The Perspectives of Medical Students in China to Undergo Short-Term Training Abroad
Bibliographic record
Abstract
Jinan University in Guangdong province has several years experience of sending medical students abroad for short-term training, based on the schools’ agreement with international universities. Currently, we analyze the problems and experience including the medical students’ favorite countries, timing, purposes, academic marks, and expenses etc., which came forth from medical students of Jinan University who had participated in the short-term training in the past several years. Our survey suggests that the choices of Chinese medical students about the host universities vary, although the universities in Western countries are still the most popular. The optimal timing the students prefer for short-term training abroad is during vacation especially after grade 2. Broadening the horizons, learning the different knowledge and increasing their academic experience are the major objectives for most exchange students. Furthermore, the survey shows that students hope to improve exchange students’ status management and credit acknowledgement system among interschool administrations. This study could supply useful information for the upcoming exchange students for Chinese medical universities/schools and meanwhile, for the host universities receiving Chinese medical students in the world. Hence, both universities and medical students will benefit for more efficiently implementing the exchange programs in the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".