Assessing the Effectiveness of a Virtual Reality-Based Simulation Program for Mental Health Nursing Practicum

doi:10.21203/rs.3.rs-3756375/v1

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Research Article

Assessing the Effectiveness of a Virtual Reality-Based Simulation Program for Mental Health Nursing Practicum

https://doi.org/10.21203/rs.3.rs-3756375/v1

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Introduction:

To develop a virtual mental nursing practice program and verify its effectiveness.

Methods

Participants were fifty-four fourth grade nursing students who completed more than one semester of theoretical courses in mental nursing with no psychiatric nursing clinical practice experience. The program comprised six themes; its effect was analyzed using repeated measures ANOVA.

Results

There were significant effects between groups and time points in mental disease-related knowledge, learning flow, and learning satisfaction.

Conclusions

The simulation education program was effective as an intermediate educational space connecting theoretical and field education. It can enhance students’ ability and confidence to interact with patients and perform therapeutic nursing.

Virtual Reality

Simulation Training

Psychiatric Nursing

Nursing Education

The prolonged coronavirus disease 2019 (COVID-19) pandemic has had tremendous repercussions worldwide on teaching and learning methods in various disciplines, including nursing. The spread of the virus significantly limited clinical practicum opportunities for nursing students. Clinical practicum is a vital component of the nursing curricula, as students acquire vital knowledge, skills, and insights during the process. Mental health nursing practicum is an essential process for nursing students as it teaches them interpersonal and communication skills, building positive relationships with people with mental disorders, and cultivating confidence and trust.²⁷ However, all mental health nursing practicum courses were canceled during the COVID-19 pandemic due to the high risk of mass outbreaks among patients with mental disorders.¹² The evolving healthcare environment focused on strengthening patient rights and patient-centered care has severely limited the scope of clinical practicum for nursing students.¹⁷

Online clinical practicums and those utilizing virtual reality (VR) and augmented reality (AR) have evolved due to the several benefits they offer, including no time- and space restrictions; a safe, real-world-like environment for learning; and relatively low costs.²³ With the rise of the fourth industrial revolution, VR and AR businesses have attracted significant public attention. Higher education institutions are now focusing on meeting the educational needs of the digital native generation, known as Generation MZ, who are among the primary consumers of higher education services.⁸

Currently, the VR technology is utilized for adult and pediatric clinical practicum courses and comprehensive simulation,¹⁸ as well as for skill learning, including intravenous injection.¹ Online and e-learning environments have been implemented for mental health nursing practicum,^15,28 but research on the use of VR is limited. Although VR simulation education has shown positive effects on focus and engagement, no framework has been formalized in nursing education.¹³ Research on the systematic design and development of various learning components based on the theoretical framework is also lacking. Moreover, there is a dearth of high-quality intervention studies based on systematic processes to measure the educational effectiveness of online practicum using VR and AR.¹¹

Ayres’² learning transfer model proposes that educational outcomes are largely determined by the motivation of the students to transfer knowledge and that educational outcomes are not solely influenced by student characteristics or educational content, but also by the students’ motivation to learn, which encompasses their expectations, preferences, and interests. Transfer motivation refers to the intention to apply learned knowledge and skills to clinical practice, and is proposed as an appropriate measure of the fundamental purpose of clinical practicum.²⁴ Online practicum has proven effective in enhancing learning motivation, satisfaction with the process, self-efficacy, transfer motivation, and problem-solving ability in nursing students.²⁶ It is a learning environment that encourages and facilitates learning transfer, characterized by the degree of supportive mechanical learning provided by peers or instructors. Once these basic components effectively motivate learning, transfer motivation is improved, which in turn facilitates the learning effects. Based on this learning transfer model,² we examined learning flow and learning self-efficacy (both considered as learning motivations), transfer motivation (representing the learning transfer process), mental illness-related knowledge, problem-solving, and learning satisfaction (outcomes of the learning transfer) to measure the effectiveness of a VR-based mental health nursing simulation program.

The systematic development of each learning element is crucial for using VR-based clinical practicum as an alternative to the traditional one, instead of being considered as a complementary option. Thus, this study aims to evaluate the effectiveness of a web-based mental health nursing practicum program using VR.

Aim

This study aimed to evaluate the effectiveness of a VR-based mental health nursing simulation program.

Study Hypotheses

The study hypothesizes the following:

Hypothesis 1

Learning flow will differ between nursing students who undergo the VR-based mental health nursing simulation and the control group.

