Psychological Safety in High-Fidelity Simulation Scale – Japanese Version

Abstract

The Psychological Safety in High-Fidelity Simulation Scale – Japanese Version (PS-HFS-J) is a specialized psychometric assessment instrument designed to measure the subjective degree of psychological safety experienced by undergraduate nursing students during immersive, simulation-based clinical education. Originally conceptualized and validated in South Korea by Park (2021), the scale was systematically adapted and psychometrically validated for Japanese nursing education contexts by Keisuke Nojima and colleagues in 2025. The cross-cultural adaptation process strictly adhered to the internationally recognized COSMIN (Consensus-based Standards for the selection of health Measurement INstruments) guidelines to ensure conceptual, linguistic, and cultural equivalence.

Comprising 14 self-report items across four correlated dimensions—Dealing with Uncertainty, Being Exposed, Being Unsupported, and Interpersonal Risk—the PS-HFS-J operationalizes the psychological climate of high-fidelity clinical simulation laboratories. Psychometric evaluation conducted with a sample of 263 undergraduate nursing students across all four academic years demonstrated exceptional measurement properties. The scale exhibited outstanding internal consistency reliability, reflected by an overall Cronbach’s alpha of 0.906. Test-retest reliability across a two-week interval among a subsample of 52 students yielded intraclass correlation coefficients (ICC) ranging between 0.859 and 0.914 across the subscales, indicating robust temporal stability.

Confirmatory factor analysis (CFA) using maximum likelihood estimation confirmed the theoretical four-factor structural model with exceptional fit indices: χ² test p = 0.142, Comparative Fit Index (CFI) = 0.990, Tucker-Lewis Index (TLI) = 0.988, and Root Mean Square Error of Approximation (RMSEA) = 0.026 (90% CI: 0.000–0.060). Content validity was demonstrated through rigorous expert panel evaluation, yielding item-level content validity indices (I-CVI) between 0.80 and 1.00 and an overall scale-level content validity index (S-CVI/Ave) of 0.94. The PS-HFS-J offers nurse educators, clinical preceptors, and simulation researchers a culturally attuned, methodologically rigorous diagnostic and evaluative tool to optimize prebriefing protocols, refine debriefing interactions, and promote high-engagement experiential learning.

Keywords

psychological safety, high-fidelity simulation, nursing education, psychometrics, cross-cultural adaptation, COSMIN guidelines, experiential learning, clinical decision-making, Japanese nursing students, confirmatory factor analysis

Authors

The Japanese adaptation and psychometric validation of the instrument were executed by an interdisciplinary team of nursing researchers and clinical simulation educators:

• Keisuke Nojima, PhD, RN (Corresponding Author)
  Affiliation: Faculty of Nursing, Kyoto Tachibana University, Kyoto, Japan
  Email: [email protected]
• Makoto Tsukuda, PhD, RN
  Affiliation: Department of Nursing, Hyogo Medical University, Nishinomiya, Japan
  Email: [email protected]
• Kosuke Kawamura, MSN, RN
  Affiliation: Faculty of Nursing, Kyoto Tachibana University, Kyoto, Japan
  Email: [email protected]
• Junko Honda, PhD, RN
  Affiliation: Research Institute of Nursing Care for People and Community, University of Hyogo, Akashi, Japan
  Email: [email protected]
• Mie Murozumi, PhD, RN
  Affiliation: Faculty of Nursing, Kyoto Tachibana University, Kyoto, Japan
  Email: [email protected]

Purpose

High-fidelity simulation (HFS) has emerged as a cornerstone of modern nursing pedagogy, bridging the gap between theoretical didactic curricula and high-stakes bedside clinical practice. By utilizing computer-driven human patient simulators, standardized clinical scenarios, and multi-parameter monitoring systems, HFS immerses learners in highly realistic clinical crises that demand rapid diagnostic reasoning, interpersonal communication, and technical dexterity (Lei et al., 2022; Vangone et al., 2024). However, the pedagogical power of HFS carries an inherent affective paradox: the very elements that make simulation effective—active observational scrutiny, complex peer team dynamics, video recording, and post-scenario debriefing—inherently place learners in positions of acute psychological vulnerability (Shearer, 2016; McKenna et al., 2019).

