Effects of standards of best practice in simulation-based training among midwifery students in China

Abstract

Aim

This study aimed to determine whether simulation training designed according to INACSL Standards improves satisfaction, self-confidence, and perceptions of simulation design and educational practices among midwifery students in China.

Background

Midwives are crucial in reducing maternal and neonatal mortality, and their effectiveness is directly impacted by the quality of midwifery education. Simulation training enhances midwifery students’ clinical competence, but research on applying INACLS standards in the China is limited.

Design

A quasi-experimental study was conducted at Zhejiang Chinese Medical University from January 2023 to June 2024, using a historical control design.

Method

A total of 129 students were divided into two groups: the control group (n = 67) received standard simulation training while the intervention group (n = 62) followed the INACSL Standards. Data were collected using three scales: Student Satisfaction and Self-Confidence in Learning Scale along with Simulation Design Scale (SDS) and Educational Practices in Simulation Scale (EPSS).

Results

The intervention group had significantly higher scores in student satisfaction (median difference=-0,200, 95%CI=[-0.400,0.0], P = 0.040) and active learning (mean difference=-0.130, 95%CI=[-0.222, -0.374], P = 0.006). The SDS sections of “objective/ information” (median difference=-0.200, 95%CI=[-0.400,-0.200], P < 0.001). and “guided reflection/ feedback” resulted in higher scores for the intervention group (median difference=-0.250, 95%CI=[-0.500,-0.250], P < 0.001) which contributed to a greater total SDS score (mean difference=-0.163, 95%CI=[-0.242,-0.084], P < 0.001). Self-confidence and other dimensions of the SDS and EPSS did not demonstrate any significant differences.

Conclusions

Simulation training based on INACSL standards enhances students’ satisfaction and active learning through clear goals and structured feedback. Future studies should extend the intervention duration and integrate teaching methods to improve students’ self-confidence and professional skills.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12909-025-08081-y.

Introduction

Globally, midwives play a critical role in avoiding and lowering stillbirths and maternal and newborn mortality [1]. The quality of midwifery education directly determines their effectiveness, making them a vital tool for enhancing the health of mothers and newborns in a variety of contexts [2, 3]. The International Confederation of Midwives (ICM) identifies education as one of three critical pillars in developing the midwifery workforce [4]. The ICM Global Standards for Midwifery Education place a particular focus on developing midwives’ practical skills [5, 6]. Advanced midwifery education began relatively later in China as undergraduate programs in this field started nationally in 2017. Zhejiang Chinese Medical University is one of the first four schools allowed by the Chinese Ministry of Education to establish a bachelor’s degree in midwifery [7]. A study [8] revealed that the core competencies of undergraduate midwifery students in China are generally at a moderate level, with relatively lower performance in the area of clinical skills.

Simulation training is a teaching methodology that provides students with the opportunity to practice skills, demonstrate procedures, and apply theoretical knowledge in a safe, controlled, and realistic environment, thereby bridging the gap between theory and clinical practice. The effectiveness of simulation training has been well-established, particularly in midwifery education, where it significantly raises students’ clinical competency. Studies have also shown that simulation enhances the critical thinking, clinical skills, and teamwork of midwifery students [9, 10]. Additionally, it has been found to enhance self-confidence, self-efficacy, and satisfaction [11–13]. A recent meta-analysis [14] has shown positive effects of simulation on knowledge, clinical judgment, and communication. These studies highlight the importance of simulation training in addressing the gap between theory and practice in midwifery education.

In 2011, the National Council of State Board of Nursing (NCSBN) conducted a nationwide study [15] in the United States, which showed that up to 50% of traditional clinical hours can be replaced with simulation-based teaching. The study also outlined three important requirements for effective simulation: adherence to INACSL best practice standards, high-fidelity scenarios, and theory-based feedback [15]. Farina [16]and Diaz-Navarro [17] further emphasize that structured frameworks are essential for quality simulation outcomes. A recent global consensus [18] further underlines the importance of adhering to distinct standards and guidelines during simulation-based teaching, including International Nursing Association for Clinical Simulation and Learning (INACSL) [19], the Association for Simulated Practice in Healthcare (ASPiH) [17], and the Association of Standardized Patient Educators (ASPE) [20], as well as the Healthcare Simulationist Code of Ethics [21]. Collectively, these studies have shown that standardized frameworks are important to ensuring simulation quality in healthcare education.