Hypothesis 2

Learning self-efficacy will differ between nursing students who undergo the VR-based mental health nursing simulation and the control group.

Hypothesis 3

Transfer motivation will differ between nursing students who undergo the VR-based mental health nursing simulation and the control group.

Hypothesis 4

VR Knowledge of mental disorders will differ between nursing students who undergo the VR-based mental health nursing simulation and the control group.

Hypothesis 5

Problem-solving will differ between nursing students who undergo VR-based mental health nursing simulation and the control group.

Hypothesis 6

Learning satisfaction will differ between nursing students who undergo the VR-based mental health nursing simulation and the control group.

Study Design

This quasi-experimental study adopts a non-equivalent control group pretest-posttest design aiming to evaluate the effectiveness of a VR-based mental health nursing simulation program.

Study Participants

Participants were fourth-year nursing students who have taken at least one mental health nursing theory course and no mental health clinical practicum courses from one school in each of the three study regions. Only students who provided written informed consent using the online platform were enrolled. Sample size was calculated using the G*Power 3.1.3 software. With reference to previous studies, the minimum sample size to achieve an effect of .25, significance (⍺) of .05, and power (1-β) of .95 with two repeated measurements was calculated to be 27 per group, with a total of 54 participants. To account for potential dropouts, we enrolled a total of 58 students, with 29 in each group.

Instruments

The following instruments were used in the study:

Transfer Climate

Transfer climate is an environmental factor influencing learning motivation and was assessed using the Transfer Climate Scale developed by Ayres.² This 20-item tool uses a seven-point Likert scale to assess support and recognition by the instructor and support from peers, where a higher score indicates greater support from instructor or peers, which increases transfer motivation. The Cronbach’s ⍺ was .97 in the study by Ayres² and .96 in this study.

Learning Flow

Learning flow was assessed using the adapted Korean Learning Flow Inventory for adults developed by Kim et al.,¹⁴ based on the Flow Questionnaire developed by Csikszentmihalyi.¹⁸ This 29-item tool uses a five-point Likert scale. The Cronbach’s ⍺ was .95 at the time of development and .94 in this study.

Learning Self-Efficacy

Learning self-efficacy refers to an individual’s belief in oneself to utilize the contents learned.² The instrument developed by Ayres and adapted by Park and Kweon²⁴ was used in the study. This 10-item tool adopts a seven-point Likert scale, where a higher score indicates greater learning self-efficacy. The Cronbach’s ⍺ was .94 at the time of development and .92 in this study.

Transfer Motivation

Transfer motivation refers to one’s willingness to apply learned knowledge, skills, and insights in a work environment²²; to measure this, the instrument developed by Ayres² and adapted by Park and Kweon²⁴ was used. This 10-item tool uses a seven-point Likert scale, where a higher score indicates greater transfer motivation. The Cronbach’s ⍺ was .80 at the time of development and .92 in this study.

Mental Disorders Knowledge

We developed 20 items to measure individual knowledge on each module based on the study of Han et al.⁹ The content was validated by two mental health nursing professors and one expert with 10 years of psychiatric nursing professional experience, with the content’s validity index being above 0.95 for all items. A higher score indicates greater level of knowledge, and Cronbach’s ⍺ was .73 in this study.

Problem-Solving

Problem-solving was assessed using the tool developed by Lee et al.,¹⁹ comprising 30 items in five domains: clarification of problem (6 items), exploration of solution (6 items), decision making (6 items), execution of solution (6 items), and evaluation and reflection (6 items). Each item is rated on a five-point Likert scale, where a higher score indicates greater problem-solving ability. The Cronbach’s ⍺ was .93 at the time of development and .93 in this study.

Learning Satisfaction

Learning satisfaction refers to students’ perceived satisfaction of their learning needs, that is, the degree of providing educational contents that are perceived to be necessary by students. In this study, we modified and adapted the Online Educational Service Quality Scale developed by Bae³ for use on online classes. This 17-item tool uses a five-point Likert scale, with higher score indicating greater learning satisfaction. The Cronbach’s ⍺ was .95 at the time of development by Bae³ and .92 in this study.

