Effects of Simulation-Based Practice Education on Learning Satisfaction, Immersion, and Self-Efficacy of Nursing Students
Bibliographic record
Abstract
The purpose of this study is to understand the effects of simulation-based practice education on learning satisfaction, immersion, and self-efficacy. Using the method of one-group pretest-posttest experimental research, this study selected total 70 nursing students (3rd year) as research subjects. The final research subjects were total 63 students excluding seven people with insufficient responses. From March to April 2021, total eight sessions of simulation practice education (4 hours per session) were conducted once a week. In the effects of the program, the immersion, learning satisfaction, and self-efficacy were measured. Using the SPSS Window Version 25.0, the immersion, learning satisfaction, and self-efficacy were analyzed through the mean, standard deviation, and paired t-test. In the results of this study, the learning satisfaction (t=-2.06, p=.003), immersion (t=-10.61, p<.001), and self-efficacy(t=-2.31, p= .024) were statistically significant. In the results of analyzing the correlations of immersion, learning satisfaction, and self-efficacy after the simulation practice education, the learning satisfaction showed significantly positive correlation with immersion (r= .647, p<.001). The immersion also had positive correlation with self-efficacy(r= .438, p<.001). The results of this study verified the improvement of immersion, learning satisfaction, and self-efficacy of nursing students after the simulation-based practice education. Thus, it would be necessary to develop the educational contents for various subjects, and also to expansively apply the simulation practice 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".