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J Korean Acad Fundam Nurs > Volume 32(1); 2025 > Article
Hong, Bang, and Ferencsik: Effects of a Nursing Education Program Using Virtual Reality for Childbirth Nursing Care on Knowledge, Problem-Solving Ability and Nursing Performance in Nursing Students

Abstract

Purpose

This study evaluated the impact of integrating virtual reality (VR) and high-fidelity simulation (HFS) in childbirth nursing education on nursing students’ knowledge, problem-solving ability, nursing performance, and learning satisfaction.

Methods

A non-equivalent control group pretest-posttest quasi-experimental design was used. The participants included 41 third-year nursing students, with 21 in the experimental group and 20 in the control group. The experimental group received VR and HFS education, while the control group received HFS alone. The interventions were conducted over 3 weeks, and data collection occurred between May and August 2022.

Results

The experimental group showed significant improvements in knowledge, problem-solving ability, and nursing performance compared to the control group. Learning satisfaction was also significantly higher in the experimental group.

Conclusion

Integrating virtual reality into nursing education can improve learning outcomes and increase satisfaction. Expanding VR-based practical training programs is vital to addressing clinical site shortages and improving educational outcomes.

INTRODUCTION

Nursing education plays a crucial role in preparing skil-led nurses by combining theoretical foundations in the classroom with clinical practice opportunities, where students apply their knowledge in real-world settings [1]. However, the rapid increase in nursing programs and students has outpaced the availability of clinical sites, leading to a widening gap between theory and practice [2]. This gap is compounded by an overreliance on observation rather than hands-on experience due to limited clinical sites and opportunities, which restricts students’ ability to effectively translate theoretical knowledge into clinical practice [3].
In obstetric nursing, this gap is even more profound. South Korea's historically low birth rate, with a fertility rate of 1.05 in 2017, has significantly impacted nursing education by limiting students’ exposure to diverse childbirth experiences [4]. This scarcity, coupled with pregnant women's reluctance to involve students in their birthing processes, has further constrained clinical training opportunities [5]. As a result, traditional teaching methods, reli-ant on didactic and clinical experiences no longer suffice to equip nursing students with the necessary skills and problem-solving abilities required for childbirth care [4,6]. Furthermore, the rise in high-risk pregnancies has increased delivery room nurses’ responsibilities, emphasizing the need for advanced competencies and specialized education in labor nursing [7]. However, limited clinical sites further restrict nursing students’ opportunities to experience the full childbirth process, leading to fragmented learning. Therefore, alternate and effective educational approaches are essential to bridge these gaps and promote the development of clinical skills and critical thinking in labor nursing care.
Simulation-based education (SBE) has become a vital supplement to traditional clinical practice, by using realistic scenarios to improve technical skills, communication, nursing performance, critical thinking, patient safety, and confidence [8]. In obstetrics, simulation has demonstrated positive outcomes by introducing students to rare clinical scenarios, such as cesarean sections, vacuum extractions, dystocia management, and high-risk deliveries, which are difficult to experience in real-life settings [9]. However, the shift to remote learning during the COVID-19 pan-demic highlighted challenges associated with traditional SBE, including high initial costs, space limitations, and the need for small group participation, which limited its scalability for larger groups [10]. These logistical demands have reduced the SBE's benefits during the pandemic, emphasizing the need for innovative educational strategies in nursing education.
In response, virtual reality (VR) simulations have emerged as a complementary method, addressing re-source constraints of traditional simulation and enabling diverse, interactive, and realistic scenarios [9]. VR simulation offers several advantages over traditional simulation, such as reduced time and cost, scalability, and accessibility. Unlike traditional simulation, VR does not require extensive physical space, equipment, or direct fac-ulty supervision, allowing students to participate anytime and anywhere, aligning with current nursing education trends. VR creates highly immersive scenarios that range from common to rare conditions, providing students with low-risk, realistic settings that foster learning and build confidence in a safe environment [8]. Moreover, VR sup-ports self-directed learning, which is increasingly im-portant in modern nursing education, and prepares students for complex clinical environments by integrating innovative technologies. VR proved invaluable during the COVID-19 pandemic by maintaining students’ clinical competencies without in-person training. Immersive virtual reality applied in this study is a form of implementation that allows users to experience a three-dimensional virtual space synthesized by a computer while wearing a three-dimensional head-mounted display (HMD) device on their heads [11]. Immersive virtual reality maximizes immersion in that it allows users to see only the virtual world by blocking the external environment and is applied according to individual capabilities and needs, so that it can overcome the limitations of repetitive learning and time limits. However, as noted by Blizzard et al. [9], VR simulation alone may not fully enhance technical skills, highlighting the need for a more comprehensive approach.
To address these limitations, a new educational approach combining VR with high-fidelity simulation (HFS) has been developed, offering immersive clinical practice that enhances skill development while fostering critical thinking and problem-solving abilities. This integrated method is particularly promising for labor nursing care, where limited clinical sites and restricted exposure to complex scenarios present significant barriers to acquiring essential skills. In addition, with the recent expansion of virtual reality simulation, virtual reality simulation and offline learning methods such as HFS (SimJunior, Laerdal Medical, Stavanger, Norway) are being integrated to confirm their effectiveness [12].
Despite its potential, research on integrating VR with traditional simulation in labor nursing education remains limited. To address this gap, the present study developed an educational program using VR and HFS to evaluate its effectiveness in educating students on complex childbirth care. Findings from this study will provide nurse educa-tors with valuable insights into how an integrated approach can enhance competencies in labor nursing, ulti-mately enabling them to develop targeted intervention strategies to address challenges in obstetric nursing education.

