Rich-Narrative Case Study for Online PBL in Medical Education
Bibliographic record
Abstract
Case studies are the basis of a well-known medical education pedagogy called problem-based learning (PBL). Traditional case studies are paper based and contain brief medical facts about a patient's illness. The authors of this article argue for a rich-narrative PBL design, and they report on a pilot project that incorporated such a design. The term "rich narrative" in this article covers two attributes. The first is the development of case studies that are rich in narrative information (often called "thick narrative"). The second component of rich narrative is the presentation of these thick narrative case studies in a media-rich format-that is, video rather than the traditional paper-based cases. Rich-narrative case studies may provide a more robust context for learning than traditional case studies because the rich cases more accurately reflect the complex reality of patient presentation and interaction. They also may help to lay the foundation for the development of a more holistic and patient-centered awareness during the training of health professionals. The use of video as a case presentation tool adds to this robust depiction of the patient as a complete human being rather than a collection of written symptoms. The authors discuss the power of narrative in learning, the significance of rich-narrative in medical education, the steps they took to develop a video-based, rich-narrative case study for online PBL tutorials at Simon Fraser University, and the evaluation of their prototype used in 2008.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".