Effectiveness and Utility of Virtual Reality Infection Control Simulation for Children With COVID-19: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: It is essential that nurses quickly learn the proper methods for preventing and controlling nosocomial infection and managing intensive care patients during the COVID-19 pandemic, including the donning and doffing of personal protective equipment (PPE). Virtual reality (VR) simulation offers the advantage of learning in a safe environment with a sense of realism similar to that of an actual clinical setting and has been reported to enhance self-efficacy in infection control, safety performance, and learning satisfaction among students. OBJECTIVE: This study aims to develop a virtual reality infection control simulation (VRICS) program regarding donning and doffing of PPE and respiratory care for pediatric patients admitted to an isolation unit for COVID-19 and to identify the effects of the program on PPE knowledge, infection control performance, and self-efficacy for nursing students. Additionally, the realism of the VRICS program and the students' level of satisfaction with the program were assessed. METHODS: This was a quasi-experimental study based on a controlled pretest-posttest design. Third- and fourth-year nursing students were divided into an experimental group (n=25) who participated in a VRICS program and a control group (n=25) with no participation. Data were collected from November 13 to December 10, 2021, and analyzed using descriptive statistics and the t test, paired t test, Mann-Whitney U test, and Wilcoxon matched-pair signed-rank test. The VRICS program consisted of a prebriefing, including direct practice of donning and doffing PPE, VR simulation, and debriefing. The VR simulation comprised 3 sessions: donning and inspection of PPE in the dressing room before entering the negative-pressure isolation unit; assessing for suction care, nasopharyngeal suctioning, and checking of COVID-19 patients in the negative-pressure isolation unit; and doffing PPE in the dressing room. The total execution time for the program was 180 min. RESULTS: =4.93, P<.001). The experimental group's mean scores for realistic immersion and learner satisfaction were 4.49 (SD 0.50) points and 4.75 (SD 0.38) points (on a 5-point Likert scale), respectively. CONCLUSIONS: The VR simulation training program involving pediatric COVID-19 patients combined skills training effectively and enhanced theoretical knowledge, respiratory care skills, and infectious disease preparedness. Thus, it could be applied to training nurses to respond more effectively to public health situations involving infectious diseases, including the COVID-19 pandemic.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".