The INACSL conducts worldwide operations to improve simulation-based education and promote patient safety measures. The Healthcare Simulation Standards of Best Practice™ (HSSOBP) were first established in 2009 and have been periodically updated to reflect advancements in the field [22–24]. The latest version [19], published in 2021, encompasses eleven different aspects which are professional development, pre-briefing, simulation design, facilitation, debriefing process, operations, outcomes & objectives, professional integrity, simulation-enhanced interprofessional education (Sim-IPE), evaluation of learning & performance, and simulation glossary. HSSOBP provide a comprehensive framework that guides the design, facilitation, debriefing, and evaluation of simulation activities. These standards emphasize a structured approach to simulation, ensuring that each phase-from pre-briefing to outcomes assessment-is systematically addressed to optimize learning and performance [25].

In China, the INACSL Standards of Best Practice™ were first introduced in 2017 [26]. Studies [27–29] in nursing programs demonstrated improved student satisfaction and self-confidence.Despite the proven benefits of simulation training and its growing adoption in nursing education, there is a lack of empirical research on the use of INACSL Standards of Best Practice™ in midwifery education in China. Given the unique educational context and recent establishment of midwifery undergraduate programs, examining the impact of these standards is both timely and essential.

In light of the growing emphasis on simulation-based education and the recognized importance of adhering to the INACSL Healthcare Simulation Standards of Best Practice™, this study aims to determine whether simulation training designed according to INACSL Standards improves satisfaction, self-confidence, and perceptions of simulation design and educational practices among midwifery students in China. By addressing a significant gap in the literature, the findings aim to inform improvements in simulation practices and contribute to better educational outcomes for midwifery students in China.

Materials and methods

Study design

This quasi-experimental study was conducted among midwifery students at School of Nursing, Zhejiang Chinese Medical University, from January 2023 to June 2024 (Fig.1).

Fig. 1.

Flow diagram of the study design

Participants and sample size

This study employed a cluster sampling method. Initially, a total of 131 midwifery students in grades 2020–2021 were invited as study participants. During their third year at university, every student engaged with simulation training. Students in grade 2020 (67 students) were assigned to the control group, which received standard simulation training. While students in grade 2021 (64 students) were assigned to the intervention group, which received simulation training based on HSSOBP. However, two students from the intervention group withdrew midway because they took a leave of absence and therefore did not finish all the surveys. The study was completed with 129 (98.5%) participants.

Every student who maintained regular enrollment status received an invitation to join the study. The exclusion criteria for midwifery students were as follows: who left school during the research period; students who took continuous leaves for over two weeks. Both groups followed the same midwifery curriculum, were in their third year of study, had identical previous course schedules, the same teaching team, similar prior clinical exposure, and neither group had prior simulation training based on INACSL standards.

Students received full explanations about the study objectives while being reminded that their participation was completely optional and would hold no impact on their future educational outcomes or final course grades. Every student who took part in the study submitted their written informed consent.

Based on previous research [30], a total sample size of 120 students was calculated, with 60 students per group. This calculation was based on a power of 90%, a Type I error level of 0.05, and an effect size of 0.6, using G*Power Version 3.1 statistical software.

Educational intervention

Teaching team formation

The educational intervention was designed and delivered by a multidisciplinary team. The teaching team reviewed literature, books, and electronic databases, with a particular focus on the Healthcare Simulation Standards of Best Practice (HSSOBP) [19] for the overall design and considerations of the simulation training program. The teaching team responsible for designing and implementing the obstetric simulation training cases consisted of one obstetrician, one midwifery expert and two simulation instructors. The criteria for inclusion in the teaching team were as follows: ① at least ten years of work experience; ② a master’s degree or higher; ③ primary work and research focus in obstetric nursing, obstetric management, or obstetric education. This multidisciplinary team ensured that the simulation scenarios were clinically relevant and educationally effective, meeting the teaching standards of the midwifery profession.