METHODS

1. Design

This study employed a non-equivalent control group pretest-posttest quasi-experimental design to develop and evaluate the effectiveness of a VR and HFS-based childbirth nursing education program.

2. Participants

Participants were third-year nursing students recruited from A university in K city to represent students preparing for obstetric nursing practice. The inclusion criteria were: (1) currently enrolled as a third-year nursing student, (2) no prior experience with VR-based obstetric education, and (3) willingness to participate in both pre-and posttest assessments. The exclusion criteria included (1) students who had participated in other obstetric simulations within the last year and (2) those with known vis-ual or auditory impairments that might affect the VR experience. The final sample size was calculated based on a previous study [13] that utilized repeated-measures ANOVA for nursing students. Using G*Power version 3.1, with an effect size of 0.25, a power of .80, and a significance level of .05, the required sample size was determined to be 34 participants, 17 in the experimental group and 17 in the control group. To account for a potential 20% dropout rate, 22 participants were initially included in each group. However, three participants dropped out during the study, resulting in a total of 41 participants (21 in the experimental group and 20 in the control group).
As strict randomization was not feasible in the educational setting, participants were assigned to either the experimental or control group based on their availability for scheduled sessions to minimize selection bias. The experimental group received VR simulation before HFS, whereas the control group participated only in the HFS sessions.

3. Data Collection

Data were collected from May 10th to August 9th, 2022. Before the educational sessions, participants in both groups completed a pre-survey assessing general characteristics, nursing knowledge, problem-solving abilities, and nursing performance. The post-survey included assessments of nursing knowledge, problem-solving abil-ities, nursing performance, and learning satisfaction. The research procedure is outlined in Table 1.
Table 1.
Research Procedures
Sessions Experimental group Control group
1 Pretest: General characteristics, nursing knowledge, problem-solving ability, nursing performance (15 minutes) Orientation and prerequisite learning (100 minutes)
2 Lecture
  - Obstetric examination
  - Phases of labor
  - Postpartum care
3 ․ Individual learning (40 minutes) ․ Individual learning (40 minutes)
․ Team learning (40 minutes) ․ Team learning (40 minutes)
․ Core skills practice using a delivery model (40 minutes) ․ Core skills practice using a delivery model (40 minutes)
VR Simulation using HMD (Oculus)
․ Assessment
․ Pain care
․ Labor care
․ Postpartum care
4 Simulation practice using HFS (SimMom) (7∼8 teams/120 minutes)
Scenario assessment (20 minutes)
  - Case study scenario: First stage of labor
  - General physical assessment
  - Leopold's maneuver
  - NST monitoring (checking fetal heart rate)
  - Measuring the height of the fundus
  - Education on breathing technique management
  - Report on client's condition
Nursing interventions for anxiety and pain: Administering oxytocin
  - Intervention training (40 minutes)
  - Team learning and role play (30 minutes)
Core nursing skills using HFS (SimMom) (30 minutes) - Nursing skills related to the scenario
5 Debriefing and evaluation (100 minutes)
Posttest: Nursing knowledge, problem-solving ability, nursing performance (15 minutes)

HDM=head mount device; HFS=high-fidelity simulation, NST=nonstress test.

