1. Study design and sample

This study adopted a non-equivalent control group pretest-posttest design to verify the effects of simulation-based education for nursing students on their knowledge, anxiety, confidence in infection control practice, and perception of the preparedness for caring for the patients of COVID-19.

The participants included senior nursing students recruited from the nursing departments of two universities in Korea through convenience sampling. Exclusion criteria were: (1) those who had previously participated in simulation-based education regarding caring for patients of COVID-19 and (2) those who had actual experience in caring for patients of COVID-19. To prevent the diffusion of experimental effects and consider the feasibility, nursing students attending Dong-A University and Dongguk University were assigned to the intervention and control groups, respectively. As the intervention included an educational program, and considering the measurement variables, the research could not be double-blinded.

In the research plan, we calculated a sample size of 60 participants in each group by using an independent t-test with the power of 0.8, significance level of 0.05, and effect size (Cohen’s d) of 0.52, which is the effect size on knowledge level in a study regarding simulation education [17]. Considering a dropout rate of 10%, we intended to recruit 66 participants in each group at first. After recruitment and dropout, 37 participants in the intervention group, and 42 in the control group were included for in the final analysis (See Supplementary Figure 1). These satisfied the cut-off of 71 persons as the minimum sample size based on the pre- and post-test values on knowledge levels in a study that validated the effectiveness of educational interventions, including simulations on infection control precautions among nursing students [18]. This sample size was calculated using the power of 0.8 and significance level of 0.05, delta value of 1.2, standard deviation of 1.5, and correlation of 0.5 obtained after the Generalized Estimated Equation (GEE) tests for the slope of two groups in a repeated measures design (continuous outcome) using the Power Analysis and Sample Size software 2023 [19]. Furthermore, to assess the adequacy of the sample size in this study, a post-hoc power analysis was conducted. The results revealed that for the variables that showed significance, the power ranged from 0.98 to 1.00. Therefore, it was considered that the participants included in this study were deemed appropriate.

2. Procedures

The application and evaluation of simulation-based education was conducted from October to December 2021. Participants were recruited via the online group chat rooms of senior-year students from October 10 to November 3. A recruitment announcement was posted in the group chat rooms, specifying, among other things, the purpose and content of the study and voluntary nature of participation. An online Google Forms survey link was also provided to enable the willing participants access the survey. Upon accessing the link, they were first presented with an explanation of the study and asked for their consent. If they did not agree to participate, the survey was automatically closed. Owing to the online-based recruitment approach and the voluntary nature of participation, it was difficult to recruit the desired sample size as planned. As this study involved students’ participation, extra attention was paid to ensure autonomy in participation, which is ethically significant. Additionally, the COVID-19-related restrictions caused administrative limitations in conducting face-to-face interventions for students who were not affiliated with the researcher’s institution. Therefore, we were unable to achieve the planned sample size.

After enrollment, students from Dong-A University and Dongguk University, who voluntarily agreed to participate in the study, were assigned to the intervention and control groups, respectively to complete the pretest survey. After the completion of the simulation-based training, the intervention group was administered a posttest survey. In contrast, the control group was administered a posttest survey four weeks later, and without any treatment.

3. Simulation-based education on caring for patients of COVID-19

This study is based on the NLN Jeffries simulation theory [16]. We designed and developed simulation-based education comprising components of the NLN Jeffries simulation theory [16] by reviewing infection control guidelines related to COVID-19 and seeking advice from the infection control unit at a university hospital in Seoul that specialized in treating patients of COVID-19.

The scenario was a case where a patient diagnosed with COVID-19 shows symptoms of high fever, is hospitalized in an isolation room, and complains of dyspnea and feelings of isolation and anxiety (Table 1). The learning goals were: “I can identify the nursing needs and problems of COVID-19 patients”; “I can apply nursing interventions according to the nursing plan to patients with COVID-19”; and “I can select appropriate personal protective equipment (PPE) and accurately perform donning and doffing.” During the simulation running, various infection control practices were performed, such as compliance with isolation precautions, proper use of PPE, and management of isolation medical waste (Table 1). While complying with these practices, participants were expected to conduct nursing assessments and implement nursing interventions for the physical problems of the patient. Furthermore, they were required to assess the patient’s feelings of isolation and anxiety and provide psychosocial interventions to resolve these problems.

