Innovation and Restructuring of Laboratory and Clinical Simulation in Undergraduate Nursing Programs During the COVID-19 Pandemic: An Integrative Review
Bibliographic record
Abstract
Background The COVID-19 pandemic forced nursing education institutions to abruptly shift away from traditional in-person learning and find alternative approaches to fulfill program requirements. This integrative review explores the various innovative and restructured simulation strategies used by undergraduate nursing programs for lab and clinical courses in response to the pandemic. Methods Whittemore and Knafl's (2005) five-step framework guided this review. A systematic search of six academic databases and quality appraisal using the Mixed Methods Appraisal Tool yielded 10 studies for the review. Results Strategies identified primarily employed virtual simulation methods using avatars or real people. Additional approaches included flipcharts and simulation-based flipped classrooms. Key themes pertaining to language and culture, immersion, facilitation and skills emerged. Conclusion Virtual simulation was a valuable tool during the pandemic, though not without challenges. Future implications are discussed. This review highlights the need for standardized terminology and considerations for cultural diversity in simulation. Additionally, further research into the effectiveness of virtual simulation as a replacement for in-person nursing clinical and lab experiences is warranted.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".