Virtual simulation in healthcare education: a multi-professional, pan-Canadian evaluation
Bibliographic record
Abstract
BACKGROUND: As we experience a shortage of healthcare providers in Canada, it has become increasingly challenging for healthcare educators to secure quality clinical placements. We evaluated the impact of virtual simulations created for the virtual work-integrated learning (Virtu-WIL) program, a pan-Canadian project designed to develop, test, and offer virtual simulations to enrich healthcare clinical education in Canada. Evaluation was important since the virtual simulations are freely available through creative commons licensing, to the global healthcare community. METHODS: Students self-reported their experiences with the virtual simulations and the impact on their readiness for practice using a survey that included validated subscales. Open-ended items were included to provide insight into the students' experiences. RESULTS: The evaluation included 1715 Nursing, Paramedicine and Medical Laboratory students enrolled in the Virtu-WIL program from 18 post-secondary universities, colleges, and institutions. Results showed most students found the virtual simulations engaging helped them learn and prepare for clinical practice. A key finding was that it is not sufficient to simply add virtual simulations to curriculum, careful planning and applying simulation pedagogy are essential. CONCLUSION: Virtual simulation experiences are increasingly being used in healthcare education. Results from this rigorous, large-scale evaluation identified ways to enhance the quality of these experiences to increase learning and to potentially decrease the number of hours healthcare students need in clinical practice to meet professional competencies. Further research is needed regarding many aspects of virtual simulations and, in particular, curriculum integration and the timing or sequencing of virtual simulations to best prepare students for practice.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".