1) Pre-survey

A week before implementing the VR and HFS childbirth nursing program, a trained research assistant (RA) held an orientation for participants. A pre-survey assessed general characteristics, childbirth nursing knowledge, critical thinking, and nursing performance.

2) Intervention

In formulating the VR-based Childbirth Nursing Education Program, the researchers adhered to the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model [14]. The program focused on labor nursing, postpartum care, neonatal care, feeding methods, and immunization and was divided into theoretical and practical components, emphasizing postpartum care.
The childbirth nursing education program for the experimental group incorporated VR in conjunction with performance-based HFS education. A two-hour online session was conducted via Zoom during the initial week for the experimental before the VR simulation. The control group also received the online session before HFS. The content included uterine contractions, elements of labor, labor mechanisms, pain management nursing, and nursing related to fetal monitoring. The VR simulation was im-plemented in the following week. The participants used an HMD and a controller (Oculus) to proceed with the VR simulation phase. The program was structured into theoretical and practical components, emphasizing postpartum nursing care and labor pain management for nor-mal labor. The scenario featured a 35-year-old woman ex-periencing her first pregnancy. After attending regular an-tenatal checkups, she was admitted to the hospital following the onset of regular labor contractions. The scenario guided participants through the labor process, beginning with assessing initial symptoms and progressing to delivery and postpartum care. To integrate VR with HFS, students first engaged in VR-based theoretical learning, practicing procedures and nursing interventions in a virtual delivery room. The HFS session followed using the same scenario on a high-fidelity simulator SimMom by Laerdal (Stavanger, Norway). This combination enabled students to first familiarize themselves with the procedures virtually and then reinforce their skills in a hands-on envi-ronment. Small groups of three students connected to the VR room simultaneously, each following guided prompts to assess and manage labor pain and monitor labor pro-gression. Students practiced interpreting clinical data, choosing interventions, and performing nursing tasks with real-time feedback from the VR system.
The HFS conducted in both groups encompassed simulation operations and debriefing sessions, conducted for two hours per week over a span of three weeks. The scenarios were simulated using SimMom by Laerdal (Stavanger, Norway), with 22 participants divided into eight groups (one group of three) practicing for 15 minutes each. Participants acted as nurses, executing interventions like Leopold maneuvers, fetal monitoring, and medication administration. Debriefing involved reviewing video re-cordings, discussing nursing actions, sharing experiences, and providing feedback.

3) Post-survey

One week after the interventions were completed, a sur-vey regarding childbirth nursing knowledge, problem-solving abilities, nursing performances, and learning satisfaction was conducted simultaneously for both the experimental and control groups.

4. Measures

1) Childbirth nursing care knowledge

Childbirth nursing care knowledge was assessed using a 20-item tool developed by the researchers to capture knowledge specific to obstetric nursing. An expert panel of two professors specializing in women's health nursing, a certified midwife, and two delivery room nurses validated the items for content accuracy and relevance. The content validity index (CVI) for these items ranged from 0.8 to 1.0, indicating high agreement among experts on the tool's relevance. Reliability, as measured by Cronbach's ⍺, was .85 in this study, showing good internal consistency. The tool included seven items focusing on obstetric physiology (e.g., passageway, passenger, powers, position) and 13 items on childbirth nursing knowledge, including delivery phases. Each item was scored as 0 (incor-rect) or 1 (correct), resulting in a possible score range of 0 to 20, with higher scores representing a greater level of knowledge in childbirth nursing care.

2) Problem-solving ability

Problem-solving abilities were assessed using the Process Behavior Survey originally developed by Lee [15] and later modified by Park and Woo [16]. This 25-item instru-ment assesses five stages of problem-solving: ‘ problem discovery,’ ‘ problem definition,’ ‘ solution generation,’ ‘ implementation,’ and ‘ evaluation of problem-solving.’ Responses were scored on a 5-point Likert scale, with possible scores ranging from 25 to 125. Higher scores indicate better problem-solving ability. The tool's reliability was previously reported as .89 [16], and in this study, it demonstrated a Cronbach's ⍺ of .87, confirming high internal consistency.