Data Collection

The participants were recruited through a social-media post announcing recruitment on the online communities of nursing schools in three Korean regions. Data were collected from the volunteers only after signing an informed consent for participation in the study. A total of 58 participants were enrolled before being randomly assigned to either the experimental or the control group (n = 29 each). An explanation was provided to the experimental group via a video conference about the contents and procedure of the program, as well as the fact that participation is completely voluntary. The control group was provided with learning materials addressing the same topics as the ones taught in the program, with the opportunity to undergo the program after study completion. Both groups completed a baseline online questionnaire, which was later used for the post-test immediately after the program and six weeks later.

Implementation of the VR-Based Simulation Program

The VR-based simulation program was developed as an online program for mental health nursing practicum. It consists of six modules (delusion, hallucination, mania, geriatric depression, depression accompanied by suicidal ideation in adolescents, and obsessive-compulsive disorder (OCD). Each module comprises 11 stages (described in Table 1). The program is a web-based program, allowing students to access it via its website anytime and from any location. The participants were provided individual IDs and passwords to access the program and were able to record their learning activities for each stage on the website.

Table 1

*Structure of the VR-Based Simulation Program*
Stage	Learning activity	Contents
Stage 1	Pre-class activity	Pre-class activity for the mental illness addressed by each module. Includes definition, cause, behavioral characteristics, clinical manifestations, treatment, and nursing care for the mental illness.
Stage 2	Pre-class evaluation	Pre-class learning is evaluated through a quiz. Students can check correct and wrong answers.
Stage 3	Case study	The patient’s chart is presented. The provided information includes demographics, psychiatric history, physical assessment, mental health assessment, physician’s orders, medication records, and nursing records.
Stage 4	Simulation scenario	Students run the simulation via VR. The simulation encompasses patient’s admission management, admission assessment, behavioral characteristics displayed after admission, and the nurse’s interview of the patient. VR videos for each illness of the corresponding module are presented.
Stage 5	Mental health assessment	After examining the patient’s scenario, students perform a mental health assessment. Students are able to check and record the assessment results based on each case of the module.
Stage 6	Therapeutic communication analysis	VR video interaction between the patient and nurse is presented. After watching the video, students analyze the therapeutic communications used in the conversation.
Stage 7	Identification of nursing problems	This is the first step to resolve the patient’s nursing problem. First, the nursing problem is identified. Students then check the nursing problem at hand from a list of options.
Stage 8	Care plan	Students devise appropriate care plans after performing their nursing assessment for the patient’s nursing problem.
Stage 9	Nursing intervention	Students provide therapeutic nursing intervention, including pharmacological treatment, cognitive behavioral therapy, activity therapy, and other nursing interventions. They plan the therapeutic interventions appropriate for each module and record them.
Stage 10	Documentation	Students document their entire nursing process.
Stage 11	Post-class evaluation	Students take a quiz to be evaluated on their knowledge and skills acquired through the module.

The experimental group completed three modules per week to finish all modules over a two-week period. An online debriefing meeting was held twice a week for students to ask questions. The scenarios of the six modules were sent to the control group via email for individual learning, with an online meeting twice a week. The control group was also provided with individual IDs and passwords to access the program after the completion of the study.

Following the program orientation on week 1, the students were instructed to complete the first three modules (delusion, hallucination, geriatric depression) on days 1, 2, and 4, respectively. Online debriefings were held on days 3 and 5. In week 2, the students were instructed to complete the suicidal ideation, bipolar disorder, and OCD modules, and the online debriefings were held on days 3 and 5.

Statistical Analysis

The data were analyzed using the IBM-SPSS 22.0 statistical package. Participants’ demographic characteristic were analyzed with actual number, percentage, mean, and standard deviation. The homogeneity between the experimental and control groups was tested with a χ² test and an independent t-test, and the effectiveness of the program was analyzed using repeated measures ANOVA.

Ethical Considerations

This study was approved by the Institutional Review Board at the authors’ affiliation (BLINDED FOR REVIEW). The participants were informed of the purpose and method of the study. They agreed voluntarily to participate in the study and could freely withdraw at any time. The participants were also informed that the data will be collected anonymously and will be coded and managed in accordance with the Bioethics and Safety Act. All participants completed the consent form voluntarily online, and the control group was also given an opportunity to undergo the same program after the completion of data collection.

Participants’ General Characteristics and Homogeneity Testing

Table 2 shows the homogeneity of the general characteristics between the two groups. There were no significant differences in age (t=-0.07, p = .944), the type of virtual class taken previously (χ2 = 1.42, p = .491), clinical practicum experience (χ2 = 2.07, p = .491), perceived information technology device literacy (t = 0.37, p = .714), and satisfaction with the major (t = 0.00, p = 1.000) between the two groups (Table 1).

