Medical Teacher最新文献

Introduction: Allied health professionals (AHPs) constitute a significant part of the global healthcare workforce. It is important that AHPs are well prepared for practice to provide quality care for patients, for their personal wellbeing, and for workforce retention. In comparison to doctors, there has been strikingly limited research on AHPs preparedness for practice. The aim of this study was (1) to understand how well newly qualified AHPs were prepared for practice and (2) to investigate support in the workplace for newly qualified AHPs.

Methods: We conducted a national qualitative study involving semi-structured interviews with multiple stakeholders across the UK. Sixty interviews were conducted with 61 participants across 15 professions newly qualified registrants (NQRs), practice supervisors/employers, and policymakers. The interviews were recorded, transcribed, coded in NVivo and analysed using a thematic framework approach.

Results: NQRs were perceived to be well-prepared for their clinical practice and communication skills; however, some were not prepared for decision-making, leadership, putting theory into practice and knowledge base. Underpreparedness for management of caseloads due to workforce shortages was a notable concern. Support for NQRs in the workplace varied hugely in its availability and effectiveness, was influenced by team size, how established a team is, resources, and the policies of different employers/trusts. Support that is tailored to the needs of the individual NQR was most valued by participants.

Discussion: There were more similarities than differences between perceptions of preparedness for practice of AHPs and other health professionals (e.g. doctors). If education providers and employers can better prepare and support NQRs in the workplace (proactive support measures) then this may contribute to fewer problems associated with mental health and well-being to resolve later (reactive support measures), e.g. at individual (impaired mental health) or system level (retention). This initial scene-setting research across all AHPs has identified pertinent issues for in-depth exploration.

Introduction: Medical educators increasingly recognize the need to improve training about the health and healthcare of sexual and gender minority (SGM) populations, yet many report uncertainty about how to address these topics in teaching. Pervasive cisheteronormativity in the medical education system necessitates intentional effort to improve SGM teaching. This study explores the drivers and impacts of medical educator behavior when teaching about SGM individuals and their health.

Methods: We conducted seven faculty educator interviews and four student focus groups (third- and fourth-year medical students, N = 18), representing experiences across the four-year medical curriculum. We used an inductive approach to identify themes about faculty behavior around SGM topics and the impacts of these behaviors on students.

Results: Faculty and students consistently value respect for SGM patients and populations. However, variability in educator comfort, local culture, and curricular complexity create inconsistent educational quality. We identified four themes: (1) SGM health education occurs through modalities beyond the formal curriculum; (2) Structural factors drive variability in SGM education; (3) Perceived importance of teaching SGM topics is tied to clinical relevance, empathy, and lived experience; and (4) Educator comfort is tied to fear of mistakes, growth mindset, and support.

Discussion: Quality of SGM teaching is driven not just by inclusion of SGM content but also by how educators engage with the information. SGM teaching quality is driven by educator perception of control in teaching about SGM health (e.g. knowledge, comfort, fear of making mistakes, extent of institutional support) and local norms about SGM inclusion in teaching. Institutional leadership can foster SGM inclusivity in medical curricula by providing intentional faculty development, supporting a growth mindset culture for educators, and providing centralized oversight of SGM content across the curriculum. These strategies promote alignment among educators and a cohesive curriculum for preparing future clinicians to care for SGM patients.

Introduction: Medical universities have become more aware of disability inclusion, and have begun taking cognizance of the importance of a supportive environment for students with disabilities. Faculty members can mitigate challenges to legitimacy and belonging by creating safe learning environments built on openness, humility, and actively valuing each learner. However, the factors contributing to these students' sense of belonging in medical university settings remain under-explored. To address this issue, the present study examines social factors influencing the sense of belonging for medical students with disabilities.

Methods: A qualitative phenomenological study using organizational paradox theory was conducted with 25 Japanese faculty members who have interacted with disabled medical students. Data were collected through face-to-face online semi-structured interviews and analyzed using thematic analysis from a social constructivism orientation.