Table 1
Scenario Progression

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In terms of fidelity, the simulation practice lab was reproduced as an isolation ward, and was divided into the anteroom, ward, and nurse’s station. Roles in the situation included one leader nurse who performed nursing proactively, and 2~3 nurses in charge of vital sign measurement, medication administration, nursing intervention, and recording. An instructor (the first author) played the role of a doctor. To resemble an actual clinical situation, it was considered realistic for one participant to play the role of a nurse. However, considering the number of students and training time, and that psychological safety could impact the educational effect in a high-fidelity situation, 2~3 nurses were assigned to each simulation running. The participants voluntarily divided their roles within the team. Scenario background and design included basic information of the module, nursing history, medical order, and examination results; prior nursing knowledge and skills; debriefing plan; and evaluation method, etc.

The simulation-based education lasted eight hours, involving four sessions: pre-lecture for background knowledge, skill practice and preparation for simulation, simulation experience, and post-learning (See Supplementary Table 1). The intervention group was divided into three subsets; each subset was divided into three teams; and each team comprised 4~5 students who attended the simulation at once. In the first session, online lectures were provided with an overview of the simulation module, learning goals, and prior knowledge and skills. The intervention group attended the online session before other sessions. The rest of the sessions were conducted in-person and consecutively, for one day. These sessions were conducted once per week and each subset of the intervention group attended these sessions on the same day; the sessions lasted a total of three weeks. In the second session, a quiz was conducted to ascertain the learning status of the pre-lecture, and training and evaluation of the skill of donning and doffing level D PPE were performed. Additionally, team discussion time was allocated to address the simulation situation. The third session comprised pre-briefing, simulation experience, and debriefing. Each team had two repeated simulation experiences on the developed COVID-19 patient nursing scenario module using the human patient simulator; simulation experience for each team lasted approximately 40 minutes. While one team experienced the simulation, the other observed the simulation their colleagues underwent. The simulation was operated by one professor (the first author) with simulation class experience and one assistant as a clinical instructor who provided technical assistance, helped set up the simulation lab, and supplied materials and supplies during the overall simulation class. After it was over, all subjects who participated in the practice attended a debriefing for 60 minutes. Debriefing was conducted as a group so that they could reflect upon their respective team’s experiences and share them with each other. The instructor led the discussion following a structured approach based on the gather, analyze, and summarize (GAS) model [20]. In the final session, participants were asked to consider topics related to the module and check their own values by performing the learning activities. These activities included investigating and summarizing key learning content related to the scenario, and watching a video of a nurse caring for the patients of COVID-19 and reflecting upon the nurse’s role. Additionally, they were instructed to reflect upon their own simulation experience and write a reflective journal individually.

4. Measurement

In the NLN Jeffries simulation theory [16], outcomes include participant, patient, and system. According to Jeffries et al. [21], with participants, it is possible to measure outcomes on self-confidence, learner’s satisfaction, skills, and knowledge. As such, this study measured primary outcomes including knowledge related to COVID-19, confidence in performing infection controlling skills, and perception of the preparedness for caring for the patients of COVID-19, which were the educational outcomes. Additionally, it assessed satisfaction and self-confidence in learning and anxiety, which were the reactions of students. Nursing students experienced higher levels of anxiety owing to factors such as fear of infection during the pandemic [22]. The high anxiety level had a negative impact on their intention to care for the patients of emerging infectious diseases [23]. Thus, this study investigated the level of anxiety among senior nursing students in the context of the COVID-19 pandemic.

Participant characteristics and primary outcomes were measured in the pretest and posttest surveys. Satisfaction and self-confidence in learning was only measured in the posttest survey of the intervention group. The pretest survey was conducted from October 19 to November 3 for the control and intervention groups. Each of the three subsets in the intervention group conducted posttest survey the day after intervention was completed. The posttest survey of the first subset was conducted three weeks after the preliminary survey, and the second and third subsets conducted survey after five and six weeks, respectively. The posttest survey of the control group was measured four weeks after the preliminary survey—similar to the intervention group—to control for the effects of extraneous variables such as maturation.

5. Data analysis

The collected data were analyzed with SPSS version 23.0 (IBM Corp., Armonk, NY, USA) for Windows, and the specific measures were as follows:

6. Ethical considerations

This study received approval from the Dong-A University Institutional Review Board (IRB No. 2-1040709-AB-N-01-202109-HR-065-02). The objective and content of this study were explained to participants, and it was emphasized that there would be no negative repercussions for refusing to participate in this study. After completing the survey, the participants received an electronic gift card.