Table 2

*General Characteristics of Participants (N = 58)*
Characteristics	Experimental group (n = 29)	Control group (n = 29)	χ2/t	p
Characteristics	n (%) / Mean ± SD		χ2/t	p
Age (year)	22.00 ± 3.63	22.07 ± 3.84	-0.07	.944
Type of virtual classes
Synchronized online classes	4 (13.8)	6 (20.7)	1.42	.491
Asynchronized online classes (recorded lectures)	1 (3.4)	0 (0.0)
Both	24 (82.8)	23 (79.3)
Clinical practicum experience
Yes	29(100.0)	27(93.1)	2.07	.491
No	0(0.0)	2(6.9)	2.07	.491
Information technology device literacy	4.28 ± 0.75	4.21 ± 0.68	0.37	.714
Satisfaction with major	3.86 ± 0.92	3.86 ± 0.79	0.00	1.000

Homogeneity of Dependent Variables

There were no significant differences in transfer climate (t = 0.63, p = .534), learning flow (t = 1.10, p = .276), learning self-efficacy (t = 0.80, p = .429), transfer motivation (t=-0.45, p = .658), knowledge of mental disorders (t = 1.58, p = .119), problem-solving (t = 1.35, p = .182), and learning satisfaction (t = 0.72, p = .475) between the two groups (Table 3).

Table 3

*Differences in Dependent Variables Between the Groups (N = 58)*
Characteristics	Experimental group (n = 29)	Control group (n = 29)	t	p
Characteristics	Mean ± SD		t	p
Transfer climate	4.95 ± 0.49	4.86 ± 0.61	0.63	.534
Instructor support	5.06 ± 0.76	4.99 ± 0.76	0.36	.718
Peer support	4.84 ± 0.41	4.73 ± 0.61	0.81	.421
Learning flow	3.24 ± 0.51	3.06 ± 0.68	1.10	.276
Cognitive learning flow	3.55 ± 0.43	3.37 ± 0.68	1.20	.236
Affective learning flow	2.89 ± 0.72	2.72 ± 0.78	0.86	.392
Learning self-efficacy	5.83 ± 0.62	5.68 ± 0.81	0.80	.429
Transfer motivation	4.86 ± 0.97	4.97 ± 0.98	-0.45	.658
Knowledge of mental disorders	11.17 ± 2.87	10.00 ± 2.78	1.58	.119
Problem-solving process	119.76 ± 12.01	115.48 ± 12.10	1.35	.182
Clarification	4.03 ± 0.48	4.10 ± 0.46	-0.51	.612
Exploration of solutions	4.06 ± 0.46	3.84 ± 0.48	1.71	.093
Decision making	3.99 ± 0.48	3.77 ± 0.58	1.60	.116
Application of solution	3.92 ± 0.46	3.78 ± 0.51	1.12	.267
Evaluation and reflection	3.95 ± 0.52	3.76 ± 0.48	1.48	.143
Learning satisfaction	3.91 ± 0.55	3.81 ± 0.48	0.72	.475
Systematic organization	3.81 ± 0.58	3.70 ± 0.50	0.75	.455
Responsiveness	3.85 ± 0.65	3.68 ± 0.69	0.98	.331
Assurance	4.31 ± 0.55	4.34 ± 0.58	-0.23	.817

Effectiveness of the VR-Based Simulation Program

After completing the VR-based simulation program, learning flow significantly differed between the experimental and control groups (F = 6.36, p = .015), with significant differences appearing between the results immediately after the program and six weeks later (F = 4.00, p = .021), as well as a significant group and time interaction (F = 6.86, p = .002). Learning self-efficacy also showed significant differences between the experimental and control group (F = 11.72, p = .001), with no significant differences between time points (F = .52, p = .598), but a significant group and time interaction (F = 4.84, p = .010). Transfer motivation did not significantly differ between the experimental and control groups (F = 0.74, p = .394), but the scores significantly differed between time points (F = 4.87, p = .011). There was also a significant group and time interaction (F = 3.49, p = .034). Knowledge of mental disorders significantly differed between the two groups (F = 24.55, p < .001), and between time points (F = 11.73, p < .001), along with a significant group and time interaction (F = 12.85, p < .001). Problem-solving also showed significant differences between the two groups (F = 9.79, p = .003), but not between time points (F = 2.75, p = .068). There was a significant group and time interaction (F = 3.35, p = .039). Learning satisfaction also significantly differed between the two groups (F = 11.87, p = .001) and between time points (F = 8.10, p = .001). There was a significant group and time interaction (F = 9.23, p < .001).

