The Effects of Blended Learning Using Virtual Reality Simulation in Pediatrics-adolescent Nursing Clinical Practice
Bibliographic record
Abstract
This study was to compare the effects of blended learning that integrates child-adolescent nursing clinical practice and virtual reality simulation. The aim of this study was to evaluate the effectiveness of blended learning in parallel clinical practice of child-adolescent nursing and virtual reality simulation of child-adolescent nursing by comparison of critical thinking, problem-solving processes, and clinical performance in both education groups before and after the educational intervention. The participants were 48 nursing college students. The experimental group (n=22) exposed a blended learning using virtual reality simulation, combining child-adolescent nursing clinical practice education, while the control group (n=26) received only child-adolescent nursing clinical practice curriculum course. The data collection period was from March 8 to November 26, 2021. The general characteristics of subject’s were analyzed by number, percentage, mean and standard deviation. And clinical practice fused with virtual reality simulation, that is, homogeneity verification of the blended learning education group was analyzed by x2 test and independent t-test. Whether there is a differences between pretest and posttest critical thinking tendency, problem-solving process, and clinical performance ability of the blended learning and the clinical practice education group was analyzed by independent t-test. The pretest & posttest results of each group showed statistically significant improvements in critical thinking, problem-solving processes, and clinical performance. In a comparison of the results of the two groups, the only statistically significant difference was found for critical thinking. Furthermore blended learning, combining a v-sim and a clinical nursing practice, into the nursing curriculum may contribute to the further development of nursing education.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".