Virtual Gaming Simulation: An Interview Study of Nurse Educators
Bibliographic record
Abstract
Background. Two methods that provide high fidelity experiences outside of clinical settings are laboratory simulation and virtual simulation. Virtual gaming simulations are emerging and currently, there are no guidelines regarding the process. Objectives. The purpose of this study was to conduct interviews with nursing educators who use virtual gaming simulation in education to better understand the extent of use, the process, the challenges and benefits they experience, and their recommendations. Design. A qualitative, descriptive study, using purposive maximum variation sampling and interviews was conducted. Setting/Participant. Participants were selected from nursing programs in different Canadian and American educational institutions who had teaching experience using virtual gaming simulations with nursing students in higher education. Methods. In-depth interviews were conducted using a semi-structured interview guide with opened-ended questions. The interviews were recorded and transcribed. Data analysis was completed using a thematic approach. Results. The final sample consisted of 17 participants, 11(65%) were from Canada and the remaining 6(35%) were from the United States. The data yielded three themes: Benefits of gaming for the student; Preparing students and educators for success and, The virtual gaming simulation process. Participants described the challenges of using virtual gaming simulation in education and made recommendations for best practice and future research. Conclusion. The results of this study can be used as guideposts for educators who embark on this new learning experience and researchers who wish to expand the body of knowledge in this emerging field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".