Simulation training needs assessment

The initial stage according to " Standards of Best Practice “ [25] requires evaluating simulation training requirements according to midwifery students’ training goals and clinical position requirements. We can determine content for simulation training that maximize students’ learning outcomes by evaluating existing courses and their deficiencies alongside student and teacher feedback and available teaching resources.

Construction of simulation training objectives

The construction of simulation training objectives involves integrating the outcomes of needs assessments with the competence levels of the teaching subjects, course aims, and teaching materials to create clear, specific, and attainable goals [31]. These objectives were designed to be Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) [31], ensuring they effectively guided the simulation activities and aligned with the broader educational goals of the midwifery students’ program. They comprehensively cover the fundamental requirements related to students’ knowledge and skills, as well as their emotional attitudes and values throughout their learning journey.

Development of simulation scenarios[25]

Guided by the Healthcare Simulation Standards of Best Practice (HSSOBP), our teaching team collaboratively developed four high-fidelity obstetric scenarios: normal vaginal delivery, postpartum hemorrhage, pregnancy-induced hypertension, and abnormal labor. The educational intervention in this study was implemented in the laboratory courses of “Comprehensive Midwifery Skills”, totaling 12 class hours. Each scenario was meticulously crafted to replicate authentic clinical workflows while aligning with core midwifery competencies.

Drawing from real clinical cases and guided by course objectives, the team constructed detailed case outlines that preserved both clinical authenticity and educational integrity. Every scenario was designed to present learners with realistic clinical challenges while targeting essential professional knowledge and clinical skills. Case-specific tasks were formulated to center on student learning outcomes, ensuring tight integration with required clinical competencies. Prior to full-scale simulation training the teaching team conducts preliminary trial runs and limited-scale drills to ascertain task feasibility [25].

Pre-experiment preparation[32]

Pre-briefing

A standardized pre-briefing outline was created to ensure that students were well-prepared for the simulation activities. The briefing content includes simulation space, equipment, simulation process, simulation format, simulation case information, roles (learners/instructors/standardized patients), time allocation, general and specific objectives, guided feedback process, evaluation methods, confidentiality principles, virtual agreement, and limitations, etc. This structured briefing was tailored to the prior knowledge level of the students, ensuring they understood the simulation context and were ready to engage effectively [33].

Simulation environment setup

A high-fidelity simulation environment was established to replicate an authentic delivery room setting. The setup included a high-fidelity birthing simulator, essential obstetric instruments, and a control room with standard functionality for instructor observation, scenario manipulation, and vital sign monitoring. Detailed technical specifications of the equipment and control room configuration are provided in Supplementary Material S1.

Simulation training team and student preparation

The simulation training team is made up of classroom instructors and those who have experience teaching in clinical settings. Simulation training team [34] members should possess knowledge of simulation training theory and skills alongside clinical practice experience to guide students effectively through simulation practice sessions. The experimental preparation team ensures both the environment and equipment remain operational throughout instructional sessions. Students prepare [33] for simulation training by learning simulation methods and researching pertinent knowledge while they develop their skills in advance through participation in guided experimental groups. The pre-experiment preparation also included a structured orientation to the simulation-based learning environment, addressing participant roles, expectations, recording equipment usage, observational procedures, evaluation methods, and environmental factors.

Implementation of guided simulation training

A week before implementing simulation training students must receive both their course schedule and recommended reading materials and relevant online resources [33]. Students participate in the teaching simulation after instructors assign their roles through a random lottery system before the simulation begins.

During pre-simulation guidance teams discuss case information while students prepare to explore the simulation laboratory and learn about necessary instruments and equipment.

During simulation cases instructors oversee student activities and provide corrections only when necessary while avoiding direct intervention in student problem-solving. The high-fidelity simulation parturient shows proper changes in vital signs and clinical results which depend on student performance of their tasks.

