Medical Teacher最新文献

What was the educational challenge?: The integration of machine learning (ML) and large language models (LLMs) into healthcare is transforming diagnostics, patient care, and administrative workflows. However, most clinicians lack the foundational knowledge to critically engage with these tools, creating risks of overreliance and missed oversight. Just as understanding computed tomography (CT) physics became essential for its safe application, clinicians must acquire basic AI literacy. Practical AI education remains absent from most medical curricula.

What was the solution?: We propose a modular curriculum using Colab notebooks to teach foundational AI concepts. Colab's free, cloud-based, and interactive environment makes it accessible and engaging, even for non-data scientists. This hands-on approach emphasizes practical applications, enabling learners to explore datasets, build ML models, and interact with locally run LLMs, fostering critical engagement with AI tools.

How was the solution implemented?: The curriculum consists of five interconnected modules: introduction to data science, exploring datasets, predictive modeling, advanced ML techniques and imaging, and working with LLMs. Designed to integrate into medical school data science threads, the curriculum provides structured, progressive learning tailored to clinical contexts.

What lessons were learned?: Global accessibility, hands-on engagement, and modular design make this approach adaptable across diverse settings. Emphasizing ethical considerations and local relevance enhances its impact.

What are the next steps?: The next step is to integrate the Colab notebook-based curriculum into the authors' medical school data science thread. To support broader adoption, adaptable teaching guides will be developed, enabling implementation at other medical schools, including those in low-resource settings, while leveraging Colab's accessibility for regional customization.

What was the educational challenge? Reflective practice (RP) fosters professional growth but is often hindered by unclear purpose and minimal guidance. Designing comprehensive RP assignments is challenging because it takes time and not all faculty possess the skills to generate effective assignments. This innovation addressed two challenges: (1) creating clear, scaffolded reflective assignments using the transparent assessment framework (TAF) and (2) reducing faculty workload in assignment design. What was the solution? ChatGPT 4o was used to design RP assignments in health professions education (HPE). The TAF guided the design of the assessment. How was the solution implemented? A four-step process was followed to facilitate the design of the assignment. ChatGPT 4o was prompted to design the assignment and refined for the course. What lessons were learned that are relevant to a wider global audience? A pilot study in three graduate-level HPE courses showed ChatGPT-assisted assignments improved clarity, structure, and student performance while decreasing faculty preparation time. What are the next steps? We plan to expand this research to obtain student feedback on the effectiveness of the redesigned assignment and to explore the effectiveness of AI-generated reflective assignments with medical students who may require more structured guidance and contextualized prompts.

Background: Minimising examiner differences in scoring in OSCEs is key in supporting the validity of the assessment outcomes. This is particularly true for common OSCE designs where the same station is administered across parallel circuits, with examiners nested within these. However, the common classification of extreme examiners as 'hawks' or 'doves' can be overly simplistic. Rather, it is the difference in patterns of scoring across circuits that better indicates poor levels of agreement between examiners that can unfairly advantage particular groups of candidates in comparison with others in different circuits.

Methods and materials: In this paper, a new measure of differences in examiner scoring is presented that quantifies the different combined patterns of scoring in global grades and checklist/domain scores for pairs of examiners assessing in the same station but in different circuits. Based on calculating the area between separate examiners' individual borderline regression lines, this measure can be used as a post hoc metric to provide a broad range of validity evidence for the assessment and its outcomes.

Results and conclusions: In challenging the 'hawks'/'doves' paradigm, this work presents a detailed empirical analysis of a new misalignment metric in a particular high-stakes context and gives a range of evidence of its contribution to overall OSCE quality control processes and of improved fairness to candidates over time. The paper concludes with comments on developing the metric to contexts where there are multiple parallel circuits which will allows its practical application to a broader set of OSCE contexts.

Faculty development in medical education may take different forms and approaches ranging from standalone workshops and short courses up to longitudinal programs and postgraduate qualifications, such as Certificates, Diplomas, Master's and PhD degrees in health professions education (HPE). Many places offer staff development opportunities to help people to learn how to teach health professional students more effectively. Yet higher degrees in HPE are expected not only to enable graduates to be better teachers or assessors but also to act on a strategic level to support institutional directions to advance teaching, learning, assessment and scholarship in HPE. This guide is for people who wish to develop programmes to provide a more systematic and deeper training for those people that see themselves as professional health professions educators and indeed, the leaders of the future. The guide discusses the rationale, plans, process of implementation and evaluation of postgraduate programs in HPE in ten phases. Different variables should be considered with respect to the local context, institutional support, readiness of expertise, availability of resources, alignment with the strategic plan of the college/university and methods to measure the impact of these PG programs.

Academic exploration of the Clinical Learning Environment (CLE) has evolved over time. In 2009, Asch et al. drew a direct connection between the CLE, training outcomes, and patient care, highlighting the importance of the CLE and inspiring further research in this domain. With a growing body of evidence articulating its significance, several organizations were prompted to publish conceptualizations of the CLE as a discrete concept, such that its components may be measured and thus improved upon. Since then, it has become increasingly clear that the CLE is not an isolated entity; it is continuously molded by external pressures including the intensifying polarizations within our global society and the political ramifications of these divides. And yet, the nuances of how these external forces influence our CLE, including how we should, or should not, adapt to accommodate them, has not yet been explicitly captured. In this commentary we will summarize the academic history of CLE, review how the shifting global landscape has influenced the CLE, and propose a way forward. As a professional community, we must understand the impact of these external factors such that we may proactively adapt and ensure quality training outcomes and positive outcomes for our patients.