Comparison of the Effects of Simulation Practice and Clinical Practice Education: Nursing Care of Children with Respiratory Diseases
Bibliographic record
Abstract
This study This study aimed to find the efficient practice education measures for the practice of pediatric health nursing by comparing the effects of simulation practice education and existing clinical practice one about children with respiratory diseases. Using the nonequivalent comparison group post-test non-synchronized design method, the research subjects were composed of total 62 people including 32 subjects for experimental group and 30 subjects for comparison group. Using the IBM SPSS v. 25.0 for data analysis, the differences in the baseline characteristics of both groups were tested through the t-test, X2-test, and Fisher’s exact test. The results of this research are as follows. In the results of conducting the homogeneity test on the experimental group and comparison group, there were no differences between two groups while it was statistically significant in self-efficacy. The experimental group who participated in practice education using the simulator was statistically significant in nursing competence(F=27.183, p<.001), communication skills(F=7.876, p=.001), and learning satisfaction(F=12.950, p<.001). The problem-solving ability(F=2.515, p=.089) was not statistically significant. Such results are significant in the aspect of implying the possibility of practice education using the simulator that could effectively complement clinical practice in the practice education of pediatric health nursing in the future.
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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 | 0.003 | 0.014 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".