1. Baseline homogeneity of the intervention and control groups

The average age of the participants was 23.06 ± 2.19 years, and women constituted the majority at 73 (92.4%) of the 79 participants (Table 2). A total of 78 participants (98.7%) had received infection control education within one year, and 75 (94.9%) possessed experience in education related to COVID-19. As for the most effective method of COVID-19 infection control education, 70 (88.6%) answered that it was education combining lecture and practice (Table 2). As mentioned above, there were no statistically significant differences between the intervention and control groups in terms of characteristics.

Table 2
Homogeneity Test of Characteristics and Education Experience between Two Groups at Baseline (N = 79)

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Among the primary outcomes, knowledge and confidence in performing infection control skills showed no statistically significant difference between the groups, indicating that both groups were homogeneous. However, anxiety was 3.27 ± 3.02 in the intervention group, which was statistically significantly higher than that in the control group (1.36 ± 2.14, p < .001). Additionally, preparedness was 31.95 ± 11.52 in the intervention group, which was significantly lower than that in the control group (44.48 ± 12.37, p < .001). Therefore, baseline anxiety and preparedness were not the same between the two groups (Table 3).

Table 3
Homogeneity Test and Effect of Intervention on the Outcome Variables of Two Groups^† (N = 79)

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The analysis of the baseline homogeneity between the follow-up and lost to follow-up groups revealed significant differences within the control group: 15 (65.2%) of the 23 participants were women in the lost to follow-up group, and 88.1% in the follow-up group. In terms of outcome variables, the score of knowledge was 17.38 ± 2.13 in the follow-up group, which was significantly higher than that of the lost to follow-up group (16.09 ± 2.41, p = .033). There was no difference between the two groups in terms of the other variables (See Supplementary Table 2).

2. Effect of simulation-based education

The effects of simulation-based education analyzed using the GEE are shown in Table 3, and the changes are shown in Figure 1 as a graph. In both the groups, the knowledge score increased in the posttest compared to the pretest. The knowledge score of the intervention group showed greater improvement from the baseline value compared to that of the control group, with an estimated mean of 25.52 in the posttest (Figure 1). The GEE analysis (Table 3) showed significant group × time interaction effect for knowledge (p < .001).

Figure 1
The trend of outcome variables between both groups. (A) Knowledge, (B) anxiety, (C) confidence in performing infection control skills, (D) perception of preparedness.

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Additionally, the correct answer rate of COVID-19 knowledge was 96.0% in the intervention group and 72.4% in the control group in the posttest, which improved in the intervention group compared to its correct answer rate (69.1%) and that of the control group’s (69.5%) during the pretest (See Supplementary Table 3). In particular, there was a significant difference in the correct answer rate between the two groups in the subcategories of sample management [98.6% (intervention group) vs. 35.7% (control group)] and isolation guidelines [95.5% (intervention group) vs. 49.2% (control group)].

Regarding confidence in performing infection control skills, there was a significant difference in the change between both groups over time (p = .005). After the intervention group received simulation-based training, their confidence in performing infection control skills significantly improved from 31.24 in the pretest to 35.59 in the posttest compared to the control group.

Regarding preparedness, the GEE results showed a significant difference in the change between the two groups over time (p < .001); the intervention group showed a significant improvement from 32.59 in the pretest to 48.77 in the posttest compared to the control group, confirming the effect of simulation-based education on preparedness.

Conversely, in the intervention group, the estimated mean value of anxiety was higher in the pre- and post-surveys than in the control group, and both groups showed a decreasing pattern in the post-survey. The GEE results showed a difference in the estimated mean of anxiety between the two groups, but the group × time interaction was not significant (p = .299). The anxiety of the intervention group, which was not statistically significant, decreased after the simulation-based training.

These results of the GEE analysis were consistent with those of the independent t-test. In the independent t-test, the differences before and after the intervention between the groups for the outcome variables showed significant changes in terms of knowledge (t = 10.35, p < .001), confidence (t = 5.52, p < .001), and preparedness (t = 7.10, p < .001), and insignificant difference in terms of anxiety (t = -0.39, p = .698) (See Supplementary Table 4).

Satisfaction and self-confidence in learning were measured only in the posttest in the intervention group (see Table 4). The degree of satisfaction for simulation-based education was high at an average of 23.62 ± 2.11. The item with the highest degree of satisfaction was: “the simulation provided me with a variety of learning materials and activities to promote my learning” at 4.86 ± 0.35. Self-confidence in learning was high at 36.22 ± 3.10, and the item with the highest score was: “my instructors used helpful resources to teach the simulation” at 4.84 ± 0.37.

Table 4
The Satisfaction and Self-Confidence in Learning of the Intervention Group after the Intervention (N = 37)

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