Towards a typology of virtual patients
Bibliographic record
Abstract
INTRODUCTION: Although on-screen "virtual patients (VPs)" have been around for decades it is only now that they are entering the mainstream, and as such they are new to most of the medical education community. There is significant variety in the form, function, and efficacy of different VPs and there is, therefore, a growing need to clarify and distinguish between them. This article seeks to clarify VP concepts and approaches using a typology of VP designs. METHODS: The authors developed a VP design typology based on the literature, a review of existing VP systems, and their personal experience with VPs. This draft framework was refined using a Delphi study involving experts in the field, and was then validated by applying it in the description of different VP designs. RESULTS: Nineteen factors were synthesized around four categories: general (title, description, language, identifier, provenance, and typical study time); educational (educational level, educational modes, coverage, and objectives); instructional design (path type, user modality, media use, narrative use, interactivity use, and feedback use); technical (originating system, format, integration, and dependence). CONCLUSION: This empirically derived VP design typology provides a common reference point for all those wishing to report on or study VPs.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".