Trends and Suggestions on the Regulation of Physician Licensing from the Perspective of Medical Professionalism and Self-regulation*
Bibliographic record
Abstract
A series of recent medical scandals in South Korea has led to calls for stricter regulation of medical licensing in this country. While the Korean government has recently revised its regulations, disagreements remain among the various stakeholders. This article explores the regulatory systems in Japan, the USA, the UK, and Canada to identify the most appropriate principles for self-regulation based on medical professionalism. The Japanese Medical Association has been working to improve its certificate system for specialists since 2011. The USA, the UK, and Canada have independent regulatory bodies to protect patients and improve medical professionalism and practice. This article explains how the licensing system for Korean physicians should be regulated.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| 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".