Medical Teacher最新文献

Podcasts for healthcare information: Podcasts are widely regarded by the public and learners as highly useful and relevant, earning high scores for satisfaction and ease of use. In younger audiences, podcasts have been shown to significantly increase awareness and understanding of complex topics.

An introduction to artificial intelligence-generated podcasts (aigps): Targeted and personalized learning through AIGP may aid in facilitating information digestion and retention. In this pilot study, we examined whether AIGP can simplify scientific health-related literature in an engaging manner while maintaining accuracy for the purposes of health education.

Aigp pilot study: To test the utility of AIGP in health education, we evaluated the engagement and performance of AIGP in comparison with matched human-generated podcasts. All six podcasts were evaluated using a validated rubric to assess critical literature evaluation skills. Inter-rater agreement showed moderate to good agreement and concordance between the three human evaluators.Both types of podcasts gave comparable definitions of concepts and were considered equally as engaging as per human evaluation.

Discussion and future directions: A technology such as AIGP holds promise for modernizing and augmenting academic curricula; however, its effectiveness in improving education outcomes across various healthcare disciplines must be rigorously assessed and evaluated prior to implementation.

Background: While career indecision is well-studied in vocational psychology, its application in medical education remains limited. This scoping review examined sources of indecision, associated factors, and strategies to support medical students' career decision-making.

Methods: A systematic search of PubMed, Scopus, CINAHL, and ERIC identified relevant studies published from January 2014 to December 2024. Two reviewers independently screened articles and data were charted. Eligible articles were synthesised using directed qualitative content analysis with inductive expansion, guided by Kulcsár et al.'s career decision-making taxonomy.

Results: Ninety-three studies were included. Most studies focused on the career aspect of specialty selection. Career decision-making difficulties were categorised into Readiness (dysfunctional beliefs, career decision-making self-efficacy, willingness, general indecisiveness), Lack of Information (about self, about world of work, about how to make career decisions) and Use of Information (unreliable information, internal conflicts, external conflicts). Personal coping strategies and institutional support recommendations were identified. Influencing factors included demographics, personal traits, educational experiences, attitudes, and macro-level disruptions.

Conclusions: This review maps medical students' career decision-making difficulties to a structured framework and outlines support strategies and influencing factors. Findings underscore the value of integrating career psychology into medical education and addressing structural barriers to better support students' career development.

Asynchronous e-learning modules offer an efficient way to teach health professions learners important concepts. Medical educators should use best practices to design, develop, implement, and continually improve e-learning modules. E-learning designers should develop clear goals relevant to their learners, engage in thoughtful selection of the learning platform, incorporate interactive modules and provide opportunities for learners to continue their learning beyond the module. Asynchronous e-learning modules allows health professions educators to both to reach more learners and to provide flexibility for learning. Although they can be combined with a flipped classroom approaches, this article focuses on best practices for creating modules that can be used as a stand-alone method for conveying important information for health professions learners.

Purpose: Near-peer teaching (NPT) is widely used to substitute faculty in undergraduate clinical skills training. However, little is known about which educational design choices are made when incorporating NPT, prompting the following research questions: What were course directors' design choices when incorporating near-peers into clinical skills courses? And, what were the reasons for these design choices?

Materials and methods: This descriptive qualitative study carried out 25 semi-structured interviews with course directors from 12 countries who had recently published on NPT, complemented by other, recognized NPT experts. Reflexive thematic analysis of the transcribed interviews combined inductive and deductive approaches. Cognitive Apprenticeship informed the deductive analysis.

Results: Course directors appointed near-peers to teach standardisable skills to small groups of beginners in courses without patients - complemented by faculty supervision. Some directors involved near-peers in co-designing education, co-assessments, or co-facilitating transition to clinical practice. Course directors justified their choices based on near-peers' limited content expertise, but also their adherence to teaching scripts, cognitive congruence with students, social proximity, digital/technical literacy and low costs.

Conclusions: Course directors' design choices were mainly inspired by working around near-peers' limitations. Some courses capitalised on near-peers' unique perspectives, extending near-peer roles to co-designing education and co-facilitating transitions to practice.

Background: In medical training, learning typically involves individuals interacting asymmetrically: instructors who explain and demonstrate and learners who follow these instructions. However, there are also moments when learners lead the interaction, for example by pointing out unclear connections. This shifting 'dance of leadership' manifests in measurable patterns of visual attention, whose impact on learning is not well understood.

Methods: Using dual mobile eye-tracking methodology, we explored the joint eye movements of 29 teacher-student pairs (mean age = 24 years ± 3; 16 females) during a simulated sonography training in an OR environment. Using diagonal cross-recurrence analysis, we computed the gaze lag time for one person to couple the other's gaze pattern, which we used as a proxy for leader-follower behaviors. Afterward, we quantified the relative frequency of leading behaviors across distinct regions within the training environment and examined their relationship to learning performance metrics.

Results: We found that leader-follower behavior varied substantially. Teachers consistently led attention on the sonography monitor, showing tight coupling and minimal variation, reflecting its role as the procedural core. Students more frequently initiated gaze toward anatomical references and during interpersonal interactions. Importantly, only teacher-led guidance toward anatomical references was positively correlated with learning outcomes (r = .50, p < .01).

Conclusions: This study reveals that visual leadership during sonography training follows a two-tiered structure: instructor-dominated domains for technical execution and learner-engaged zones for exploration and social interaction. These insights about leader-follower dynamics could be used for targeted analysis and adaptation of clinical teaching situations.

Purpose: Creating clinically complex Multiple Choice Questions (MCQs) for medical assessment can be time-consuming . Large language models such as GPT-4, a type of generative artificial intelligence (AI), are a potential MCQ design tool. Evaluating the psychometric properties of AI-generated MCQs is essential to ensuring quality.

Methods: A 120-item mock examination was constructed, containing 40 human-generated MCQs at novice item-writer level, 40 at expert level, and 40 AI-generated MCQs. int. All examination items underwent panel review to ensure they tested higher order cognitive skills and met a minimum acceptable standard. The online mock examination was administered to Australian medical students, who were blinded to each item's author.

Results: 234 medical students completed the examination. Analysis showed acceptable reliability (Cronbach's 0.836). There were no differences in item difficulty or discrimination between AI, Novice, and Expert items. The mean item difficulty was 'easy' and mean item discrimination 'fair' across all groups. AI items had lower distractor efficiency (39%) compared to Novice items (55%, p = 0.035), but no difference to Expert items (48%, p = 0.382).

Conclusions: The psychometric properties of AI-generated MCQs are comparable to human-generated MCQs at both novice and expert level. Item quality can be improved across all author groups. AI-generated items should undergo human review to enhance distractor efficiency.