Journal of Advances in Medical Education and Professionalism最新文献

Introduction: Medical education in the Gulf Cooperation Council (GCC) has undergone substantial transformation over the past two decades, driven by health system reform, workforce localization, and increasing demand for quality and accountability. Saudi Arabia serves as a central analytical anchor due to its scale and regulatory maturity. This study provides a structured, policy-oriented synthesis of medical education across the GCC and examines its transition from expansion toward system alignment.

Methods: A narrative review with structured thematic analysis was conducted. A search of PubMed, Scopus, and Web of Science, supplemented by policy documents and institutional reports, was performed for 2010-2025. Sources were selected based on relevance to GCC medical education. Data were organized across domains, including governance and accreditation, undergraduate education, assessment and licensing, postgraduate training and workforce alignment, and faculty development.

Results: Substantial progress is evident in regulatory consolidation, competency-based education, simulation-based learning, and national licensure examinations. However, implementation remains variable across institutions and countries. Key challenges include faculty capacity constraints, limited clinical training infrastructure, an imbalance in assessment practices, and misalignment between educational output and workforce needs. The literature remains largely descriptive, with limited standardized data on the outcomes and workforce impact.

Conclusion: Medical education in the GCC is transitioning from expansion toward system alignment, where integration of educational design, regulation, and workforce planning is critical. Strengthening assessment systems, investing in faculty development, expanding community-based training, and improving outcome measurements are essential. A coordinated, data-driven approach with regional collaboration is required to advance a responsive and globally competitive medical education system.

Introduction: Assessing professionalism in a valid, objective, and cost-effective manner remains a persistent challenge in clinical education. In Indonesia, this issue is particularly relevant in surgical training, where residents must integrate technical competence with professional behaviour in high-stakes, resource-limited settings. This study aimed to develop and evaluate the educational suitability of a Situational Judgement Test (SJT) combined with guided written reflection to assess and promote professionalism among general surgery residents.

Methods: A mixed-methods sequential design was used. An SJT was developed through blueprinting, item writing, and expert validation, resulting in 26 scenarios across six professionalism domains. Residents (n = 64 at baseline; n = 44 completers) completed a proctored pre-test SJT, participated in an 8-week guided reflection program (four submissions), and completed an asynchronous post-test SJT. Educational suitability evidence was examined through content validity (CVI) and internal consistency (Kendall's W). No construct or criterion validity testing was performed. Reflections were scored using Kember's four-level reflective framework by three independent assessors with Fleiss' Kappa for inter-rater agreement. Changes in SJT and reflection depth were analysed using paired tests and correlation analysis. Data were analysed using SPSS version 23.

Results: The mean CVI across items was 0.97 (range 0.90-1.00). The mean SJT score increased significantly from 386.59 ± 19.31 to 420.45 ± 12.56 among completers, with a mean difference of 33.86 ± 16.03 (p < 0.001). Reliability improved from Kendall's W = 0.38 to 0.52. Sensitivity analysis comparing baseline pre-test scores between completers (386.6 ± 19.3) and dropouts (391.0 ± 15.5) showed no significant difference (Mann-Whitney U, p = 0.258), indicating limited attrition bias. Reflective depth progressed from median level 1 to 4 across four submissions (median Δ = +3, p < 0.001), with substantial inter-rater agreement (κ = 0.68). There was no significant correlation between Δ reflection and Δ SJT (r = 0.059, p = 0.703).

Conclusions: Integrating SJT with guided reflection demonstrated strong content validity, internal consistency, and educational benefit, supporting its suitability for formative educational use in surgical professionalism training.

Introduction: The rapid advancement of Artificial Intelligence (AI) in medical education is driving a shift from traditional instructional design methods toward personalized and adaptive learning models. Despite numerous promising applications, the available evidence remains limited and fragmented; therefore, a comprehensive synthesis of the evidence is needed to support robust conclusions.

Methods: This study employed the four-phase meta-synthesis framework proposed by Sandelowski and Barroso. A systematic search was conducted across Medline, Embase, CINAHL, PsycINFO, PubMed, Web of Science, ScienceDirect, Wiley Online Library, SpringerLink, Taylor & Francis Online, SAGE Journals, and Scopus, covering publications from 2010 to 2025. Studies were screened according to predefined inclusion and exclusion criteria, and their methodological quality was evaluated using the Critical Appraisal Skills Programme (CASP). Coding reliability was assessed through a test-retest procedure, resulting in a reliability coefficient of 0.81.

Results: A total of 273 records were identified, of which 16 studies met the inclusion criteria and obtained CASP scores exceeding the threshold of 30. Content analysis revealed five principal domains: faculty-related applications (21%), student-related applications (28%), applications in the learning process (15%), curriculum development (13%), and assessment mechanisms (23%). Student-related applications constituted the largest proportion, highlighting the pivotal role of learner-centered personalization in AI-driven medical education.

Conclusion: The integration of Artificial Intelligence (AI) into individualized educational experiences represents a transformative model for medical education. AI enables adaptive learning pathways, dynamic assessment methods, and data-driven instructional environments, thereby enhancing student engagement, fostering faculty innovation, and promoting equity in learning outcomes. This synthesis proposes an overarching conceptual framework to inform policy, research, and implementation in the context of AI-supported personalized medical education.

Introduction: Effective education on emergency trolley medications is vital for the patient safety. Traditional methods often fall short of promoting deep and lasting learning. This study compared two active learning strategies, crossword puzzles and mind mapping, for teaching emergency trolley medications to anesthesia students.

