assessing the Cognitive Skills of Undergraduate Medical Students. \u003cem\u003eInt J Health Sci (Qassim)\u003c/em\u003e,\u003cem\u003e 5\u003c/em\u003e(1), 39-43. \u003c/li\u003e\n\u003cli\u003eMahase, E. (2020). Covid-19: UK starts social distancing after new model points to 260\u0026thinsp;000 potential deaths. \u003cem\u003eBMJ\u003c/em\u003e,\u003cem\u003e 368\u003c/em\u003e, m1089. https://doi.org/10.1136/bmj.m1089 \u003c/li\u003e\n\u003cli\u003eMarani, M., Katul, G. G., Pan, W. K., \u0026amp; Parolari, A. J. (2021). Intensity and frequency of extreme novel epidemics. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e,\u003cem\u003e 118\u003c/em\u003e(35), e2105482118. https://doi.org/doi:10.1073/pnas.2105482118 \u003c/li\u003e\n\u003cli\u003eMedical-Schools-Council. (2021.). The expansion of medical student numbers in the United Kingdom. In. London.\u003c/li\u003e\n\u003cli\u003eMian, A., \u0026amp; Khan, S. (2020). Medical education during pandemics: a UK perspective. \u003cem\u003eBMC Medicine\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(1), 100. https://doi.org/10.1186/s12916-020-01577-y \u003c/li\u003e\n\u003cli\u003eMicrosoft. (2022). Microsoft Teams version 1.5. In.\u003c/li\u003e\n\u003cli\u003eMorgan, P. J., \u0026amp; Cleave-Hogg, D. (2002). Comparison between medical students\u0026apos; experience, confidence and competence. \u003cem\u003eMedical Education\u003c/em\u003e,\u003cem\u003e 36\u003c/em\u003e(6), 534-539. https://doi.org/https://doi.org/10.1046/j.1365-2923.2002.01228.x \u003c/li\u003e\n\u003cli\u003e\u003cem\u003eOSCE Prep UK.\u003c/em\u003e https://www.youtube.com/channel/UCbejPHqcmQmRd4yQhByHiDg]\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eOSCE Prep UK. An Interactive Online Virtual Learning Environment for Medical Education.\u003c/em\u003e (2021.). https://www.facebook.com/OSCEPrepUK/]\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Doherty, D., Dromey, M., Lougheed, J., Hannigan, A., Last, J., \u0026amp; McGrath, D. (2018). Barriers and solutions to online learning in medical education \u0026ndash; an integrative review. \u003cem\u003eBMC Medical Education\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e(1), 130. https://doi.org/10.1186/s12909-018-1240-0 \u003c/li\u003e\n\u003cli\u003ePeyton, J. R. (1998). \u003cem\u003eTeaching \u0026amp; learning in medical practice\u003c/em\u003e. Manticore Europe Limited. \u003c/li\u003e\n\u003cli\u003eR: A language and environment for statistical computing. (2017.). In. Vienna, Austria.\u003c/li\u003e\n\u003cli\u003eSahi, P. K., Mishra, D., \u0026amp; Singh, T. (2020). Medical Education Amid the COVID-19 Pandemic. \u003cem\u003eIndian Pediatrics\u003c/em\u003e,\u003cem\u003e 57\u003c/em\u003e(7), 652-657. https://doi.org/10.1007/s13312-020-1894-7 \u003c/li\u003e\n\u003cli\u003eSHAPIRO SS, W. M. (1965;52(3\u0026ndash;4):591\u0026ndash;611. ). An analysis of variance test for normality (complete samples). . In.\u003c/li\u003e\n\u003cli\u003eSud, R., Sharma, P., Budhwar, V., \u0026amp; Khanduja, S. (2020). Undergraduate ophthalmology teaching in COVID-19 times: Students\u0026apos; perspective and feedback. \u003cem\u003eIndian Journal of Ophthalmology\u003c/em\u003e,\u003cem\u003e 68\u003c/em\u003e(7), 1490-1491. https://doi.org/10.4103/ijo.IJO_1689_20 \u003c/li\u003e\n\u003cli\u003eTangianu, F., Mazzone, A., Berti, F., Pinna, G., Bortolotti, I., Colombo, F., . . . Nardi, R. (2018). Are multiple-choice questions a good tool for the assessment of clinical competence in Internal Medicine? \u003cem\u003eItalian Journal of Medicine\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(2), 88-96. https://doi.org/10.4081/itjm.2018.980 \u003c/li\u003e\n\u003cli\u003eVall\u0026eacute;e, A., Blacher, J., Cariou, A., \u0026amp; Sorbets, E. (2020). Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis [Review]. \u003cem\u003eJ Med Internet Res\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(8), e16504. https://doi.org/10.2196/16504 \u003c/li\u003e\n\u003cli\u003eWickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. In: Springer-Verlag New York \u003c/li\u003e\n\u003cli\u003eWilcha, R.