Instructors evaluate student learning objectives achievement by observing student performance and providing guided feedback [35]. Effective simulation training depends on instructors providing guided feedback. During simulation cases instruction leaders lead group discussions to encourage students to reflect on their own performance as well as their peers’ performance while guiding them to summarize their experiences and perform critical analysis of their actions in the simulation. Before feedback sessions begin instructors focus on teaching safety principles while building trust and fostering open dialogue. The Gather-Analyze-Summarize (GAS) model [36] recommended by the “Best Practice Standards” is applied for guided feedback, which consists of three stages: information gathering, problem analysis, and summarization (in Table 1).

Table 1.

Guided feedback-GAS model

Phrase	Question
Gather	i. How do you feel about the entire simulation case teaching? ii. Which aspects of the patient’s medical history and manifestations are worth paying attention to? What assessments did you conduct? iii. Before administering uterine contraction drugs, what assessments do you need to perform to ensure safety? iv. As an observer, what nursing measures did you see other students implement for the patient?
Analyze	i. What were your thoughts behind making this decision? ii. Why did your group decide to implement this measure? Can you explain the reasons? iii. How should we communicate when calling the team or contacting other relevant personnel? iv. What aspects do you think you/other students did well in? What areas need improvement? What difficulties were encountered?
Summarize	i. If you had the chance to do it again, how would you handle this situation differently? ii. What did you learn from this experience? What lessons or experiences can you share? iii. What are the areas that the team or individuals need to work on?

Student evaluation

The establishment of the evaluation method occurred before simulation training began and its alignment covered learning objectives alongside outcomes and learner levels [37]. The teaching team engaged a self-created evaluation checklist (in Table 2) to fully evaluate the students during simulation drills and guided feedback sessions [37]. The evaluation tool covered four dimensions: The evaluation tool measured students through their participation in simulation drills along with their knowledge and skills assessment, attitude evaluation, and engagement in guided feedback discussions. The evaluation tool functions across different cases and situations while maintaining a common standard for assessing students’ learning achievements. In previous usage experiences, the evaluation tool has a discrimination coefficient of 0.332, indicating good differentiation ability. The evaluation results from different teachers are highly correlated (r = 0.780, P < 0.05) and show good consistency (ICC = 0.825, P < 0.05).

Table 2.

Student evaluation form for simulation teaching scenarios

Primary Indicator		Secondary Indicator	Evaluation Criteria
Dimension	Weighting Coefficient
Participation in simulation scenarios	0.4	1.1 Actively participates in simulation scenarios	Excellent (A) 40 points, Good (B) 32 points, Fair (C) 20 points, Poor (D) 0 points
Knowledge & skills	0.4	2.1Demonstrates proficient and effective application of learned knowledge 2.2 Demonstrates awareness and capability to conduct comprehensive assessments of pregnant women (or new mothers) 2.3 Provides accurate and timely analysis of the condition, with clear prioritization of response measures 2.4 Exhibits skilled, standardized operational techniques, strong aseptic consciousness, and ability to complete tasks on time 2.5 Offers accurate, comprehensive, and timely health guidance
Attitude	0.1	3.1 If training is conducted in groups, the group leader demonstrates leadership, and members show good teamwork 3.2 Demonstrates clinical thinking, ability to identify, analyze, and solve problems 3.3 Uses communication skills proficiently and effectively 3.4 Shows a caring attitude, concern for pregnant women and their families, and reflects professional ethics	Excellent (A) 10 points, Good (B) 8 points, Fair (C) 5 points, Poor (D) 0 points
Participation in debriefing discussions	0.1	4.1 Actively participates in debriefing discussions
Total score(100 points)

Measurement instruments

The research team collected demographic data (age, gender and location of family location) post-simulation. Quantitative outcomes were measured using three validated scales developed by the National League for Nursing (NLN) and the Laerdal multisite project group: the Student Satisfaction and Self-Confidence in Learning Scale, the Simulation Design Scale (SDS), and the Educational Practices in Simulation Scale (EPSS) [38]. Chinese scholar Wang translated these tools into Chinese in 2013 [39] and researchers used the Chinese versions in this study.

Students’ satisfaction and self-confidence

This 13-item scale assesses student satisfaction with simulation activity through five items and evaluates self-confidence in learning with eight items. The measurement tool includes a Likert-type scale that ranges from 1 representing strong disagreement to 5 representing strong agreement. The Cronbach’s alpha test showed reliability coefficients of r = 0.72 for satisfaction and r = 0.94 for self-confidence in Chinese version [39].

