Integrating Simulation Scenarios and Clinical Practices Guided by Concepts of Translational Medicine
Bibliographic record
Abstract
Purpose: To develop a novel method for closely and effectively integrating simulation scenarios and clinical practices to improve clinical skills training in the concepts of translational medicine.Methods: Forty-two and 38 third-year medical students in the classes of 2010 and 2009 at Jinan University were selected as an observation group and a control group, respectively. The former group was taught according to a new, integrated mode, while the latter received traditional methods. Students' scores on practical tests in physical examination, internal punctures, and case analysis; theory-based exams on diagnostics and internal medicine; and questionnaire surveys were compared and analyzed. In addition, system-oriented curricula were explored and implemented.Results: A novel mode that closely and effectively integrates theory and practice in the observation group had been established although there were no statistically significant difference (P>0.05) between Grade 2010 and Grade 2009 in clinical basic skills training scores. However, there were statistically significant differences (P<0.05) in scores on practical tests of physical examination and internal punctures among the diagnostic, internal medicine and internship periods in the class of 2010 but no statistically significant difference (P>0.05) in case analysis scores. Therefore, system-oriented curricula were initially designed and explored in excellent students from Grade 2010 to reinforce clinical thinking.Conclusion: The novel program integrating simulation scenarios and clinical situations for training students in diagnostics and internal medicine skills can improve medical students’ clinical comprehensive abilities and achieve effects that are similar to those of the traditional method. This program is more popular with students and ensures patient safety as well. In addition, different characteristics of clinical skills training have been compared for the further exporation of system-oriented curricula.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".