Use of Virtual Patients in Dental Education: A Survey of U.S. and Canadian Dental Schools
Bibliographic record
Abstract
The use of virtual patients in dental education is gaining acceptance as an adjunctive method to live patient interactions for training dental students. The objective of this study was to determine the extent to which virtual patients are being utilized in dental education by conducting a survey that was sent to sixty-seven dental schools in the United States and Canada. A total of thirty dental schools responded to the web-based survey. Sixty-three percent of the responding dental schools use virtual patients for preclinical or clinical exercises. Of this group, 31.3 percent have used virtual patients in their curricula for more than ten years, and approximately one-third of those who do use virtual patients expose their students to more than ten virtual patient experiences over the entirety of their programs. Of the schools that responded, 90.5 percent rated the use of virtual patients in dental education as important or very important. An additional question addressed the utilization of interactive elements for the virtual patient. Use of virtual patients can provide an excellent method for learning and honing patient interviewing skills, medical history taking, recordkeeping, and patient treatment planning. Through the use of virtual patient interactive audio/video elements, the student can experience interaction with his or her virtual patients during a more realistic simulation encounter.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".