Methods: This quasi-experimental study was conducted on 40 undergraduate anesthesia students of Iran University of Medical Sciences (IUMS) and Tehran University of Medical Sciences (TUMS) selected by census sampling. The same instructor delivered both groups personally. Simple individual randomization was used to randomly divide the participants into the mind mapping group (n = 20) and the crossword puzzle group (n = 20), through a coin toss procedure Two training sessions on emergency trolley medication-related concepts were attended by each group. To examine the efficacy of the intervention, we administeredthree tests: a pre-test, a post-test, and an unexpected retention test (after 3 weeks). Independent t-test and repeated measures ANOVA were used to analyze the data. The tests were constructed following the Bloom's taxonomy Table, and the reliability and validity of both tests were validated using standard procedures.

Results: Demographic factors such as age, GPA, and gender did not impact the results significantly either (P0.05). Independent t-test indicated that there was no significant difference in the two groups at pre-test levels (mind mapping: 5.58 ± 0.47; crossword puzzle: 5.60 ± 0.47; P = 0.985). Both of the methods led to a significant improvement in post-test learning metrics compared with pre-test results (map: 12.87 ± 0.53; puzzle: 13.25 ± 0.59; P = 0.641; repeated measures ANOVA: P < 0.001). However, in the retention test, students in the crossword puzzle group outperformed those in the mind map group (9.36 ± 0.66 vs. 6.52 ± 0.65; P = 0.004), indicating better long-term retention.

Conclusion: Although both methods enhanced short-term learning, crossword puzzles were more effective for long-term retention. These findings underscore the need to tailor instructional strategies to achieve desired learning outcomes.

Introduction: Traditional OSCEs, while reliable, are criticized for limited authenticity, delayed feedback, and high stress. The Clinical Assessment for Progression Examination (Clinical APEx), a new competency-based assessment at Monash, was developed to address these issues.

Methods: We conducted an interpretivist qualitative interview study with 33 final-year medical students who completed both the Clinical APEx and OSCE, recruited via post-assessment survey and site-wide invitations. Semi-structured Zoom interviews were analysed using inductive reflexive thematic analysis.

Results: Analysis revealed four key themes: Authenticity and Empathy, Feedback and Learning, Supportive Environment, and Challenges, which included concerns about standardisation, subjectivity, and under-assessment of clinical reasoning.

Conclusion: Beyond describing a local innovation, this study highlights broader mechanisms of educational impact, particularly authenticity, feedback, and psychological safety, that can inform assessment design across contexts. The Clinical APEx serves as a case example for reimagining high-stakes assessment as assessment for learning rather than solely of learning. These findings provides conceptual insights as to how clinical assessments might be made both rigorous and developmentally meaningful.

Introduction: Modern large language models (LLMs) like ChatGPT (based on the GPT-4 architecture) and DeepSeek offer unprecedented capabilities for generating scientific text. However, their performance in replicating structured, high-quality scientific writing, especially compared to human-authored abstracts, remains insufficiently evaluated. To compare the abstract quality produced by human authors, ChatGPT/GPT4, and DeepSeek across: six evaluation criteria Clarity, Coherence, Conciseness, Accuracy, IMRaD Structure, and Language Quality, using blinded expert ratings and non-parametric statistical methods, specifically the Kruskal-Wallis test followed by pairwise Wilcoxon rank-sum tests with false discovery rate correction.

Methods: We selected 23 medical and healthrelated research topics, each yielding three abstracts (human, ChatGPT, DeepSeek), for a total of 69 abstracts. Three raters scored each abstract. Kruskal-Wallis tests assessed group differences; Cliff's Delta (δ) was calculated as a nonparametric effect size for each comparison, suitable for ordinal data.

Results: Across criteria, ChatGPT and DeepSeek significantly outperformed human authors in Clarity, Coherence, IMRaD Structure, and Language Quality. In contrast, Conciseness and Accuracy showed negligible effect sizes (|δ| <0.10), suggesting parity across all three sources.

Conclusions: ChatGPT and DeepSeek achieved significantly higher scores in clarity, coherence, structure, and language quality, while showing comparable performance in conciseness and accuracy. These findings complement recent evaluations showing competitive medical and reasoning performance of DeepSeek models compared to proprietary LLMs. While shortform abstracts, expert oversight, and domain expertise remain critical, the results suggest that LLMs-particularly GPT4 and DeepSeek-can serve as effective tools in drafting scientific abstracts.

Introduction: The tension between tradition and innovation is a defining challenge in medical education, yet the literature lacks a strategic framework to guide educators through this dynamic.

Methods: This study addresses this gap using a ten-year qualitative autoethnography, drawing on a 65,000-word corpus of reflective journals and institutional artifacts to explore the educator's lived experience.

Results: The findings reveal a spectrum of three professional archetypes in response to this tension: Oppositionists, who view the two as rivals; Passive Integrators, who blend them reactively; and Smart Integrators, who do so with intentional purpose. Based on these insights, the study proposes the FUSION Design Helix, a dual-layered framework for strategic integration. The core comprises a six-stage process - ranging from 'Framing Goals' to 'Navigating Implementation' - which is synthesised through the macro-phases of Design Thinking: Inspiration, Ideation, and Implementation. By positioning tradition and innovation as complementary resources rather than rivals, the Helix provides a proactive, design-oriented engine for curriculum development.

Conclusion: Collectively, the findings reposition educational change as an act of purposeful pedagogical design rather than reactive compromise, offering a scalable cognitive framework for cultivating reflective educators and building resilient, future-ready curricula in medical education. Practically, the model provides medical schools and faculty developers with a structured roadmap for planning, implementing, and sustaining pedagogically grounded innovation while strengthening the educators' capacity for reflective, theory-informed practice.