Concept and Development of Resident Training Program for General Competencies
Bibliographic record
Abstract
Resident training programs in South Korea lag far behind that of advanced countries. Given the problems the current system in South Korea has, it is time to consider a new resident training system, resident training for general competencies. Training for the general competencies was practiced in medical fields in advanced countries such as the USA, Canada, and the UK as early as 20 years ago. This system has rendered itself a key component of resident training. Although a few theoretical procedures on general competencies have been practiced in South Korea, the awareness of this concept is still very weak, and the application of the theory to actual training is a long way off from becoming effective. It is urgent for South Korea to adopt competency- and outcome-based training for general competencies. To this end, the knowledge of the concept of this type of training should be improved. Also, the system should be carefully designed to cover a doctor’s whole career, and be applied immediately. The competency- and outcome-based training for general competencies is a system that assures high level qualifications. It reflects the needs of our society under the recognition that a professional organization should be committed and accountable in order to respond to social demands. As the benefits of the new training system reach the public and medical care consumers, training-related expenses should be borne by social costs.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".