Educational practices in simulation scale (EPSS)

The EPSS is a 16-item tool designed to evaluate student learning outcomes in simulation-based training and assess the extent to which evidence-based best practices are implemented in simulations. The scale operationalizes four measurable dimensions: active learning (10 items), diverse ways of learning (2 items), high expectations (2 items), and collaboration (2 items). Participants rate items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Reliability was confirmed via Cronbach’s alpha; for presence of specific practices, r = 0.87, and importance of specific practices, r = 0.94 [39].

Simulation design scale (SDS)

The SDS is a 20-item instrument designed to evaluate student experiences in simulation-based learning and assess the structural components of simulation design [40]. Participants completed the tool after participating in the simulation. The evaluated design elements include: objectives/information (5 items), student support (4 items), problem solving (5 items), guided reflection and feedback (2 items), and fidelity (2 items). The instrument also uses a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). Reliability was established using Cronbach’s alpha, yielding coefficients of 0.88 for feature presence and 0.89 for feature importance [39].

Data collection and statistical analysis

The “Wenjuanxing” platform (https://www.wjx.cn/) which specializes in questionnaire services collected the survey data for this study. An electronic questionnaire was distributed to all participants through student QQ groups initially to gather basic personal information. To ensure accurate and complete responses, the survey links were individually sent to each participant via one-on-one messaging in QQ, and confirmation was obtained upon completion. After completing the course, students in both groups received another electronic survey link, to evaluate their simulation training experience using the three scales. All the data were collected by researcher (BBM). When students had questions about the survey items, researcher (BBM) immediately explained the items in detail. The platform was configured to allow questionnaire submission only after participants completed all items. In addition, each participant received a confirmation message upon successful submission, which helped ensure data integrity and participation verification.

Data analysis was conducted using IBM SPSS Statistics 28.0. Continuous variables were assessed for normality using the Shapiro-Wilk test and for homogeneity of variances via Levene’s test. If the assumptions were met, results were reported as mean ± standard deviation (Mean ± SD) and analyzed with an independent samples t-test; otherwise, they were presented as median (interquartile range) [M (P25, P75)] with the Mann-Whitney U test for comparison. The Hodges-Lehmann estimator was utilized to calculate the median difference and its confidence interval. Effect sizes were determined using the rank-biserial correlation coefficient (r) [41]. Categorical variables were represented as frequency (percentage) [n (%)], with group comparisons performed using the chi-square test (χ² test), and Fisher’s exact test was applied when theoretical frequencies were less than 5. The effect size was assessed using Cramér’s V. All statistical tests were two-sided, and p-values < 0.05 were considered statistically significant.

Ethics consideration

This study was approved by the Research Ethics Committee at the Nursing School of Zhejiang Chinese Medical University (202550228-1) and conducted according to the Helsinki Declaration. Prior to participation, all students received a comprehensive explanation of the study’s purpose, procedures, potential risks, and benefits. Written informed consent was obtained from all participants. They were assured that participation was voluntary and that they could withdraw from the study at any time without any negative consequences to their future educational outcomes or course grades. To ensure confidentiality, all data were anonymized during collection and analysis. Access to the data was restricted to the research team, and all electronic data were stored on password-protected computers.

Results

Participants’ characteristics

A total of 129 midwifery students participated in the study (control group n = 67, intervention group n = 62). Both groups had an average age of 21 years. Gender distribution differed between groups (control group: 97.0% female; intervention group: 85.5% female). The differing gender distribution between groups can be explained by yearly random gender enrollment. Most participants were from rural areas (in Table 3).

Table 3.

