Evaluating Virtual Simulation to Augment Undergraduate Nurses' Clinical Practice
Bibliographic record
Abstract
Background: Due to the lack of clinical placements during the pandemic, virtual simulation was used to augment student practice experiences. Method: Using Kirkpatrick's evaluation model, a program evaluation study using a mixed-methods design was implemented to assess student and faculty satisfaction and usefulness of virtual simulation, the effectiveness of meeting learning needs, and the effects of the virtual simulation resource on the development of clinical judgment ( n = 70). Results: Virtual simulation was rated as moderately useful with an overall mean of 1.7 ( SD = 0.66, range 1 to 3). Only 21% to 49% of the students found online simulation either met or well met the various areas of learning needs. Qualitative data highlighted the benefits of this strategy as well as implementation factors that affected students' experience. Conclusion: Virtual simulation can be used in clinical courses to augment learning when implemented in a way that addresses students' needs. [ J Nurs Educ . 2024;63(7):470–477.]
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".