Combined Application of Study Design and Case-based Learning Comprehensive Model in Epidemiology Teaching
Bibliographic record
Abstract
This paper aims to conduct the SD-CBL (study design with the case based learning, SD-CBL) in Epidemiologyteaching and evaluate its effect. Students from five classes were recruited, and a combined comprehensive teachingmodel of SD-CBL was used in the “Injury Epidemiology” chapter, while other chapters in “Epidemiology”curriculum were using a teaching model of case based learning (CBL) only or single PowerPoint (ppt) teaching (itwas considered as a traditional teaching in many universities). In the final of the semester, the effects of these threeteaching models were compared in different majors and different students source. We found that SD-CBLcomprehensive teaching model was better than ppt only and CBL teaching methods (P<0.001, P=0.007), and thesignificant differences were found in the increased scoring rate between different majors and different studentssource (P<0.001, P=0.015). Thus, we concluded that the SD-CBL teaching model is effective and worth to promotein “Epidemiology” teaching, especial in chapters of epidemiology application. Moreover, it is recommended toconduct SD-CBL teaching model in students, who are major in medicine and have good science basis.
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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 | 0.068 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".