Case-Informed Learning in Medical Education: A Call for Ontological Fidelity
Bibliographic record
Abstract
Case-informed learning is an umbrella term we use to classify pedagogical approaches that use text-based cases for learning. Examples include Problem-Based, Case-Based, and Team-Based approaches, amongst others. We contend that the cases at the heart of case-informed learning are philosophical artefacts that reveal traditional positivist orientations of medical education and medicine, more broadly, through their centering scientific knowledge and objective fact. This positivist orientation, however, leads to an absence of the human experience of medicine in most cases. One of the rationales for using cases is that they allow for learning in context, representing aspects of real-life medical practice in controlled environments. Cases are, therefore, a form of simulation. Yet issues of fidelity, widely discussed in the broader simulation literature, have yet to enter discussions of case-informed learning. We propose the concept of ontological fidelity as a way to approach ontological questions (i.e., questions regarding what we assume to be real), so that they might centre narrative and experiential elements of medicine. Ontological fidelity can help medical educators grapple with what information should be included in a case by encouraging an exploration of the philosophical questions: What is real? Which (and whose) reality do we want to simulate through cases? What are the essential elements of a case that make it feel real? What is the clinical story we want to reproduce in case format? In this Eye-Opener, we explore what it would mean to create cases from a position of ontological fidelity and provide suggestions for how to do this in everyday medical education.
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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 | 0.144 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.191 |
| Scholarly communication | 0.029 | 0.058 |
| Open science | 0.009 | 0.042 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".