3) Nursing performance

Nursing performance in childbirth was measured with a 24-item tool developed specifically for this study. The items spanned nursing tasks across the four stages of labor, covering procedures such as Leopold's maneuver, cervical dilation and effacement assessment, delivery phase management, and pain control. The tool underwent vali-dation by a panel comprising two maternal health nursing professors, two experienced delivery room nurses, and an obstetrician, achieving CVI scores between 0.8 and 1.0. The tool included eight items for the first stage of labor, ten items for the second stage and neonatal nursing, three items for the third stage, and three items for patient-nurse relationships. Each item was scored as 0 (not performed), 1 (performed inadequately), and 2 (performed adequately), with a maximum possible score of 48. Higher scores re-flected higher nursing performance capability. Cronbach's ⍺ for this tool in the present study was .78.

4) Learning Satisfaction

Learning satisfaction was evaluated using a modified version of Ko et al.'s lecture evaluation tool [17], adapted to assess satisfaction with the simulation-based education. This 12-item tool covered domains such as course oper-ation, teaching methods, assessment objectivity, and over-all class satisfaction, using a 5-point Likert scale. Higher scores indicated greater satisfaction with the learning experience. Reliability was high, with Cronbach's ⍺ reported at .89 in Ko et al.'s original study [17] and .91 in this study, confirming high reliability.

5. Ethical Considerations

This study received Institutional Review Board appro-val (IRB No: 1040191-201911-HR-013-01) and adhered to ethical standards, particularly considering that participants were students, a potentially vulnerable group. The study was conducted independently of any coursework, grading, or assessments to ensure no influence on academic standing. Participation was voluntary, with students in-formed that their involvement or withdrawal would not affect their grades or academic status. Informed consent included details on voluntary participation, anonymity, data use, and withdrawal rights. Control group participants were offered optional access to the VR-based childbirth education program after the study concluded to ensure ethical fairness. Finally, a small token of appreciation was provided to all participants following data collection.

6. Statistics Analysis

Data were analyzed using SPSS/WIN 27.0. General characteristics were assessed with frequencies, percen-tages, means, and standard deviations. Normality of variables was checked with Shapiro-Wilk test. Verification of sphericity was checked Greenhouse-Geisser, Huynh-Feldt. Homogeneity between the experimental and control groups was tested using x2 tests and independent t-tests. Paired t-tests were used to analyze the effects of pre- and post-education on childbirth nursing knowledge, prob-lem-solving, and nursing performance within each group. Independent t-tests were conducted to compare differences between the experimental and control groups before and after the simulation education. Repeated-measures ANOVA assessed the pre- and post-intervention effects on childbirth knowledge, problem-solving abilities, and nursing performance in both groups. Post-education evaluations and satisfaction were analyzed with independent t-tests. Tool reliability was confirmed using Cronbach's ⍺, with a p-value of <.05 considered statistically significant.

RESULTS

The general characteristics of the participants and the homogeneity of the dependent variables are summarized in Table 2. The research findings on the effects of education on nursing knowledge, problem-solving abilities, and nursing performance are presented in Table 3. The experimental group showed a significant difference in nursing knowledge, with scores rising from 10.71±1.62 points before education to 14.05±1.98 points after education, while the control group showed no significant change (F=6.14, p=.018). However, there was no significant difference in the interaction between the group and time regarding nursing knowledge (F=2.46, p =.125). The experimental group showed increases in problem-solving abilities (from 3.64±0.70 to 4.19±0.30) and nursing performance (from 33.62±7.92 to 42.90±4.83) after education. The control group had a slight increase in problem-solving abilities (from 3.78±0.34 to 3.87±0.47) and nursing performance (from 35.70±5.16 to 38.95±5.04) after education. No significant differences were found between the groups (F=0.72, p=.403; F=0.34, p=.561), but there were significant improvements within both groups before and after education in problem-solving abilities (F=39.00, p=.003) and nursing performance (F=46.99, p<.001). There was a significant interaction between group and time for prob-lem-solving abilities (F=5.26, p=.027) and nursing performance (F=10.89, p=.002).
Table 2.
Homogeneity Test of General Characteristics and Dependent Variables between the Experimental and Control Group (N=41
Variables Categories Exp. (n=21) Cont. (n=20) x2 or Fisher or t p
n (%) or M± SD n (%) or M± SD
Age (years) 21.38±1.63 21.10±1.48 0.58 .567
Gender Men 5 (23.8) 4 (20.0) 1.00
Women 16 (76.2) 16 (50.0)
GPA ≥4.0 7 (33.3) 2 (10.0) 6.01 .111
3.5∼3.9 5 (23.8) 11 (55.0)
3.0∼3.4 6 (28.6) 6 (30.0)
2.5∼2.9 3 (14.3) 1 (5.0)
Satisfaction with school life Unsatisfied 3 (14.3) 3 (15.0) 4.33 .115
Normal 14 (66.7) 7 (35.0)
Satisfied 4 (19.0) 10 (71.4)
Satisfaction with major Unsatisfied 4 (19.0) 3 (15.8) 4.33 .115
Normal 15 (71.4) 9 (47.4)
Satisfied 2 (9.5) 7 (36.8)
Nursing knowledge 10.71±1.62 10.45±1.93 0.48 .637
Problem-solving ability 3.64±0.70 3.78±0.34 -0.83 .415
Nursing performance 33.62±7.92 35.70±5.16 -1.00 .328

