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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".