Demographic characteristics of participants (n = 129)

Items		Control group (n = 67)	Intervention group (n = 62)	Test statistics	P	Effect size	Mean difference/95% CI
Age (mean ± SD)		21.01 ± 0.50	20.99 ± 0.71	t = 0.206	0.837	Cohen’s d = 0.306	0.022 [−0.190,0.234]
Gender n (%)	Female	65(97.0)	53(85.5)	χ2 = 5.489	0.026*b	V = 0.206	/
	Male	2(3.0)	9(14.5)
Location of the family n (%)	Urban	17(25.4)	17(27.4)	χ2 = 0.326	0.850	V = 0.050	/
	Rural	45(67.1)	39(62.9)
	Township	5(7.5)	6(9.7)
Grade 1–2 Average GPA [M(P25, P75)]		3.03 (2.73, 3.46)	3.02 (2.65, 3.39)	Z=−0.646	0.518	r = 0.005	0.058a [−0.130,0.224]

*P < 0.05, ^aMedian difference, ^bUsing fisher’s exact test

Students’ satisfaction and self-confidence

The study measured student satisfaction and self-confidence levels in both the intervention and control groups for comparison. For student satisfaction, the intervention group demonstrated significantly higher scores than the control group, with a median of 4.30 (P25 = 4.00, P75 = 4.60) versus 4.00 (P25 = 3.80, P75 = 4.40). The median difference was − 0.200 (95% CI: −0.400 to 0.0), and this difference was significantly significant (P = 0.040). According to Table 4, self-confidence measures showed no statistically significant difference between the intervention and control groups.

Table 4.

Comparison of students’ satisfaction and self-confidence between study groups

Items	Control group (n = 67)	Intervention group (n = 62)	Test statistics	P	Effect size	Mean difference/95% CI
Student Satisfaction [M(P25, P75)]	4.00 (3.80, 4.40)	4.30 (4.00, 4.60)	Z = 2.050	0.040*	r = 0.016	−0.200a [−0.400,0.0]
Self-Confidence (mean ± SD)	3.69 ± 0.30	3.76 ± 0.28	t=−1.283	0.202	Cohen’s d=-0.226	−0.066 [−0.167,0.036]

*P < 0.05, ^aMedian difference

Educational practices in simulation scale

The intervention group achieved a significantly superior active learning score on the Educational Practices in Simulation Scale (4.18 ± 0.23) than the control group (4.05 ± 0.29), with a mean difference of 0.130 (95% CI: −0.222 to −0.374, P = 0.006). The groups showed no significant differences in collaboration, diverse ways of learning high expectations and total scores according to Table 5.

Table 5.

Comparison of educational practices in simulation scale between study groups

Items	Control group (n = 67)	Intervention group (n = 62)	Test statistics	P	Effect size	Mean difference/95% CI
Active learning (mean ± SD)	4.05 ± 0.29	4.18 ± 0.23	t=−2.783	0.006*	Cohen’s d=−0.490	−0.130 [−0.222, −0.374]
Collaboration [M(P25, P75)]	4.50 (4.00, 5.00)	4.50 (4.00, 4.63)	Z = 0.451	0.652	r = 0.003	0a[0,0]
Diverse ways of learning [M(P25, P75)]	4.0 (3.50, 4.50)	4.0 (4.0, 4.13)	Z = 0.768	0.442	r = 0.060	0a[0,0]
High expectations [M(P25, P75)]	4.0 (4.0, 4.50)	4.0 (4.0, 4.50)	Z = 0.227	0.821	r = 0.002	0a[0,0]
Total Scores [M(P25, P75)]	4.08 (3.93, 4.38)	4.18 (4.00, 4.36)	Z = 1.545	0.122	r = 0.012	−0.075a [−0.175,0.025]

*P < 0.05, ^aMedian difference

Simulation design scale

The intervention group demonstrated significantly higher scores than the control group in two dimensions of the Simulation Design Scale: the objective/information section (median: 4.20 [P25 = 4.0, P75 = 4.40] vs. 4.0 [P25 = 3.60, P75 = 4.20], median difference:−0.200, 95% CI: −0.400 to −0.200, P < 0.001) and the guided reflection/feedback section(median: 4.38 [P25 = 4.25, P75 = 4.75] vs. 4.0 [P25 = 4.0, P75 = 4.25], median difference:−0.250, 95% CI: −0.500 to −0.250, P < 0.001).

