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Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) in medical education has the potential to facilitate complicated tasks and improve efficiency. For example, AI could help automate assessment of written responses, or provide feedback on medical image interpretations with excellent reliability. While applications of AI in learning, instruction, and assessment are growing, further exploration is still required. There exist few conceptual or methodological guides for medical educators wishing to evaluate or engage in AI research. In this guide, we aim to: 1) describe practical considerations involved in reading and conducting studies in medical education using AI, 2) define basic terminology and 3) identify which medical education problems and data are ideally-suited for using AI. [ABSTRACT FROM AUTHOR]

This AMEE Guide summarizes fundamentals of a major experimental design option for medical education researchers: the randomised study. Medical education researchers face an overwhelming taxonomy of study design options; given the breadth of information on experimental design, the purpose of this Guide is to offer a resource for medical education researchers wishing to equip themselves with helpful information for when to match a study's objective and the use of randomised designs. Once a research question has been formulated study design is the cornerstone of the intricate, nested activities of any research project. Researchers negotiate many decisions in the pursuit of choosing an appropriate design approach; failure to do so can undermine a project's capacity to, for example, sufficiently test a hypothesis or theory. Written as an introduction, this Guide is intended for medical education researchers seeking to build on and synthesise the existing corpus of literature on experimental and quasi-experimental design approaches. While not comprehensive, presented are key concepts alongside relevant examples from the field of health professions education. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to compare student teachers and clinical associate professors regarding the quality of procedural skills teaching in terms of participants' technical skills, knowledge and satisfaction with the teaching. Methods: This is an experimental, randomized, controlled study comparing the teaching of student teachers and associate professors regarding participants' learning outcome and satisfaction with the teaching. Two skills are chosen for the experiment, IV-access and bladder catheterization. Learning outcome is assessed by a pre- and post testing of the participants' knowledge and skills. Participants evaluate satisfaction with teaching on nine statements immediately after the teaching. Results: In total 59 first year medical students are included as participants in the experiment. The students taught by student teachers perform just as well as the students taught by associate professors and in one skill-catheterization-they perform even better, mean post- minus pre-test scores 65.5 (SD 12.9) vs. 35.0 (SD 23.3), One-way ANOVA, p < 0.0001, effect size 1.62. Student teachers receive significantly more positive evaluations than associate professors on several statements. Conclusion: Trained student teachers can be as good as associate professors in teaching clinical skills. [ABSTRACT FROM AUTHOR]