Virtual Patient Simulation at U.S. and Canadian Medical Schools
Bibliographic record
Abstract
PURPOSE: "Virtual patients" are computer-based simulations designed to complement clinical training. These applications possess numerous educational benefits but are costly to develop. Few medical schools can afford to create them. The purpose of this inventory was to gather information regarding in-house virtual patient development at U.S. and Canadian medical schools to promote the sharing of existing cases and future collaboration. METHOD: From February to September 2005, the authors contacted 142 U.S. and Canadian medical schools and requested that they report on virtual patient simulation activities at their respective institutions. The inventory elicited information regarding the pedagogic and technical characteristics of each virtual patient application. The schools were also asked to report on their willingness to share virtual patients. RESULTS: Twenty-six out of 108 responding schools reported that they were producing virtual patients. Twelve schools provided additional data on 103 cases and 111 virtual patients. The vast majority of virtual patients were media rich and were associated with significant production costs and time. The reported virtual patient cases tended to focus on primary care disciplines and did not as a whole exhibit racial or ethnic diversity. Funding sources, production costs, and production duration influenced the extent of schools' willingness to share. CONCLUSIONS: Broader access to and cooperative development of these resources would allow medical schools to enhance their clinical curricula. Virtual patient development should include basic science objectives for more integrative learning, simulate the consequences of clinical decision making, and include additional cases in cultural competency. Together, these efforts can enhance medical education despite external constraints on clinical training.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".