Results: Faculty members believed that verbalizing appreciation for medical students with disabilities should be public and include the entire student body, feeling that this would enhance community perception and help disabled students feel a sense of belonging. They felt that disabled students' personalities and experiences before entering medical university may have already influenced their sense of belonging, potentially being a barrier in the specific medical university environment. Faculty members understood that the information network established when supporting students with disabilities also promotes a community-wide sense of belonging. The requirement for achieving certain technical standards required by physician training programs could also make some students with disabilities feel stigmatized and alienated.

Discussion: Japanese faculty members articulated a tension between the organization and the individual as a paradox encompassing various social factors, and that collaboration between both the disabled medical students involved and the medical students and faculty surrounding them is necessary to foster a general sense of belonging. The findings suggest that medical university faculty can play an active role in creating more inclusive environments.

Introduction: Accreditation is widely used to ensure quality in medical education; however, its effects on student learning outcomes across various domains and institutional contexts remains unclear. This study examines whether and how accreditation influences value-added learning outcomes among Chinese medical students.

Methods: Using a quasi-experimental design, we analyzed data from the 2019 to 2020 China Medical Student Survey (CMSS), which was linked to national accreditation records. Final-year students from institutions accredited between the two survey waves were classified as the treatment group; those from non-accredited institutions served as controls. Value-added learning outcomes were measured as self-assessed growth across four domains: science and scholarship, clinical practice, health and society, and professionalism. Propensity score matching and difference-in-differences (PSM-DID) estimation were applied. Guided by goal-setting theory, we interpret heterogeneity in accreditation effects across learning domains and institutional contexts.

Results: Accreditation significantly improved learning outcomes in clinical practice ($\beta$ = 0.275, p = 0.049), but had no significant effects in other domains. Subgroup analyses revealed stronger effects in non-elite universities and medical schools embedded within comprehensive universities, whereas limited or negative effects were observed in elite universities and free-standing medical colleges.

Discussion: The effects of accreditation varies unevenly across different learning domains and institutional contexts. Guided by goal-setting theory, our findings suggest that institutional response depends on the perceived difficulty, observability, and relevance of accreditation goals.

Objectives: This study evaluated the effectiveness of three retrieval practice strategies: re-solving virtual patient (VP) cases, answering multiple-choice questions (MCQs), and answering short answer questions (SAQs), on long-term memory retention of medical students using a VP simulation platform.

Methods: Eighty fifth-year medical students participated in a stratified randomized trial conducted in three phases. In the initial learning phase, participants completed a 14-item baseline test (seven MCQs and seven SAQs) to assess prior knowledge and enable stratified randomization. They then engaged with two clinical cases using the Paciente 360^® VP platform. One week later, participants were randomly assigned to one of three retrieval practice conditions: re-solving the original VP cases, answering 24 related MCQs, or answering 24 related SAQs. Six weeks after the intervention, participants completed a 40-item retention test (20 MCQs and 20 SAQs), which included both previously encountered and novel questions to assess long-term retention and transfer of learning.

Results: Participants in the SAQ condition demonstrated a statistically significant improvement in performance over time, while those in the re-solving the virtual case and MCQ conditions maintained their knowledge levels. No significant differences were observed between performance on repeated versus novel questions or between MCQs and SAQs.

Conclusions: Retrieval practice using VP simulations supports knowledge retention, with SAQs yielding the greatest improvement from baseline. Comparable performance on repeated and novel questions suggests that retrieval practice may also promote transfer of learning to new clinical scenarios.

Introduction: Factors influencing successful remediation in postgraduate medical education remain largely unexplored. This study aims to investigate the variables associated with remediation outcomes.

Methods: This was a retrospective cohort study of all residents who underwent remediation from July 1, 2012 - June 30, 2024 at the University of Calgary Cumming School of Medicine Postgraduate Medical Education. Multilevel mixed-effects logistic regression was used to identify outcome variables associated with remediation success.

Results: The most common CanMEDS roles remediated were Professional (52.7%), Communicator (51.1%), and Medical Expert (45.1%). Reduced odds of successful remediation were seen when the remediation plan included the Medical Expert Role (Odds Ratio (OR) 0.35 [0.14, 0.82], p = 0.016); and an increasing number of successful prior remediations (OR 0.58 [0.35, 0.95], p = 0.029).

