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Objectives: Concept maps are a learning tool that fosters clinical reasoning skills in healthcare education. They can be developed by students in combination with virtual patients to create a visual representation of the clinical reasoning process while solving a case. However, in order to optimize feedback, there is a need to better understand the role of connections between concepts in student-generated maps. Therefore, in this study we investigated whether the quality of these connections is indicative of diagnostic accuracy.Methods: We analyzed 40 concept maps created by fifth-year medical students in the context of four virtual patients with commonly encountered diagnoses. Half of the maps were created by students who made a correct diagnosis on the first attempt; the other half were created by students who made an error in their first diagnosis. The connections in the maps were rated by two reviewers using a relational scoring system. Analysis of covariance was employed to examine the difference in mean connection scores among groups while controlling for the number of connections.Results: There were no differences between the groups in the number of concepts or connections in the maps; however, maps made by students who made a correct first diagnosis had higher scores for the quality of connections than those created by students who made an incorrect first diagnosis (12.13 vs 9.09; p=0.03). We also observed students’ general reluctance to use connections in their concept maps.Conclusion: Our results suggest that the quality, not the quantity, of connections in concept maps is indicative of their diagnostic accuracy.

The COVID-19 pandemic posed new global challenges for teaching. We met these challenges as an international collaboration by adapting a collection of virtual patients for clinical reasoning training to this novel context.

Background: Clinical reasoning is a complex skill students have to acquire during their education. For educators it is difficult to explain their reasoning to students, because it is partly an automatic and unconscious process. Virtual Patients (VPs) are used to support the acquisition of clinical reasoning skills in healthcare education. However, until now it remains unclear which features or settings of VPs optimally foster clinical reasoning. Therefore, our aims were to identify key concepts of the clinical reasoning process in a qualitative approach and draw conclusions on how each concept can be enhanced to advance the learning of clinical reasoning with virtual patients. Methods: We chose a grounded theory approach to identify key categories and concepts of learning clinical reasoning and develop a framework. Throughout this process, the emerging codes were discussed with a panel of interdisciplinary experts. In a second step we applied the framework to virtual patients. Results: Based on the data we identified the core category as the "multifactorial nature of learning clinical reasoning". This category is reflected in the following five main categories: Psychological Theories, Patient-centeredness, Context, Learner-centeredness, and Teaching/Assessment. Each category encompasses between four and six related concepts. Conclusions: With our approach we were able to elaborate how key categories and concepts of clinical reasoning can be applied to virtual patients. This includes aspects such as allowing learners to access a large number of VPs with adaptable levels of complexity and feedback or emphasizing dual processing, errors, and uncertainty.

Introduction: Massive Open Online Courses (MOOCs) are an increasingly popular form of education in health professional education. VPs have been introduced in MOOCs to increase interactivity. There is a lack of research in understanding the reasons behind high dropout rates in MOOCs. We explored how learners interact with VPs and compared the significance of different VP designs on dropout rates. Methods: RCT of 378 participants split into two groups to interact with two VPs using different design: branching and linear. Data on node progression and VP attempts was analysed using descriptive and quantitative analysis. Results: Eight groups of learner interaction patterns were identified. The majority of learners completed the VP in a linear path in one attempt. A significant number either completed the case in a loop path in one attempt, completed in a linear path in multiple attempts or dropped out without attempting the case. VP design has a significant effect on dropout rates of learners. There is a higher dropout rate from a branched VP compared to linear VP. Discussion: Prior research showed that branched VPs are more engaging and promote greater learning compared to linear VPs. However, our results indicate that branched VPs had greater dropout compared to VPs that require less time to be solved. Conclusions: We conclude that branching had a negative effect on completion of the VP activity in the MOOC. Moreover, we believe that more complex VPs require more effort on task and this might not be a design that facilitates the interaction in a MOOC audience, where the participants might wish to acquire the basic medical knowledge offered by the course.

Early‐stage responses of Brassica juncea, B. napus and B. oleracea during black spot disease and the development of Alternaria brassicicola were evaluated. Infection of plant cells by the fungus occurred mainly through direct penetration or by appressoria and, rarely, stomata in B. juncea and B. oleracea; in B. napus, penetration was mostly direct. The process of conidial germination, germ tubes and appressoria formation on a leaf surface was correlated with the post‐inoculation time and host species (p < 0.05). Changes in the leaf surface were observed at successful infection sites as bright, wax‐deprived areas. Significant differences in progression of infection between Brassica species were observed, with B. oleracea being the most susceptible and B. napus the least. Accumulation of superoxide anion radicals and hydrogen peroxide was not only observed in plant cells, but also in A. brassicicola germ tubes and appressoria at 8 and 12 h post‐inoculation (hpi). Enhanced production of reactive oxygen species (ROS) and lipid peroxidation during infection triggered diverse patterns of enzymatic and non‐enzymatic antioxidant activities and phenolic compound contents in response to A. brassicicola. In each Brassica species, a different antioxidant was the most active. With their antioxidant properties, phenolic compounds also played an essential role in these interactions. This phenomenon could be related to the disparate levels of susceptibility of the Brassica species to A. brassicicola; for example, the most susceptible, B. oleracea, showed high ROS accumulation with decreasing tendency during disease progression, unchanged phenolics content, and the highest catalase activity, differentiating it from B. juncea and B. napus. [ABSTRACT FROM AUTHOR]

A fundamental challenge in medical education is creating high-quality, clinically relevant multiple-choice questions (MCQs). ChatGPT-based automatic item generation (AIG) methods need well-designed prompts. However, the use of these prompts is hindered by the time-consuming process of copying and pasting, a lack of know-how among medical teachers, and the generalist nature of standard ChatGPT, which often lacks the medical context. The Case-based MCQ Generator, a custom GPT, addresses these challenges. It has been trained by using GPT Builder, which is a platform designed by OpenAI for customizing ChatGPT to meet specific needs, in order to allow users to generate case-based MCQs. By using this free tool for those who have ChatGPT Plus subscription, health professions educators can easily select a prompt, input a learning objective or item-specific test point, and generate clinically relevant questions. It enhances the efficiency of MCQ generation and ensures the generation of contextually relevant questions, surpassing the capabilities of standard ChatGPT. It streamlines the MCQ creation process by integrating prompts published in medical education literature, eliminating the need for manual prompt input. Future development aims at sustainability and addressing ethical and accessibility issues. It requires regular updates, integration of new prompts from emerging health professions education literature, and a supportive digital ecosystem around the tool. Accessibility, especially for educators in low-resource countries, is vital, demanding alternative access models to overcome financial barriers. [ABSTRACT FROM AUTHOR]