Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it