Challenges for Sustainable Interprofessional Education in South Korea: Insights from Key Global Countries
Bibliographic record
Abstract
Interprofessional education (IPE) is relatively new in medical schools in South Korea. Since the introduction of IPE in 2022, its effective and sustainable implementation has been of great interest in medical schools. This study analyzed literature on the development of IPE in the United States, Canada, the United Kingdom, Australia, and Japan to explore strategies for successful IPE in Korean medical schools. A systematic literature search focused on institutionalizing IPE yielded 30 papers for review. The findings included the following crucial elements for effective IPE: (1) government or institutional-led support; (2) establishment of networks and partnerships; (3) development of standardized core competency frameworks for IPE; and (4) inclusion of IPE in accreditation standards. These aspects underscore the importance of IPE as an essential component of health professional education that should be effectively and sustainably implemented in academic settings. The study concludes that the successful integration and sustainable development of IPE in Korean health education will necessitate expanded and proactive governmental support. Moreover, promoting collaborations among universities, hospitals, and local healthcare institutions will be vital for creating synergies in implementing IPE programs. Establishing networks to develop and execute joint IPE initiatives and securing initial support for conceptualizing and developing competency frameworks will be critical. Additionally, forming consortia of healthcare accreditation bodies to collaboratively develop and incorporate IPE standards into evaluation criteria will be essential. Efforts to surmount these challenges will contribute to building a structural and institutional support system for the successful introduction and sustainability of IPE in Korea.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".