Participation Experience in Simulation Training Using Holographic Standardized Patients
Bibliographic record
Abstract
To develop simulation practice education content and improve the quality of practice education by identifying nursing students' perception types regarding simulation practice education experiences using Holographic Standardized Patients (HSP) and analyzing and describing the characteristics of each type of nursing students' perception. This study is a qualitative study that applied phenomenological methods to explore the essential meaning of nursing students' perceptions of nursing students' simulation training using HSP. It was conducted after obtaining approval from the institutional ethics committee, and the data collection period was from October 24, 2022 to September 31, 2023. The interview was conducted as an in-depth interview. The participants were third-year nursing students at a university who received training implemented through simulation using HSP. Among the 14 students who voluntarily agreed to participate in the study after receiving an explanation of the contents of the study, they were the dropouts. The subjects were 10 people excluding. As a result of this study, 81 meaning compositions, 20 topic collections, and 4 categories were derived: 'interesting classes', 'expectations that overcome obstacles', 'models for future classes', and 'strengthening practical skills'. Korean nursing students have already become accustomed to digital culture for a long time, and it has been confirmed that this form of converged technology-based simulation education no longer poses a technical problem to students. If various simulation-based education is developed in the future, it is believed that it will lead to integrated development of nursing education and simulation education beyond the limited clinical practice environment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".