Comparison of Knowledge Retention after the Use of a Virtual Patient versus a High-Fidelity Physical Simulator and Traditional Training
Bibliographic record
Abstract
Aim: This research assesses the effect of a virtual patient simulation platform CyberPatient (CP) compared to a high-fidelity physical simulator SimJunior (SJ) and traditional bedside training (TBT) on knowledge retention and competencies in a health education environment. Material: A total of 143 fifth-year medical students were randomly assigned to three groups: TBT-Group (n = 55) received traditional education; CP-Group (n = 44) was trained with a virtual patient platform CyberPatient; and SJ-Group (n = 44) was trained using a high-fidelity simulator SimJunior. Educational content for all groups included competencies on pediatric asthma. Methods: Students’ level of knowledge acquisition was measured with a multiple-choice question test (MCQ) administered before the application of educational methods (Assessment I), immediately after completion of pediatric asthma training (Assessment II), and knowledge retention was measured two months later the completion of training (Assessment III). At the end of the study, student satisfaction was also measured by a survey questionnaire containing 5 questions rated on a Likert scale. Results: Assessment of acquired knowledge immediately after completion of pediatric asthma training revealed a significant difference between TBT-Group and SJ-Group (p p Conclusions: Virtual training with CyberPatient and high-fidelity physical simulation had a significant (p
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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 | 0.006 |
| 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) | 0.002 | 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".