RN-BSN students’ perception of vSim for nursing® using the simulation effectiveness tool – modified (SET-M)
Bibliographic record
Abstract
Introduction: This first-time novel pilot study explored RN-BS students’ perceived effectiveness of utilizing vSim for Nursing® as a clinical replacement with a second aim that explored the efficacy of their preparation.Methods: Outcomes of this quantitative study explored the effectiveness of vSim for Nursing utilized the Simulation Effectiveness Tool – Modified (SET-M).Results: Frequency distributions demonstrated majority (n = 14) strongly agreed on the effectiveness of vSim for learning, with all items ranging from 50% (n = 7) to 78.6% (n = 11). Debriefing had the overall highest responses, 57.1% (n = 8) to 78.6% (n = 11). Majority strongly agreed that their preparation was highly effective, 71.43% (n = 10) to 85.71% (n = 12).Conclusions: vSim for Nursing was perceived to be an efficacious clinical practice replacement tool while feeling prepared to achieve the learning outcomes was beneficial. Debriefing continues to be a crucial and fundamental facet to any mode of simulation. Virtual simulation experiences can bridge the gap to assist students to further their knowledge and confidence.
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 | 0.004 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".