-J. (2020). Effectiveness of Virtual Medical Teaching During the COVID-19 Crisis: Systematic Review [Review]. \u003cem\u003eJMIR Med Educ\u003c/em\u003e,\u003cem\u003e 6\u003c/em\u003e(2), e20963. https://doi.org/10.2196/20963 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinical, e-Learning, Online, Medical, SARS-COV2, Teaching, Undergraduate","lastPublishedDoi":"10.21203/rs.3.rs-8540692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8540692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe COVID-19 pandemic posed an existential challenge on medical education (Ferrel \u0026amp; Ryan, 2020; Sahi et al., 2020). Complete removal of medical students from clinical rotations and limitations on physical interactions drastically affected the in-person learning opportunities available and demanded innovation toward online education. Though COVID-19 restrictions have eased, limitations on in-person teaching will persist due to infection control measures, logistical difficulties, and with the expectation of other epidemics in the future (Marani et al., 2021).\u003c/p\u003e\n\u003cp\u003eOSCE (Observed Structured Clinical Examination) Prep UK developed a virtual learning environment to characterise and mitigate the disruptive impact that the COVID-19 pandemic caused to medical education. The overarching goal was to provide an accessible, reproducible, and scalable platform to aid and enhance future medical student learning.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess and mitigate negative disruptions in undergraduate medical education caused by COVID-19, through a data driven interactive virtual learning environment.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMedical students completed online questionnaires detailing COVID-19 related training disruption. 29 hour-long interactive system-specific clinical examination tutorials were delivered with accompanying asynchronous online content. Pre-and-post session questionnaires assessed quantitative changes in knowledge and confidence, and qualitative perceptions of the value of the materials.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e1693 participants completed the COVID-19 survey. \u0026gt;75% described reduced patient contact, bedside teaching, and clinical skills training. 69% reported university teaching gaps. \u0026lt;20% were satisfied with clinical skills training. \u0026lt;35% felt competent performing expected clinical examinations. \u0026gt;85% desired supplemental teaching.\u003c/p\u003e\n\u003cp\u003eOver 689 attendances, \u0026gt;95% of participants, agreed that sessions: enhanced locally organised teaching; led to achieved learning outcomes and were relevant to their curriculum. Median increases in knowledge and confidence scores were 25% (IQR:0-60%, p\u0026lt;0.01) and 43% (IQR:23-81%, p\u0026lt;0.01) respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMedical students reported significant medical education disruptions and shortfalls due to COVID-19. OSCE Prep UK’s virtual learning environment was highly valued, in great demand and demonstrated significant improvements in student knowledge and confidence. This extremely scalable and reproducible educational model can continue to improve accessibility, efficiency, and efficacy of undergraduate medical education, particularly when physical resources are limited.\u003c/p\u003e","manuscriptTitle":"Using Virtual Learning Environments to Improve the Quality and Availability of Clinical Examination Education for Medical Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 16:16:23","doi":"10.21203/rs.3.rs-8540692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T15:22:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T21:12:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T10:22:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222077204611898777194657901351082602883","date":"2026-02-22T07:25:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284841384560558998550506294378283011813","date":"2026-02-18T11:29:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T02:01:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T02:00:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-30T11:44:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T20:27:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-01-29T20:22:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4d58431-9775-445f-9719-6f9f8dcf36a3","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T02:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 16:16:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8540692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8540692","identity":"rs-8540692","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}