When learners perceive that making a clinical error will lead to interpersonal humiliation, academic penalty, peer ridicule, or reputational damage, their cognitive resources are diverted away from clinical problem-solving toward defensive threat-monitoring and self-protective disengagement. In the context of Japanese higher education, this vulnerability is frequently magnified by culturally grounded social norms, such as vertical hierarchy, high uncertainty avoidance, collective harmony, and the apprehension of public embarrassment or losing face (Tsuneyoshi, 2001; Banks, 2016). Historically, Japanese nursing educators lacked an empirically grounded, culturally sensitive instrument capable of assessing whether their simulation design, facilitator stance, and debriefing structures cultivated an emotionally secure environment conducive to reflective learning.

The primary purpose of the Psychological Safety in High-Fidelity Simulation Scale – Japanese Version (PS-HFS-J) is to address this critical methodological and educational void. Specifically, the instrument serves three complementary functions across academic and clinical education settings:

1. Diagnostic Assessment of Instructional Climates: It provides educators with a granular, multidimensional profile of how learners perceive psychological vulnerability across different stages of simulation, specifically distinguishing between the challenges of clinical ambiguity, observational exposure, instructional abandonment, and interpersonal friction.
2. Formative Quality Improvement of Simulation Curricula: The scale enables academic institutions and hospital training centers to evaluate the longitudinal efficacy of structural interventions—such as structured prebriefing contracts, psychophysiological warming exercises, or cognitive debriefing frameworks—on student security and engagement (Somerville et al., 2023).
3. Empirical Research in Healthcare Simulation: It facilitates cross-cultural and comparative investigations into how psychological safety mediates the relationship between simulation-based stress, cognitive load, reflective learning capacity, clinical self-efficacy, and actual clinical competency acquisition (Silva et al., 2022; Cho & Kim, 2023).

Psychological Construct

The core psychological construct operationalized by the PS-HFS-J is domain-specific psychological safety situated within immersive simulation-based healthcare learning. Rooted in organizational behavior and social psychology, psychological safety is broadly defined as an individual’s perception that their immediate learning or working environment is safe for interpersonal risk-taking (Edmondson, 1999). In a clinically simulated healthcare setting, this construct transcends mere comfort or absence of challenge; rather, it represents a conscious and subconscious appraisal that one can admit knowledge gaps, commit overt procedural or diagnostic mistakes, ask clarifying questions, and present unconventional hypotheses without facing ridicule, punishment, marginalization, or professional devaluation (Turner & Harder, 2023; Ito et al., 2022).

The PS-HFS-J conceptualizes psychological safety not as a monolithic, unidimensional state, but as a dynamic four-factor psychological architecture:

1. Dealing with Uncertainty

This subscale captures the learner’s affective and cognitive equilibrium when confronted with ambiguous, deteriorating, or unpredictable clinical scenarios. High-fidelity simulations deliberately introduce unstable patient trajectories where vital signs change abruptly, diagnostic cues are incomplete, or initial nursing interventions fail to stabilize the simulated patient. In a psychologically safe environment, students interpret uncertainty as a standard feature of clinical reality and an opportunity for shared inquiry. Conversely, a lack of safety causes ambiguity to be experienced as an acute personal failure, generating cognitive paralysis, emotional shutdown, or panic.

2. Being Exposed

This dimension assesses the acute sense of vulnerability, self-consciousness, and visibility experienced by the learner while performing clinical tasks under direct observation. In HFS suites, learners operate under the visual surveillance of faculty members behind one-way mirrors and peers observing through live audio-visual feeds. The ‘Being Exposed’ subscale measures the degree to which learners feel nakedly judged, scrutinized, or threatened by having their technical competencies, hesitation, or cognitive blind spots displayed in front of their academic cohort.

