Effects of Exploratory and Heuristic Multi-methods on System-oriented Curricula Based on Clinical Scenarios
Bibliographic record
Abstract
A system-oriented curriculum in which basic medical courses and clinical subjects are integrated and reformed is a new trend for medical education in China. In order to train and enhance the comprehensive clinical competency, thirty-two excellent medical students who formed a observation group from grade 2010 in Jinan University were selected to participated in this study. Three different exploratory and heuristic pedagogies, which employed the flexible application of case-based learning (CBL), problem-based learning (PBL), and team-based learning (TBL), were explored and implemented in cardiovascular, respiratory, and digestive teaching sections. The effects of these exploratory and heuristic pedagogical approaches based on the flexible application of the CBL, PBL, and TBL methods with clinical scenarios in system-oriented curricula are satisfactory. Accordingly, these teaching methods and experiences had been summarized and promoted to mainland and non-mainland medical students from grade 2014 in Jinan University. Teaching and learning effects are also satisfactory. In addition, different characteristics in conducting these courses between two types of university students are further compared and analyzed for the improvement of all system-oriented curricula 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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".