Investigating the medical study of overseas students at Jinan University Medical School
Bibliographic record
Abstract
A great number of overseas students have studied medicine at Jinan University Medical School over the past decade. Statistics from the past ten years show that these students’ test scores on diagnosis and medicine I & II are lower than those of their classmates from mainland China. To address the underlying causes of this phenomenon, we implemented a series of questionnaires for overseas and mainland Chinese medical students. The results indicate that there are no significant differences between overseas students and mainland Chinese students with regard to their attitude towards the study of medicine, their approval of the teaching and learning environments or their ability to improve their independent study capabilities at Jinan University Medical School; however, overseas students prefer to study at night and sleep later than their mainland Chinese classmates. One outstanding difference between these groups is that overseas students like to arrange their studies based on their interests, regardless of available time and subject contents, and this might lead them to perform poorly on examinations during their academic term at Jinan University Medical School. Overseas students might not have achieved scores as high as their Chinese classmates is that they do not completely focus on the content taught by teachers in class, which would later be assessed by exams at the end of each academic term. This observation is actually part of our medical educational concepts, especially in Chinese medical schools. Attention should be paid by both overseas students and medical schools to this discrepancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".