Virtual simulation in healthcare education: a multi-professional, pan-Canadian evaluation
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
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it