Pre-simulation Preparation Preferences of Senior Nursing Students: Virtual Simulation Games Versus Traditional Case Studies
Bibliographic record
Abstract
Background Presimulation preparation is critical to prepare learners to participate fully in clinical simulations; however, many do not complete assigned presimulation activities. Research Question Which presimulation preparation activities will senior nursing students choose and perceive as helpful? Methods A quasi-experimental study evaluated senior nursing student (n = 115) pre-simulation preparation preferences. Students had access to eight activities including a case study and a virtual simulation game (VSG). Participants indicated which activities they completed, and rated the case study and VSG in terms of usability, engagement, and impact on learning. Results Overall, 57% of participants completed the paper-based case study and 37% played the VSG. Participation in any preparation resulted in significant improvements in competence (t = 2.3; p = .02). Learners rated VSG higher than case study in terms of usability (t = 2.6; p = .01), engagement (t = 2.8; p = .01) and impact on learning (t = 2.4; p = .02). Conclusion Results revealed nursing students have different preferences for pre-simulation preparation. Although more students completed the case study than the VSG, those who played the game rated it higher. This supports providing a choice in presimulation preparation activities.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".