Support and problem-solving measures revealed no significant differences between groups and fidelity remained consistent across both groups. The intervention group achieved a significantly higher total SDS score (4.20 ± 0.20) than the control group, which scored 4.03 ± 0.25, with a mean difference of −0.163 (95% CI: −0.242 to −0.084, P < 0.001) (as shown in Table 6).

Table 6.

Comparison of simulation design scale between study groups

Items	Control group (n = 67)	Intervention group (n = 62)	Test statistics	P	Effect size	Mean difference/95% CI
Objective/information [M(P25, P75)]	4.0 (3.60, 4.20)	4.20 (4.0, 4.40)	Z = 3.476	< 0.001*	r = 0.027	−0.200a [−0.400,−0.200]
Support [M(P25, P75)]	4.0 (3.75, 4.0)	4.0 (3.75, 4.25)	Z = 1.344	0.179	r = 0.010	0a [−0.250,0.0]
Problem-solving [M(P25, P75)]	4.0 (3.80, 4.40)	4.20 (4.0, 4.20)	Z = 1.744	0.081	r = 0.014	−0.200a [−0.200,0.0]
Guided reflection/feedback [M(P25, P75)]	4.0 (4.0, 4.25)	4.38 (4.25, 4.75)	Z = 4.658	< 0.001*	r = 0.036	−0.250a [−0.500,−0.250]
Fidelity [M(P25, P75)]	4.0 (4.0, 4.50)	4.0 (4.0, 4.50)	Z = 0.412	0.681	r = 0.003	0a[0,0]
Total Scores (mean ± SD)	4.03 ± 0.25	4.20 ± 0.20	t=−4.074	< 0.001*	Cohen’s d=−0.718	−0.163 [−0.242,−0.084]

*P < 0.05, ^a Median difference

Discussion

According to the findings, the implementation of INACSL Standards of Best Practice led to significant improvements in both student satisfaction and active learning during simulation training. The intervention group achieved higher scores in the Simulation Design Scale (SDS) for the “objective/information” and “guided reflection/feedback” dimensions. While these individual dimensions showed significant improvements, their combined impact likely contributed to the higher total SDS score observed in the intervention group. These improvements highlight the effectiveness of clear objectives and structured debriefing in promoting student engagement and learning. However, no significant differences were found in self-confidence, collaboration, or other SDS dimensions.

Simulation training can recreate the real obstetric working scenarios and psychological states in a safe environment and can also create tense emergency events. It has gained wide recognition in enhancing the professional skills, self-confidence, and satisfaction of midwifery students [11, 42]. Gaining a sense of confidence and satisfaction before graduation can prepare nursing students, as well as midwifery students, to face complex clinical environments and establish a positive belief that facilitates rapid adaptation to clinical nursing work, thereby aiding in their career development [43, 44]. The results of this study indicate that simulation training based on INACSL is superior to traditional simulation training methods in enhancing midwifery students’ satisfaction, aligning with findings from several other studies [27, 45–50].

The results of this study show that simulation training based on INACSL best practice standards does not significantly enhance students’ self-confidence compared to traditional simulation training. This result should be interpreted within the unique cultural and educational context of China, where a hierarchical teaching culture—characterized by a teacher-centered instructional approach—may inhibit the development of student self-confidence and their engagement in simulation-based learning.In such a context, students are often accustomed to passive knowledge acquisition rather than active participation and self-directed learning, which are essential for building self-efficacy in simulation. Even with a structured simulation experience, these deeply ingrained cultural norms may still hinder gains in self-confidence. Future interventions should consider incorporating strategies to foster a more participatory and supportive learning environment to enhance students’ self-confidence.

It is worth noting that Gabbouj et al. [51] emphasize that students’ self-confidence is closely linked to specific simulation design characteristics, notably “Support,” which shows a strong correlation with self-confidence. This suggests that well-supported simulation environments can bolster learners’ confidence levels. Consistent with this, our study also found no significant differences between groups in the “Support” dimension of the Simulation Design Scale (SDS). This may also explain the reason why simulation training based on INACSL best practice standards has show no difference in significant improving “self-confidence” compared to traditional simulation training. These finding points to a specific gap in our current simulation design that future efforts must address. In future, simulation instructional design should strengthen support and guidance for students across different phases of simulation-based activities—including pre-briefing, during-simulation accompaniment, and extended post-simulation reflection and feedback. Such comprehensive and continuous support can help better build students’ practical competence and ultimately enhance their self-confidence.

