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 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".