Advancing Virtual Simulation in Education: Administrators' Experiences
Bibliographic record
Abstract
Background Higher education healthcare administrators are under increasing pressure to find quality clinical placements and there is a critical need to look beyond traditional ways of preparing students for practice. Virtual simulation is a rapidly emerging tool for learning within healthcare education. Administrators are just starting to learn how to manage its integration into curricula. Methods Eleven healthcare administrators from seven institutions of higher education, colleges and universities were interviewed in this qualitative study to understand their needs, challenges, and recommendations regarding virtual simulation integration. These administrators were testing and integrating virtual simulations provided through the Virtu-WIL program, a pan-Canadian, work-integrated learning experience that developed and tested health care virtual simulations. Results Four themes were derived from the data: Driving forces, Impact of VS on learning process and outcomes, Collaboration and Coordination, and Sustainability. In addition, administrators recommended several different strategies to support the implementation of virtual simulation. These included faculty support, collaboration between schools and institutions and sustainability initiatives. Conclusion Administrators are integral to successful VS adoption; therefore, they need to effectively manage its integration in the curriculum.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".