Use of virtual simulations for improving knowledge transfer among baccalaureate nursing students
Bibliographic record
Abstract
Background: Use of simulation has been identified as an integrative strategy to bridge theory to practice and has been identified as a need in educating nurses in the future. Use of simulation provides an opportunity for nursing students to deliberately practice skills needed to be an expert nurse. The purpose of this study was to explore the use of virtual simulations to improve knowledge transfer of nursing students in one Midwest University. Methods: This study used a quasi-experimental design with 115 nursing students in one University. All students received education on topics related to conflict management, priority-setting, and patient safety. The intervention group also participated in three virtual simulations. To evaluate knowledge transfer, performance on an individual simulation was evaluated using the Capacity to Rescue Instrument (CRI). Comparisons were made among the two groups using ANOVA. Results: Total CRI score for the intervention group (m=21.98, SD 4.29) was significantly higher than the score for the control group (m=20.09, SD 4.05). Therefore, students participating in virtual simulations were able to transfer the knowledge learned in the classroom better than those not participating in the virtual simulations. Conclusions: Efforts for providing more opportunities for deliberate practice of critical skills (e.g. communication, conflict management, priority setting) must be provided. The addition of virtual simulations focused on the deliberate practice of specified skills improved the students’ performance. Use of a virtual environment may provide greater access to practice opportunities at a much lower cost than high fidelity simulators.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".