Medical Teacher最新文献

Background: Feedback is a powerful educational intervention in clinical education, yet its effectiveness depends on how it is integrated into teaching and learning activities. Previous studies have shown that productive feedback in clinical education relies on sociocultural factors such as a supportive feedback culture, trustworthy relationships, and student agency. Co-creation is a promising approach for designing educational interventions that are contextually relevant and aligned with the needs of teachers and students. This study aimed to advance both theoretical and practical understanding of co-creation as a design strategy in health professions education, particularly in developing productive feedback processes tailored to undergraduate clinical education.

Materials and methods: Eight co-creation sessions were conducted with faculty, clinical teachers, students, and researchers. The process was iterative and grounded in feedback design principles informed by the literature. Co-creation led to the development of a prototype of a Feedback Toolkit, which was piloted in two dyads of clinical-teacher students in a seven-week physiotherapy clerkship. Weekly audio diaries were collected from participants and analyzed using content analysis.

Results: Data from the co-creation sessions informed the development of a Feedback Toolkit specifically designed for the clinical teacher-student dyad. The toolkit was built upon three design principles: (1) Contributes to a trustful relationship based on continuous mutual support, (2) Envisioned learning opportunities and feedback scaffolding, and (3) Plan the use of feedback. To operationalize these principles, the toolkit included practical materials such as podcasts, infographics, feedback prompts, and a Mini-CEX. The pilot study demonstrated the toolkit's usability and acceptability and highlighted its value in structuring feedback interactions. Challenges included limited time for full implementation and difficulties in providing constructive feedback.

Conclusion: The co-creation approach enabled the development of a fit-for-purpose feedback toolkit that aligns with the dynamic needs of clinical education. This study highlights co-creation as a feasible strategy for designing feedback processes in workplace-based learning.

Introduction: The advent of competency-based education has led to concerns regarding reductionism in the assessment of clinical competence. This apprehension stems from using the assessment of isolated subunit competencies to build a complete picture of clinical competence. Some argue that the entrustable professional activity (EPA) framework complements the construct of competencies, as EPAs describe units of work and require a global approach to their assessment. To that end, we aimed to discern whether the assessment of separate subunit competencies subsequently aggregated is equivalent to the global assessment of EPAs.

Methods: We designed a simulation-based workshop and assessed each student using the subunit competencies mapped to the core EPAs (the bottom-up approach) compared to the assessment of the global EPAs (the top-down approach) using 1) a supervision scale, 2) a global statement regarding entrustment and 3) a statement regarding readiness for residency. We aimed to determine whether the global assessment of EPAs was equivalent to aggregating the corresponding subunit competency assessments. The subunit competency assessments were additionally compared to aggregate workplace-based assessment data on the various subunit competencies from core clerkships.

Results: All eligible students participated (136/136). Assessment data obtained using the subunit competencies mapped to the EPAs were highly correlated with the assessment of subunit competencies obtained in the workplace during core clerkships. However, these subunit competency assessments obtained during the TTR course did not correlate with EPA-based global supervision scale ratings, entrustment decisions, or perceived readiness for residency.

Discussion: Global assessment of EPAs and the judgment of entrustment appear to be separate processes from aggregating the assessment of subunit competencies. This may reflect variations in the approach to global assessment when compared to the assessment of subunit competencies and the need to consider the construct of trustworthiness in addition to the learner's ability to perform each activity.

Introduction: Effective teamwork is essential for the successful functioning of healthcare. Breakdowns in teamwork are frequently flagged as contributing to major patient safety issues. Current research indicates a lack of knowledge regarding key factors that impact upon teamwork and how medical educators can best prepare students. This study explores how doctors work within healthcare teams; exploring barriers and enablers to effective teamworking.

Methods: A realist evaluation was used to understand the contextual influences and subsequent mechanisms that impact teamwork outcomes. Phase 1 included a realist literature review and scoping interviews with key stakeholders (n = 9). Phase 2 included 63 realist interviews representing a wide range of professional groups, roles and demographics across the UK healthcare.

Results: The initial program theory developed in Phase 1 was refined during Phase 2, integrating and extending the dispersed and patchy current evidence on the contexts, mechanisms, and outcomes of teamwork. Enablers included building a positive and supportive culture, effective communication, leaders who are understanding and approachable, clearly defined roles and respect, and continuity and experience of those in newer roles. Barriers included high service demands and work pressures, power imbalances and negative hierarchy, a lack of support for those new to teams and organisations, poor communication, poor leadership, a lack of appreciation and understanding of the needs of differing groups within teams, and finally EDI issues. There were particular difficulties for those in newer roles.

Discussion: We have identified that team dynamics are likely to be hindered by transient teams, lack of support, dysfunctional leadership and communication, and non-approachable colleagues. There are currently clear difficulties in how doctors interact with those in newer roles, and the ways in which team members are integrated into teams. This is the first research to develop a teamworking programme theory that can be used to support educators, institutions and regulators.

Purpose: In the post-COVID era, recognizing evolving physician competencies is crucial for guiding medical education and test development. This study aimed to extract valuable insights concerning emerging physician competencies from influencers' posts on X, leveraging an AI-driven approach.

