Competency‐Based Psychiatry Residency Training Program Development in South Korea
Bibliographic record
Abstract
Psychiatry residency training in South Korea currently has many limits in developing proper competencies of residents. To address this problem, the Korean Neuropsychiatric Association has been developing a new competency-based training program since 2015, using the educational systems of advanced countries such as Canada, the United Kingdom, the United States, and Australia as references. It was found that within the referenced countries’ residency training systems, objectives based on competencies are stated in detail by psychiatric topics as well as various assessment methods and feedback about the resident’s competency level. In addition, we surveyed psychiatric resident training hospitals, and found that more than 80% of the respondents answered positively in reference to the new training program. This paper briefly reviews competency-based residency training systems of advanced countries and compares them to the current training program in South Korea. Many resources are needed to run a new competency-based training program, and governmental supports are essential to improve the quality of the residency training system.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".