Developing virtual simulation games for presimulation preparation: A user-friendly approach for nurse educators
Bibliographic record
Abstract
Objective: Engaging presimulation activities are needed to better prepare undergraduate nursing students to participate in clinical simulations.Methods: Design: We created a series of virtual simulation games (VSGs) to enhance presimulation preparation. This involved creating learning outcomes, assessment rubrics, decision point maps with rationale, and filming scripts. Setting: This was a multi-site project involving four universities across Ontario, Canada. Participants: Games were to be embedded within undergraduate nursing courses and used as presimulation preparation before participating in a traditional live simulation. Four existing bilingual peer-reviewed simulation scenarios were transformed into VSGs to be used for presimulation preparation. The team selected critical decision-points from each scenario to form the basis of each VSG, created filming scripts, and filmed and assembled video clips.Results: Our project generated four bilingual presimulation preparation VSGs with a user-friendly, low-cost VSG design process.Conclusions: We have demonstrated that nurse educators can easily create contextually relevant VSGs addressing program gaps.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".