The differences in the study parameters between the two groups at different time points are presented in Fig. 1.

The effects of the program continued until six weeks after the program (Table 4).

Table 4

*Differences in the Study Parameters Between the Two Groups by Time Point (N = 58)*
Category	Time	Experimental group (n = 29)	Control group (n = 29)	Source	F	p
Category	Time	Mean ± SD		Source	F	p
Learning flow	pre	3.24 ± 0.51	3.06 ± 0.68	G	6.36	.015
	post 1	3.39 ± 0.46	3.32 ± 0.68	T	4.00	.021
	post 2	3.66 ± 0.52	3.02 ± 0.52	G*T	6.86	.002
Learning self-efficacy	pre	5.83 ± 0.62	5.68 ± 0.81	G	11.72	.001
	post 1	6.12 ± 0.45	5.55 ± 0.79	T	0.52	.598
	post 2	6.10 ± 0.50	5.42 ± 0.67	G*T	4.84	.010
Transfer motivation	pre	4.86 ± 0.97	4.97 ± 0.98	G	0.74	.394
	post 1	5.26 ± 0.80	5.14 ± 0.87	T	4.87	.011
	post 2	5.47 ± 0.97	5.00 ± 0.68	G*T	3.49	.034
Knowledge of mental disorders	pre	11.17 ± 2.87	10.00 ± 2.78	G	24.55	< .001
	post 1	14.45 ± 3.22	10.03 ± 2.91	T	11.73	< .001
	post 2	13.69 ± 3.11	9.69 ± 2.41	G*T	12.85	< .001
Problem-solving	pre	119.76 ± 12.01	115.48 ± 12.10	G	9.79	.003
	post 1	125.86 ± 12.45	116.72 ± 15.47	T	2.75	.068
	post 2	125.86 ± 10.80	113.52 ± 11.79	G*T	3.35	.039
Learning satisfaction	pre	3.91 ± 0.55	3.81 ± 0.48	G	11.87	.001
	post 1	4.28 ± 0.47	3.97 ± 0.56	T	8.10	.001
	post 2	4.37 ± 0.29	3.70 ± 0.56	G*T	9.23	< .001
Note. G - Group, T - Time

Mental health nursing practicum requires students to be sensitive and delicate in their approach, as it involves direct therapeutic communication with mental illness patients. Thus, inadequate preparation during client interviews can result in negative outcomes for both the nursing student and client. Kim and Kim¹⁵ reported that many nursing students lack confidence and experience fear and anxiety during their first clinical practicum. Simulations can provide a possible solution to this problem, and their effectiveness has been reviewed by Cobbett and Snelgrove-Clarke.⁵ In essence, our VR-based simulation for mental health nursing practicum enabled nursing students to gain adequate experience before entering actual clinical setting. This approach expands the traditional nursing education beyond the classroom and hospital to a virtual online space, and our findings show that the program produced positive educational outcomes.

The VR-based mental health nursing simulation program was most effective in imparting knowledge of mental disorders and improving learning flow and learning satisfaction. We presume that the increased level of knowledge of mental disorders is attributable to the structure of the simulation, which consisted of 11 stages, including pre-class activities and pre-class quizzes to check students’ preparation for the simulation and post-class quizzes to assess their knowledge after completion. Increased level of knowledge is a frequently used outcome measure in simulation education.⁴

Learning flow was also significantly increased. Kim and Kim¹⁵ suggest that one reason for the improved learning flow among nursing students participating in a virtual simulation is that they actually have fun and are able to completely immerse in the simulation experience. Our program incorporated a gaming-like feature by using true or false questions with image cards to engage students even if they had no prior experience in simulations. Further, in stage 4, a virtual patient based on actual clinical cases was introduced in a video while exhibiting problem behaviors specific to the mental illness of the modules, and students were instructed to think about how they would engage in therapeutic communication with the patient if they were the patient’s nurse before entering their responses in a conversational tone. The use of such interactive features in the simulation is also speculated to have contributed to increasing students’ learning flow.