Cont.=control group; Exp.=experimental group; GPA=grade point average; M=mean; SD=standard deviation.

Table 3.
Pretest-Posttest Differences in Nursing Knowledge, Problem-Solving Ability, and Nursing Performance between the Experimental and Control Groups Over Time (N=41)
Variables Categories Exp. (n=21) Cont. (n=20) Source F p
M± SD M± SD
Nursing knowledge Pretest 10.71±1.62 10.45±1.93 Group 6.14 .018
Posttest 14.05±1.98 12.45±1.70 Time 39.37 <.001
Group/Time 2.46 .125
Problem-solving ability Pretest 3.64±0.70 3.78±0.34 Group 0.72 .403
Posttest 4.19±0.30 3.87±0.47 Time 39.00 .003
Group/Time 5.26 .027
Nursing performance Pretest 33.62±7.92 35.70±5.16 Group 0.34 .561
Posttest 42.90±4.83 38.95±5.04 Time 46.99 <.001
Group/Time 10.89 .002

Cont.=control group; Exp.=experimental group; M=mean; SD=standard deviation.

The research findings regarding the learning satisfaction after education are presented in Table 4. The experimental group showed significantly higher satisfaction, with a mean of 4.71±0.36, compared to the control group's mean of 3.71±0.32 (t=9.38, p<.001).
Table 4.
Differences in Learning Satisfaction between Groups (N=41)
Variable Exp. (n=21) Cont. (n=20) t p
M± SD M± SD
Learning satisfaction 4.71±0.36 3.71±0.32 9.38 <.001

Cont.=control group; Exp.=experimental group; M=mean; SD=standard deviation.