Method: Two datasets pertaining to medical competency were analyzed, with posts collected from January 1, 2020, to June 1, 2023. Social network analyses were performed to identify influencers leading medical competency conversations on X. ChatGPT was utilized for textual analyses of influencers' posts to reveal core themes of physician competencies.

Results: Social network analysis revealed that medical professionals played a predominant role in disseminating information on medical competency on X. Textual analysis identified six core themes in the CanMEDS dataset-clinical learning environment, anti-racism, EDI, adaptive expertise, planetary health, and leadership development-and seven in the MedEd dataset-cultural competency, structural competency, assessment models, virtual care, EDI, leadership development, and wellness.

Conclusion: The identified themes emphasize physicians' competencies in addressing health disparities, preparing for real-world challenges, adapting to the evolving healthcare landscape, and leading effectively in diverse healthcare settings. The findings hold significant implications for medical education, test development, and the integration of artificial intelligence in physician competency assessment.

Introduction: Item difficulty prediction is crucial for planning and administrating educational assessments, especially those with high-stakes such as medical licensing examinations. The inconsistent findings across existing studies, however, highlight a critical gap in understanding which modeling components are most influential. This research addresses this gap by systematically investigating several key factors hypothesized to affect prediction performance.

Methods: This study explored the impact of: (1) model domain specificity, (2) input content granularity (e.g. item stem, correct answer, and distractors), (3) embedding dimensionality, and (4) the choice of the machine learning regressor. By selecting a range of embedding models and a series of Machine Learning models to predict the difficulty of 2815 Multiple-Choice Questions sourced from the National Center for Health Professions Education Development.

Results: Analyses revealed that XGBoost outperformed other counterparts (Mean RMSE = 0.1779), and the use of a domain-specific MedEmbed-small embedding model consistently improved prediction accuracy (Mean RMSE = 0.1860). Notably, using the item stem and the correct answer as input features achieved the best trade-off between predictive accuracy and model parsimony (RMSE = 0.1756).

Discussion: These findings offer valuable insights for data-driven measurement practices including Automated Item Calibration, Computerized Adaptive Testing, and Intelligent Tutoring Systems in medical education. Furthermore, this study revealed that the optimal feature set for difficulty prediction is contingent on the item style. Future research should extend this line of inquiry to the difficulty prediction of Multimodal test items. [Box: see text].

Purpose: A successful mentoring process and relationship require active engagement from both mentor and mentee. This study explored and evaluated the experiences, perceptions and associated factors of mentoring within postgraduate medical education from both mentors' and mentees' perspectives.

Materials and methods: The Mentors' Competence Instrument (MCI) was used to collect data in the three Wellbeing Service Counties in Finland. The cross-sectional survey yielded a total of 154 mentor and 79 mentee responses. Statistical analyses were conducted on the quantitative data, while the qualitative data were analysed using inductive content analysis.

Results: Statistically significant differences between the two groups were observed in Reflection during mentoring, Constructive feedback, and Learner-centred evaluation. The youngest mentees (under 31 years old) received the highest overall evaluations across all MCI sum variables. Areas for improvement were identified by the mentees in the structures and resourcing of mentoring, the quality of the mentoring relationship, the mentoring process, and the pedagogical competence of the mentors.

Conclusion: Mentees tended to evaluate the mentoring they received less positively than mentors assessed their own mentoring competence. Younger mentees appeared to rate their mentoring experience more favorably than older mentees. Mentees highlighted various aspects of mentoring that could benefit from further development.

Purpose: Medical schools would benefit from systematic guidance for developing comprehensive artificial intelligence (AI) policies, given generative AI's rapid integration into medical education. This study developed and applied an idealized AI policy framework to analyze AI-related documents at U.S. medical school institutions, providing reference points for the development and refinement of institutional policies.

Methods: AI-related documents from institutions with U.S. allopathic and osteopathic medical schools were systematically collected (from August to October 2024) and analyzed using a comprehensive framework containing 24 subthemes across six themes: Background/Context, Governance, AI Literacy, Tools/Usage, Ethical/Legal Considerations, and Technology Support and Infrastructure. Publicly available online documents were systematically coded to generate framework subtheme scores indicating breadth of coverage across framework themes.

Results: AI-related documents retrieved from 73.7% (146/198) of U.S. medical school institutions covered an average of 8 of 24 subthemes, representing a mean framework coverage score of 32.3% ± 19.8 Rarely addressed subthemes included Audit and Compliance Mechanisms (6.8%, 10/146), Technical Infrastructure (6.2%, 9/146), and Environmental Stewardship (1.4%, 2/146). Academic Honesty and Plagiarism dominated AI-related documents (81.5%, 119/146), followed by Decision-Making Authority (54.1%, 79/146) and Critical Evaluation (52.1%, 76/146). Formal AI policies demonstrated significantly higher framework coverage than other AI document types (44.0% vs 30.4%, p = 0.003). Seven institutions with the highest coverage (≥13/24 subthemes) shared seven common distinguishing features, with six present universally.

Conclusions: AI-related documents currently emphasize academic integrity over strategic planning, with substantial gaps in infrastructure and review mechanisms. Institutions can enhance their AI policies by incorporating common features identified in well-designed policies and following frameworks that strike a balance between immediate concerns and long-term adaptability.