High learning satisfaction after participating in simulations has been extensively documented in previous research. Luctkar-Flude et al.²¹ reported that students showed high satisfaction with a virtual simulation about intravenous injections. Chae et al.⁴ also reported that students were satisfied after a simulation about a pediatric patient with asthma, rating it with 4.12 out of 5. High satisfaction with simulation is positively correlated with confidence in nursing performance. Thus, we recommend incorporating our VR-based simulation program to the mental health nursing practicum to improve the preparedness of the students for the practicum.

Although there were no significant differences in the learning self-efficacy and problem-solving scores between time points, there were significant differences between groups. Hur et al.¹⁰ reported that nursing students participating in an emergency care simulation showed enhanced problem-solving abilities and critical thinking skills, based on which they suggested that simulation education not only helps nursing students acquire essential nursing skills, but also improves their nursing-related cognitive abilities. As noted by Lee et al.,²⁰ education using VR helps nursing students self-direct their learning by identifying and applying the most effective nursing practices for each specific case, and evaluating the outcomes, and such successful learning experience increase students' self-efficacy for learning. Similarly, Verkuyl et al.²⁹ reported that virtual simulation education using games increased nursing students' self-efficacy and satisfaction.

Transfer motivation showed no significant difference between groups but did differ between time points. Reilly and Spratt²⁵ suggested that incorporating simulation in clinical practicum courses enables students to acquire knowledge and skills that can be transferred to clinical practice, ultimately fostering their nursing competence. Zulkosky³⁰ suggested that one key strength of simulation education pertains to its interactive learning environment, which can provide learners with comprehensive learning effects across various domains, including mental, cognitive, spiritual, and emotional, as well as increasing knowledge in both the motor and affective domains. In recent years, the scope of VR-based educational simulation has been further expanded through the use of haptic sensors, which can provide tactile sensations complementing visual and auditory stimulation, thereby creating more vivid experiences for the user.

Based on these findings, we recommend utilizing simulation education more extensively in nursing education. The benefits of the VR-based simulation apply to our mental health nursing simulation program as well. Mental health nursing practicums are especially challenging, as the number of psychiatric units available for nursing is very limited even when including closed and open wards in larger secondary hospitals comparable to university hospitals. Moreover, the duration of such practicum is typically short, usually lasting only one to two weeks, making it difficult for students to encounter a wide range of cases other than patients who are hospitalized at that time. This significantly undermines the quality of the mental health nursing practicum, further highlighting the need to implement virtual simulations in the process.

This study aimed to develop a VR-based simulation program for mental health nursing practicum using actual clinical scenarios and examine the effectiveness of the program on nursing students. Our results show that all six study parameters had group and/or time effects. Nursing students are more comprehensively prepared and have more experience for the mental health nursing practicum compared to other clinical practicum courses because of encountering patients with mental illnesses and engaging in direct therapeutic communication with these patients during the practicum. Hence, we hope that our simulation program can serve as an intermediary educational platform that bridges theoretical education and clinical practicum, where students are able to engage in self-directed learning before and during clinical practicum to improve their competency and confidence in interacting with clients, and provide therapeutic nursing care in actual clinical practice. Based on our findings, we recommend that future studies incorporate haptic devices in mental health nursing simulations to enable students to experience both verbal and physical nursing care that resembles real-world situations. Moreover, although we developed a simulation program specifically for the mental health nursing practicum, we recommend subsequent studies to develop simulations enhancing nursing students' clinical reasoning skills by comprehensively addressing adults, children, older adults, community, and nursing management from the scenario development stage to promote a holistic approach in patient care.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

None.

Funding Disclosure Summary

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1077285). The funding agency had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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No competing interests reported.

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Assessing the Effectiveness of a Virtual Reality-Based Simulation Program for Mental Health Nursing Practicum

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Abstract

Introduction:

Methods

Results

Conclusions

Figures

Introduction

Aim

Study Hypotheses

Methods

Study Design

Study Participants

Instruments

Transfer Climate

Learning Flow

Learning Self-Efficacy

Transfer Motivation

Mental Disorders Knowledge

Problem-Solving

Learning Satisfaction

Data Collection

Implementation of the VR-Based Simulation Program

Statistical Analysis

Ethical Considerations

Results

Participants’ General Characteristics and Homogeneity Testing

Homogeneity of Dependent Variables

Effectiveness of the VR-Based Simulation Program

Discussion

Conclusion

Declarations

References

Additional Declarations

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