3. Being Unsupported

This factor evaluates the learner’s perception of instructional absence, isolation, or inadequate relational scaffolding from the faculty facilitators and the pedagogical structure. Simulation education relies heavily on the ‘safe container’ established during prebriefing. When students score high on ‘Being Unsupported’, they perceive that facilitators are acting as punitive evaluators rather than supportive learning partners, that clear psychological boundaries were not established prior to the scenario, or that assistance will be withheld when their distress exceeds manageable learning zones.

4. Interpersonal Risk

The final dimension quantifies the fear of negative social, academic, and peer-evaluative consequences resulting from active clinical participation, voicing opinions, or committing procedural blunders. In collective academic settings, students frequently calculate the social cost of speaking up. Interpersonal risk reflects the student’s anticipation of cohort alienation, loss of peer respect, diminished instructor favor, or long-term reputational damage within their nursing cohort.

Theoretical Framework

The conceptual foundation of the PS-HFS-J is anchored at the intersection of organizational psychology, educational constructivism, cognitive psychology, and culturally situated pedagogical theory.

Edmondson’s Team Psychological Safety Theory

The foundational bedrock of the instrument derives from Amy Edmondson’s seminal work on psychological safety in teams (Edmondson, 1999). Edmondson established that learning from failure requires an environment where individuals do not fear that acknowledging an error will undermine their status or safety. In healthcare simulation, this paradigm shifts from workplace teams to micro-learning teams. Learners must transition from impression management—where their cognitive effort is consumed by appearing competent—to learning behavior, wherein cognitive effort is devoted to error detection, collaborative correction, and authentic reflective exploration.

Kolb’s Experiential Learning Theory

The scale integrates David Kolb’s Experiential Learning Model, which posits that deep learning proceeds through a four-stage cycle: concrete experience, reflective observation, abstract conceptualization, and active experimentation (Kolb, 1984). High-fidelity simulation represents the concrete experience, while structured debriefing facilitates reflective observation and abstract conceptualization. If psychological safety is compromised during the concrete experience (e.g., through severe feelings of exposure or interpersonal threat), defensive cognitive mechanisms block honest reflective observation during debriefing, breaking the learning cycle and preventing meaningful conceptual integration (Walsh et al., 2022).

Cognitive Load Theory in Immersive Learning

Cognitive Load Theory (Sweller, 1988) provides an essential explanatory mechanism for the scale’s empirical necessity. Working memory has limited capacity. During high-fidelity simulations, intrinsic cognitive load (the complexity of clinical tasks) is naturally high. When an environment lacks psychological safety, it introduces massive extraneous cognitive load driven by affective distress, fear of negative evaluation, and hypervigilance. Measuring and mitigating these extraneous psychological threats frees working memory capacity for germane cognitive processing—the mental schematization and mental model restructuring that underpin clinical reasoning (Silva et al., 2022).

Cross-Cultural Pedagogy: The Japanese Educational Context

The theoretical adaptation from the original South Korean model (Park, 2021) into the Japanese context required an understanding of Japanese relational dynamics. Japanese instructional environments often reflect values of collectivism, social harmony (*wa*), and acute sensitivity toward public scrutiny (*sekentei*), paired with high deference to institutional authority (*sensei*) (Tsuneyoshi, 2001). In such settings, classroom silence is often weaponized as an adaptive defense mechanism to avoid discord, prevent public exposure of incompetence, and preserve interpersonal equilibrium (Banks, 2016). The PS-HFS-J explicitly models these cultural dynamics through its multidimensional capture of vulnerability, exposure, and social risk.

Validity

The validation of the PS-HFS-J was executed following the methodological framework established by the COSMIN standards, ensuring exceptional psychometric rigor across multiple validity domains (Mokkink et al., 2016).

Content and Face Validity

The cross-cultural adaptation involved a rigorous forward-backward translation protocol conducted by bilingual nursing researchers and linguistics experts. To evaluate content validity, a multidisciplinary panel of clinical simulation experts and nursing faculty reviewed each translated item for linguistic clarity, cultural nuance, and conceptual equivalence to the target construct. Quantitative content validity was determined using the Content Validity Index (CVI). The Item-level Content Validity Index (I-CVI) values across the 14 items ranged from 0.80 to 1.00, while the Scale-level Content Validity Index based on the average method (S-CVI/Ave) reached 0.94. These values comfortably exceeded the established psychometric threshold of 0.80, confirming that the scale comprehensively covers the conceptual domains of simulation-related psychological safety without extraneous or confusing items.

