Bibliographic record
Abstract
Every year, South Korean medical field is undergoing significant shortage of residents, especially in the field of surgery. Therefore, hospitals hire physician assistants(PA) to cover for short-staffed physicians and increase the quality of medical care. PA’s perform medical checkups, surgery assistance, prescription, anesthesia, and directing nurses to aid physicians. Foreign nations like the United States and Canada produce highly skilled PA’s via official PA training programs and standardized license exams, and PA’s are allowed to perform limited medical practice. However in South Korea, lack of legal basis for PA’s eligibility, training program, and range of allowed medical practice causes social problems like unlicensed medical care and medical accident. South Korea’s objective for PA program is significantly different from that of the United States and Canada. In those countries, purpose of PA is to aid and cover for primary care, whereas South Korean PA program is used for remedying surgeon shortage problem and hospitals’ business purposes, such as cost reduction and treatment time saving. This study criticizes implementing a new occupational field based on foreign PA policy will cause numerous problems and limits due to the difference of purpose. Therefore, establishing a new management& operating system for medical support persons considering patient safety, supply of health medical manpower, medical field requirements and characteristics of different medical institutions will be a realistic solution. Tasks for PA’s should be categorized and trained accordingly so that PA’s quality increases. Also, appropriate reward system, athority, and eligibility of PA’s are required according to their experience and ability. Registered nurse(RN) in South Korea is recognized as professional medical personnels that went through systematic education. This study suggests improvising RN program and utilizing it will be a efficient and realistic alternative solution for this problem.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".