Multiple cases in case‐based learning: A qualitative description study
Bibliographic record
Abstract
INTRODUCTION: Case-based learning is widely used in health professions education to improve clinical learning, but little is known about how best to approach multiple cases in this active learning strategy. Our study explored dental student views of multiple case-based learning in oral pathology. MATERIALS AND METHODS: Qualitative description informed the study design. Data were collected through semi-structured, individual interviews with twenty-one third- and fourth-year dental students who participated in multiple case-based learning seminars. Data were analysed using inductive, manifest thematic analysis. RESULTS: Themes were identified at approach and case levels. Approach-level themes included preparing students for clinical practice and board exams and maximising exposure (e.g., to lesions/conditions), knowledge application, and engagement within the time allotted for the learning session. Case-level themes included using challenging but manageable cases, linking cases to lecture content, providing the necessary clinical information to solve the cases, and ensuring that cases were authentic and common with non-typical presentations. Aspects of themes encompassed definitions of case characteristics, benefits, conditions of implementation, and recommendations for improvement. CONCLUSION: Cases should be considered individually, collectively, purposefully, and contextually in multiple case-based learning. Evaluations of learning and behavioural outcome are needed to further establish the effectiveness of approaches and case characteristics in multiple case-based learning.
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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 | 0.035 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".