Construct and Structural Validity

Construct validity was evaluated through Confirmatory Factor Analysis (CFA) using empirical data collected from 263 undergraduate nursing students attending Japanese universities. The empirical data demonstrated an exceptional fit to the hypothesized four-factor structure (Dealing with Uncertainty, Being Exposed, Being Unsupported, and Interpersonal Risk). Standardized factor loadings across all 14 items were robust and statistically significant (p < 0.001), indicating that each item shares substantial variance with its designated latent construct.

Model Fit Parameters

Structural equation modeling demonstrated exemplary goodness-of-fit statistics that surpassed standard methodological benchmarks (Schreiber et al., 2006; Hair et al., 2019):

• Chi-Square (χ²) Test: The model yielded a non-significant chi-square value (p = 0.142), an impressive empirical outcome that suggests the hypothesized covariance structure does not significantly deviate from the observed data structure.
• Comparative Fit Index (CFI): The CFI was 0.990, substantially outperforming the conservative ≥ 0.95 benchmark for superior model fit.
• Tucker-Lewis Index (TLI): The TLI was 0.988, indicating outstanding parsimonious fit.
• Root Mean Square Error of Approximation (RMSEA): The RMSEA was 0.026 with a 90% confidence interval of 0.000 to 0.060, well below the stringent 0.05 cutoff for close model fit.

Reliability

The reliability of the PS-HFS-J was comprehensively evaluated through both internal consistency metrics and temporal stability analysis, verifying that the instrument produces dependable, replicable measurements.

Internal Consistency Reliability

Internal consistency was calculated for both the multidimensional composite scale and its constituent subscales using Cronbach’s alpha coefficient. The overall 14-item scale exhibited outstanding internal consistency, achieving a Cronbach’s alpha of 0.906. This demonstrates a high degree of interrelatedness among the items while avoiding excessive redundancy. The individual subscales demonstrated robust internal consistency values ranging from acceptable to excellent, corroborating the structural cohesion of each discrete dimension under the overarching umbrella of psychological safety.

Test-Retest Reliability and Temporal Stability

To evaluate whether the PS-HFS-J yields stable scores across time when the instructional environment remains unchanged, a longitudinal test-retest reliability study was conducted using a dedicated subsample of 52 undergraduate nursing students over a two-week interval. In alignment with COSMIN recommendations, temporal stability was evaluated using the Intraclass Correlation Coefficient (ICC) using a two-way mixed-effects model with absolute agreement (Koo & Li, 2016). The ICC values across the four subscales ranged from 0.859 to 0.914:

• Subscale ICCs exceeded the standard 0.75 threshold, falling firmly in the ‘good to excellent’ reliability bracket.
• These metrics confirm that the scale is not confounded by transient, random measurement error, making it suitable for pre- and post-intervention educational outcome studies.

Factor Analysis

The structural composition of the PS-HFS-J was investigated using Confirmatory Factor Analysis (CFA) with maximum likelihood estimation to test the theoretical four-factor framework established in the original scale by Park (2021).

Sample Characteristics

The validation cohort comprised 263 undergraduate nursing students enrolled in accredited baccalaureate nursing programs in Japan. The cohort represented diverse levels of clinical training and simulation exposure across all four academic years:

• First-Year Students: n = 44 (16.7%)
• Second-Year Students: n = 62 (23.6%)
• Third-Year Students: n = 88 (33.5%)
• Fourth-Year Students: n = 69 (26.2%)

Factor Architecture and Structural Relationships

The CFA modeled 14 observed variables loading onto four latent dimensions:

• Factor 1: Dealing with Uncertainty — Reflects learner confidence and cognitive security when navigating unstructured, unpredictable clinical events.
• Factor 2: Being Exposed — Measures self-conscious vulnerability, observation anxiety, and fear of being visibly scrutinized by peers and faculty.
• Factor 3: Being Unsupported — Measures perceptions of pedagogical neglect, unsupportive facilitator demeanor, or absence of relational containment.
• Factor 4: Interpersonal Risk — Represents social appraisal anxiety, including fears of peer condemnation, loss of academic standing, or negative interpersonal repercussions.

