Impact of Simulation-Based Learning on Learning Loss Among Nursing Students: A Quasi-Experimental Study
Bibliographic record
Abstract
Nursing students faced learning losses during the COVID-19 pandemic due to the transition to virtual classes, inadequate communication, and reliance on virtual clinical training as a prerequisite for clinical practice. This study aimed to investigate the extent of learning loss experienced by nursing students and examine the impact of simulation-based learning (SBL) on mitigating this learning loss and on students’ confidence, satisfaction, and performance before and after the SBL program. This quasi-experimental study used a within- and between-subjects design. Data were collected from January 2022 to May 2023 from 177 nursing students before and after the SBL program. The Learning Loss scale and Simulation Training Evaluation Questionnaire were used. Substantial learning losses were observed in nurses’ knowledge, professional attitude, and skills before the intervention. The intervention group had significantly higher knowledge, professional attitude, and professional skills than the control group. The intervention significantly improved nursing students’ confidence, expectations/satisfaction, and performance. The regression model revealed that age and weeks in internship were significant predictors of learning loss. Prior distance education experience did not show any significant association with learning loss. Thus, SBL is useful in crisis situations; it enhances nursing students’ knowledge, professional attitudes, and professional skills. Course designers should consider integrating SBL into nursing curricula as an innovative teaching strategy to compensate for possible learning losses. This approach will help prepare graduates to enter the workforce with the ability to quickly adapt and practice confidently in clinical settings to ensure patient safety.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".