Bridging the Gap in Medical Education: A Qualitative Study on the Perspectives of Japanese Medical Students and Patients on Outcomes-Based Education
Bibliographic record
Abstract
INTRODUCTION: Outcomes-based education (OBE) has transformed medical education by focusing on specific, measurable learning results. However, educators typically formulate these outcomes with little regard for the perspectives of key stakeholders, such as students and patients. This study explored the perceptions of Japanese medical students and patients regarding these outcomes. METHODS: We conducted focus group interviews with 14 medical students and 13 patients from the first author's university. Participants reflected on the eight outcomes of the 2010 edition of the Model Core Curriculum for Medical Education in Japan. Qualitative data analysis was conducted using thematic analysis. RESULTS: Medical students emphasized the importance of practical education, patient interaction early in their training, and education that bridges knowledge and action, questioning the effectiveness of traditional teaching methods related to professionalism and communication skills. The students also expressed dissatisfaction with simulated learning for team care. In contrast, patients stressed the importance of physicians' empathy and communication skills alongside a patient-centered research approach. They also expressed a desire for a range of ways in which physicians respond to patients as individuals. CONCLUSION: The study results have significant implications for outcomes-based medical education. Both medical students and patients questioned the efficacy of the traditional curriculum, notably in teaching professionalism, communication skills, and team care. The findings suggest that medical education outcomes for future physicians should integrate practical application, empathy training, and flexibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".