Discussion: Remediation rates in postgraduate medical education are rising. Difficulties in the Medical Expert role are associated with lower remediation success and persistent issues that lead to recurrent remediations predict poor outcomes. Support should target core competencies, provide compassionate exit strategies for recurring struggles, and prioritize early intervention research.

Programmatic Assessment (PA) relies on the integration of diverse data sources to deliver holistic feedback that supports student learning. However, the complexity and volume of assessment data can hinder efficient tracking and interpretation, challenging the implementation of PA's principles. To address these challenges, we developed the Learning Progress Dashboard (LPD) - a bespoke technological-enhanced solution and infrastructure designed for medical students. Built with an user-centric approach, the dashboard integrates previously siloed data systems, offering tailored interfaces for both students and faculty to facilitate data-informed learning strategies. Key lessons were the importance of breaking down data silos, fostering interdisciplinary collaboration, and employing gradual change management strategies to balance innovation with user acceptance.

Introduction: Timely, high-quality feedback is vital in medical education but increasingly difficult due to rising student numbers and limited faculty. Artificial intelligence (AI) tools offer scalable solutions, yet limited research compares their effectiveness with traditional tutor feedback. This study examined the comparative effectiveness of AI-generated feedback versus human tutor feedback within the medical curriculum.

Methods: Second-year medical students (n = 108) received two sets of feedback on a written assignment, one from their tutor and one unedited response from ChatGPT. Students assessed each feedback using a structured online questionnaire focused on key feedback quality criteria.

Results: Eighty-five students (79%) completed the evaluation. Tutor feedback was rated significantly higher in clarity and understandability (p < 0.001), relevance (p < 0.001), actionability (p = 0.009), comprehensiveness (p = 0.001), accuracy and reliability (p = 0.003), and overall usefulness (p < 0.001). However, 62.3% of students indicated that both pieces of feedback complemented each other. Open-ended responses aligned with these quantitative findings.  .

Conclusion: Human tutors currently provide superior feedback in terms of clarity, relevance, and accuracy. Nonetheless, AI-generated feedback shows promise as a complementary tool. A hybrid feedback model integrating AI and human input could enhance the scalability and richness of feedback in medical education.

Introduction: Predictive analytics and Machine Learning (PAML) are gaining traction in health professions education (HPE). Their utilization includes, but is not limited to, guiding student enrollment, identifying at-risk learners, enhancing educational decisions, and allocating proper resources through data-driven insights. This study explored the use of PAML to identify at-risk learners in a one-year Certification Program in Emergency Medicine (CPEM) at the Indus Hospital and Health Network (IHHN), Pakistan with the aim of providing targeted educational support for improved outcome.

Methodology: By leveraging data from prior CPEM cohorts (2018-2022, n = 91), regression tree and linear regression machine learning models were compared to predict the final examination performance of the CPEM 2023 learner cohort (n = 26). The models were prospectively applied to identify at-risk learners (n = 14/26). Extra learning support (ELS) was offered as an inclusive measure to everyone, not just the ones flagged by the models and was accepted by ten learners. Data were analyzed for model accuracy and the impact of the educational intervention.

Results: Both models showed high accuracy (regression tree: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC)= 0.89; linear regression: AUC= 0.88), though the regression tree model demonstrated slightly better sensitivity and specificity. The models altogether predicted unsatisfactory performance for 14 learners scheduled to sit for the 2023 final examination. Following targeted intervention, eight learners showed improvement in their final scores. Regression tree model was comparatively better in making predictions; however, both models had their limitation.

Conclusion: The study demonstrated the feasibility and utility of using PAML to identify at-risk learners and tailor support strategies for enhancing educational outcome in low-resource settings. This additional support can augment expert judgement and ensure equitable educational practices. However, model limitations and ethical concerns, such as algorithmic bias, overfitting, and data imbalance, must be actively addressed in high-stakes assessments.[Box: see text].