The impact of small-group virtual patient simulator training on perceptions of individual learning process and curricular integration: a multicentre cohort study of nursing and medical students
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has precipitated rapid changes in medical education to protect students and patients from the risk of infection. Virtual Patient Simulators (VPS) provide a simulated clinical environment in which students can interview and examine a patient, order tests and exams, prioritize interventions, and observe response to therapy, all with minimal risk to themselves and their patients. Like high-fidelity simulators (HFS), VPS are a tool to improve curricular integration. Unlike HFS, VPS require limited infrastructure investment and can be used in low-resource settings. Few studies have examined the impact of VPS training on clinical education. This international, multicenter cohort study was designed to assess the impact of small-group VPS training on individual learning process and curricular integration from the perspective of nursing and medical students. METHODS: We conducted a multi-centre, international cohort study of nursing and medical students. Baseline perceptions of individual learning process and curricular integration were assessed using a 27-item pre-session questionnaire. Students subsequently participated in small-group VPS training sessions lead by a clinical tutor and then completed a 32-item post-session questionnaire, including 25 paired items. Pre- and post-session responses were compared to determine the impact of the small-group VPS experience. RESULTS: Participants included 617 nursing and medical students from 11 institutions in 8 countries. At baseline, nursing students reported greater curricular integration and more clinical and simulation experience than did medical students. After exposure to small-group VPS training, participants reported significant improvements in 5/6 items relating to individual learning process and 7/7 items relating to curricular integration. The impact of the VPS experience was similar amongst nursing and medical students. CONCLUSIONS: In this multi-centre study, perceptions of individual learning process and curricular integration improved after exposure to small-group VPS training. Nursing and medical students showed similar impact. Small-group VPS training is an accessible, low-risk educational strategy that can improve student perceptions of individual learning process and curricular integration.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".