Seeking the Optimal Time for Integrated Curriculum in Jinan University School of Medicine
Bibliographic record
Abstract
The curricular integration of the basic sciences and clinical medicine has been conducted for over 40 years and proved to increase medical students’ study interests and clinical reasoning. However, there is still no solid data suggesting what time, freshmen or year 3, is optimal to begin with the integrated curriculum. In this study, the integrated courses on cardiovascular and respiratory systems were performed to part of year 1 and year 3 medical students while non-participant students acted as control. We tried to explore the optimal time through comparison of the exam results and questionnaire of participated students. It was demonstrated that year 3 participant students got better exam score than year 1 students did, and the questionnaire showed that it might be due to the year 1 participants difficultly caught up with the contents of integrated courses without appropriate background knowledge. Three years later, the participant students got higher ability to analytical thinking of clinical diseases in comparison to non-participant students, while it did not improve the acquirement of their clinical practical skills. Taken together, our study in Jinan University School of Medicine indicated that the integrated courses would be approximately effective if combined to conventional medical teaching at year 3 after the students obtain relevant basic sciences knowledge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".