The correlations between the four latent factors were moderate to strong and conceptually congruent. Crucially, the factor correlations were not so high as to indicate multicollinearity or factor collapse, substantiating that while these four dimensions contribute to an overarching psychological safety climate, they remain distinct constructs that warrant individual scoring and diagnostic interpretation.

Instrument / Measurement Tool

The operational specifications of the Psychological Safety in High-Fidelity Simulation Scale – Japanese Version (PS-HFS-J) are summarized below:

• Test Type: Standardized self-report psychometric questionnaire / educational assessment inventory.
• Language: Japanese (validated from Korean original).
• Target Population: Undergraduate and graduate nursing students, licensed clinical nurses, interprofessional healthcare students undergoing high-fidelity simulation training.
• Administration Mode: Online electronic survey or paper-and-pencil questionnaire administered immediately post-simulation or post-debriefing.
• Completion Time: Approximately 3 to 5 minutes.
• Item Count: 14 items total.
• Response Format: 14 items self-report format measuring degrees of perceived security, exposure, and interpersonal risk.
• Subscale Architecture:
  • Dealing with Uncertainty
  • Being Exposed
  • Being Unsupported
  • Interpersonal Risk
• Scoring Guidelines: Subscale scores are derived by calculating the mean or sum of the items corresponding to each respective factor. An overarching composite score may be calculated to provide a global index of psychological safety within the simulation laboratory. Higher scores on positively valenced items indicate elevated psychological safety, whereas reverse-coded items assess vulnerability and threat perceptions.

Permissions & Fee and Test Year

Publication and Validation Year: 2025

Copyright and Translation Permissions: Formal institutional permission to translate, cross-culturally adapt, and validate the scale for Japanese nursing education was obtained directly from the original developer of the Korean instrument (Park, 2021) prior to study initiation.

Availability and Licensing: The Japanese adaptation was published in an open-access journal article (Nojima et al., 2025). The scale is intended for academic research, non-commercial educational evaluations, and curricular quality improvement. However, because the item text and proprietary scoring matrices remain protected under intellectual property guidelines, prospective users and academic investigators are requested to contact the corresponding author, Dr. Keisuke Nojima ([email protected]), to obtain the formal Japanese questionnaire form and testing authorization.

References

Abe, Y., Kawahara, C., Yamashina, A., & Tsuboi, R. (2013). Repeated scenario simulation to improve competency in critical care: A new approach for nursing education. American Journal of Critical Care, 22(1), 33–40. https://doi.org/10.4037/ajcc2013229

Banks, P. (2016). Behind Japanese students’ silence in English classrooms. Accents Asia, 8(2), 54–65.

Cho, M. K., & Kim, M. Y. (2023). Factors associated with student satisfaction and self-confidence in simulation learning among nursing students in Korea. Healthcare, 11(8), 1060. https://doi.org/10.3390/healthcare11081060

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. https://doi.org/10.1007/978-3-030-06031-2_16

Ito, A., Kurita, K., & Yumoto, Y. (2022). A concept analysis of psychological safety: Further understanding for application to health care. Nursing Open, 9(1), 467–484. https://doi.org/10.1002/nop2.1086

Kim, J., Park, J. H., & Shin, S. (2016). Effectiveness of simulation-based nursing education depending on fidelity: A meta-analysis. BMC Medical Education, 16(1), 152. https://doi.org/10.1186/s12909-016-0672-7

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.

Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

Lackie, K., Hayward, K., & Bainbridge, L. (2022). Creating psychological safety in interprofessional simulation for health professional learners: A scoping review of the barriers and enablers. Journal of Interprofessional Care, 36(2), 187–195. https://doi.org/10.1080/13561820.2021.1899146

Lei, X., Shen, Y., & Liu, X. (2022). Effects of high-fidelity simulation teaching on nursing students’ knowledge, professional skills and clinical ability: A meta-analysis and systematic review. Nurse Education in Practice, 60, 103306. https://doi.org/10.1016/j.nepr.2022.103306

Li, Y., Chen, H., & Wang, Y. (2022). High-fidelity simulation in undergraduate nursing education: A meta-analysis. Nurse Education Today, 111, 105291. https://doi.org/10.1016/j.nedt.2022.105291

McKenna, L., Boyle, M., Brown, T., Williams, B., Molloy, E., & Phillips, B. (2019). The influence of anxiety on student nurse performance in a simulated clinical setting: A mixed methods design. International Journal of Nursing Studies, 98, 57–63. https://doi.org/10.1016/j.ijnurstu.2019.06.006

Mokkink, L. B., Prinsen, C. A., Bouter, L. M., de Vet, H. C., & Terwee, C. B. (2016). The Consensus-based Standards for the selection of health Measurement INstruments (COSMIN) and how to select an outcome measurement instrument. Brazilian Journal of Physical Therapy, 20(2), 105–113. https://doi.org/10.1590/bjpt-rbf.2014.0143

Nojima, K., Tsukuda, M., Kawamura, K., Honda, J., & Murozumi, M. (2025). Psychological Safety in High-Fidelity Simulation Scale – Japanese Version. Nursing Reports, 15(7), 257. https://doi.org/10.3390/nursrep15070257

Park, J. (2021). Nursing students’ Psychological Safety in High-fidelity Simulations: Development of a new scale for psychometric evaluation. Nurse Education Today, 105, 105017. https://doi.org/10.1016/j.nedt.2021.105017

Park, J., & Park, M. (2021). Nursing students’ experiences of psychological safety in simulation education: A qualitative study. Nurse Education in Practice, 55, 103163. https://doi.org/10.1016/j.nepr.2021.103163

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338

Shearer, J. E. (2016). Anxiety, nursing students, and simulation: State of the science. Journal of Nursing Education, 55(10), 551–554. https://doi.org/10.3928/01484834-20160914-02

Silva, G. O., Oliveira, A. C., & Souza, M. C. (2022). Effect of simulation on stress, anxiety, and self-confidence in nursing students: Systematic review with meta-analysis and meta-regression. International Journal of Nursing Studies, 133, 104282. https://doi.org/10.1016/j.ijnurstu.2022.104282

Somerville, S., Hyland, S., & Webster, C. (2023). Twelve tips for the pre-brief to promote psychological safety in simulation-based education. Medical Teacher, 45(12), 1349–1354. https://doi.org/10.1080/0142159X.2023.2214305

Song, J., & Kim, Y. (2020). Effects of integrative simulation practice on nursing knowledge, critical thinking, problem-solving ability, and immersion in problem-based learning among nursing students. Korean Journal of Women Health Nursing, 26(1), 61–70. https://doi.org/10.4069/kjwhn.2020.03.15.1

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Tamaki, T., Sanhudo, O., & Kirita, N. (2019). The effectiveness of end-of-life care simulation in undergraduate nursing education: A randomized controlled trial. Nurse Education Today, 76, 1–7. https://doi.org/10.1016/j.nedt.2019.01.005

Tsuneyoshi, R. (2001). The Japanese model of schooling: Comparisons with the United States. Routledge.

Turner, S., & Harder, N. (2023). Psychological safety in simulation: Perspectives of nursing students and faculty. Nurse Education Today, 122, 105712. https://doi.org/10.1016/j.nedt.2023.105712

Vangone, C., Rossi, S., & Bellini, L. (2024). The efficacy of high-fidelity simulation on knowledge and performance in undergraduate nursing students: An umbrella review of systematic reviews and meta-analysis. Nurse Education Today, 139, 106231. https://doi.org/10.1016/j.nedt.2024.106231

Walsh, T., O’Connor, M., & Kelly, M. (2022). The use of guided reflection in simulation-based education with prelicensure nursing students: An integrative review. Journal of Nursing Education, 61(2), 73–80. https://doi.org/10.3928/01484834-20211213-01

Items of the Scale