DISCUSSION

This study aimed to develop and validate a VR-based childbirth nursing education program to enhance nursing knowledge, problem-solving abilities, and performance. Findings showed that the experimental group, which received combined VR and HFS education, scored significantly higher in nursing knowledge, problem-solving abil-ities, and nursing performance than the control group, which received only HFS. This indicates that integrating VR with HFS offers significant advantages in childbirth nursing education over traditional simulation alone.
In this study, nursing knowledge improved in both groups, and there were significant improvements within both groups before and after education. However, there was no significant difference in the interaction between the group and time. The findings of this study align with prior research in maternal and neonatal nursing education that demonstrates significant improvements in pregnancy nursing knowledge [18] and neonatal nursing assessment [3] with the VR integration. Some studies on neonatal infection control knowledge and preeclampsia did not show significant differences [19]. This discrepancy may be due to variations in educational delivery methods, such as the use of YouTube in Weideman and Culleiton's study [18] and the extended intervention period lasting one semester in Kang et al.'s research [20], which allowed for repeated education and prolonged exposure. In addition, a meta-analysis by Chen et al.[8] confirmed the VR's efficacy in enhancing knowledge across various nursing specialties. In this study, both the experimental and control groups improved their problem-solving abilities. This result is consistent with the findings of Song and Kim [4], who reported that nursing students participating in VR-based labor nursing simulation education demonstrated higher problem-solving abilities. Similarly, Kim et al.[12] reported that VR simulation focused on asthmatic childcare substantially increased problem-solving ability scores. In addition, this study's findings resonate with prior research where VR-based nursing education improved neonatal nursing skills [21]. Similarly, in a study on VR-based pre-natal education, pregnant women demonstrated significantly higher scores in pregnancy health management practice behaviors [22], aligning with the outcomes ob-served in this study. These results further validated that VR simulation is a valuable educational tool for improving problem-solving skills through repeated practice in labor nursing education. Integrating VR and HFS simulation offers a more effective educational approach than relying solely on a single method. Even though the nursing performance in both experimental and control groups improved after the training, the group with integrated VR and HFS achieved higher nursing performance outcomes. In a comparative study, Kim et al. [12] showed that blended simulations substantially improve nursing performance in groups receiving HFS. Kim et al. [12] argued that using VR simulations before engaging in HFS enhances positive outcomes by building foundational knowledge and self-confidence. Moreover, engaging in immersive simulation allows students to acquire information through individual experiences, enabling them to prioritize nursing actions based on the specific knowledge gained. This further enhances self-confidence and practical application of skills in nursing practice [22]. A blended educational approach also promotes higher student satisfaction. Son and Choe [23] reported that nursing students learning in-travenous injection and indwelling catheterization techni-ques via VR and HFS expressed higher learning satisfaction levels than those receiving simulation or traditional lecture-based education. Chae [24] similarly found that in-corporating VR in psychiatric nursing classes increased students’ learning satisfaction. A study by Kim and Kim [25] employed VR with actual hospital environment foot-age that gave nursing students a sense of presence in clinical settings. This innovative approach allowed students to select their observation points and experiences, leading to higher engagement and significantly increased learning satisfaction [25]. By applying the immersive virtual reality program in this study, it was possible to improve the educational satisfaction of nursing students by conducting practical training according to individual competencies and needs [11].
However, some argue that the impact of blended approaches may vary based on a study sample's demographics. Bryant et al.[26] reported no significant difference in satisfaction level among graduate nursing students using VR simulation. This discrepancy could be due to differences in student demographics, as over 64% of participants in their study were over 36 years old. In contrast, this study's sample average was 21 years, representing ‘ digital natives’ [27] who are accustomed to and proficient in digital technology, likely contributing to enhanced learning outcomes and satisfaction.
Traditional simulation classes require large physical space for simulators, storage, and technologies. In contrast, VR simulation education is not constrained by location, does not require large physical spaces or extensive equipment, and can be accessed in a small educational setting with only an HMD device. The flexibility of VR also allows students to access their training anytime and anywhere, offering convenience and self-paced learning, which aligns with the modern nursing education trend. Song and Kim [4] argued that simulation-based nursing education provides students with invaluable opportunities to experience realistic scenarios to improve their prob-lem-solving skills. However, critical gaps remain, as current VR education programs do not sufficiently address knowledge and skills on preterm labor, dystocia, and high-risk pregnancies. Thus, educational programs are ur-gently needed to allow students to gain knowledge and develop hands-on skills to fill these gaps. VR content is an attractive learning medium for nursing students, allowing them to experience clinical environments safely, without the risk of patient safety incidents, and to engage in repeated learning anytime, anywhere, depending on the platform [11,12,25]. Continuous research and support are needed to enhance VR simulation education with advanced technology, fostering nursing students’ interest and engagement in their educational programs.
This study faced several limitations. The study sample, nursing students from a single university in a specific met-ropolitan area, posed challenges in generalizing the study results. In addition, this study measured outcomes only before and immediately after the intervention, limiting the ability to assess long-term effects. Future research should include follow-up evaluations to confirm the program's sustained impact.

CONCLUSION

This study demonstrated that a childbirth nursing education program using VR and HFS effectively enhanced students’ knowledge, problem-solving abilities, nursing performance, and learning satisfaction. Combining VR and HFS is a powerful and efficient strategy for advancing nursing education by providing immersive and hands-on learning experiences. To maximize the benefits of integrated simulation methodologies, developing systematic op-erational plans and guidelines is imperative to further enhance students’ knowledge, skills, and attitudes. Additionally, there is a growing need for diverse VR-based practical training programs in childbirth nursing, espe-cially given the shortage of clinical sites and limited opportunities for real-life exposure to complex scenarios. Ongoing long-term research focusing on the effectiveness of the blended approach to simulation education is required to validate and assess the sustainability of impacts over time.

CONFLICTS OF INTEREST

The authors declared no conflict of interest.

AUTHORSHIP

Study conception and design acquisition - Hong SJ and Bang HL; Data collection - Hong SJ; Data analysis & Interpretation - Hong SJ, Bang HL and Ferencsik L; Drafting & Revision of the manu-script - Hong SJ, Bang HL and Ferencsik L.

DATA AVAILABILITY

Please contact the corresponding author for data availability.

ACKNOWLEDGEMENTS

The authors would like to thank Prof. Cho Ae Jung for statistical advice.

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