In our study, by following structured phases, the educational intervention reinforced that the simulation training was systematic, effective, and followed the"Best Practice Standards”, which enhanced the learning experience and outcomes for the midwifery students. Meanwhile, adherence to the “Best Practice Standards” emphasized the role of feedback mechanisms within simulations, which also contributed to student satisfaction. There is also evidence that suggests that facilitation of guided reflection as part of a simulation training had a positive impact on student satisfaction. It is important to ensure that debriefing and feedback are provided in a standardized and effective way to have the most educational benefit [52, 53]. The integration of feedback within debriefing process and following best practice standards would have a positive impact on the learning as well as the students’ satisfaction.

According to INACSL standards [31], simulation activities promote the development of learners’ knowledge, skills, and attitudes by providing realistic and immersive experiences that promote active learning. In our study, the intervention group used INACSL Standards of Best Practice and the mean in in the active learning dimension of EPSS was significantly higher than the control group (P = 0.006). The INACSL standards [25] emphasize the importance of designing simulation activities that require students to actively participate, solve problems, and make decisions. This study suggests that following the INACSL standards effectively promotes active learning in midwifery students. The results of this study were aligned with the findings of Parekh de Campos [54], which showed that simulations developed according to the HSSOBP™ is an effective form of practical experiences in palliative care for active learning. This study established a better learning environment for students by following elements such as clear objectives, guided reflection, and structured feedback, and made the learning experience more engaging and effective. The simulation training became more than just a passive experience; it became an interactive and dynamic learning opportunity. The results of this study highlight the potential benefits of integrating active learning strategies into simulation-based training programs, particularly in the context of midwifery education. Studies [55] demonstrates that active learning methods enhance cognitive and practical skills. Through repeated simulation-based practice in assessing maternal-fetal status and managing obstetric emergencies, students develop core competencies that directly improve their ability to manage real-world complications-such as postpartum hemorrhage and neonatal resuscitation—potentially reducing maternal and neonatal morbidity and mortality.

Simulation design is a crucial element in simulation-based training. The INACSL Standards of Best Practice provide comprehensive guidelines for simulation design, including the construction of measurable objectives, the use of various types of fidelity to create realism, and the planning of a learner-centered facilitative approach. Evaluating the design of scenario simulation training aims to continuously refine and enhance the scenario simulation training process [56]. In this study, the intervention group, which followed the HSSOBP, showed significant improvements in the “Objective/information” and “Guided reflection/feedback” sections of the Simulation Design Scale (SDS) compared to the control group. The scenario simulation training design used for the intervention group achieved clear objectives along with sufficient pre-simulation briefing information and timely effective guided feedback strategies that enabled students to conduct behavior analysis and internalize knowledge and skills. This suggests that clear objectives and high-quality feedback are essential in improving the effectiveness of simulation training. The clarity of objectives allows students to understand what is expected of them, while structured feedback allows students to reflect on their performance and learn from their mistakes. These findings are similar to other literature [57, 58] that supported the value of clear objectives and feedback in simulation-based training. The structured feedback allows students to internalize lessons from both successes and errors, forging stronger mental models for clinical judgment. This habit of reflective practice is essential for continuous improvement in clinical quality and safety.

According to the Healthcare Simulation Standards of Best Practice, simulation-based experiences are most effective when they are designed to achieve specific, shared objectives, which guide the entire process from preparation to debriefing [33]. In particular, pre-briefing is emphasized as an essential preparatory step that involves preparation and briefing tailored to the purpose and objectives of the simulation [33]. This step aims to familiarize learners with the scenario content, clarify expectations, and set the stage for meaningful engagement. Pre-briefing functions as a preparatory mechanism which allows nursing students to achieve their learning objectives before participating in a simulation exercise [59]. According to a meta-analysis study, high-fidelity simulation pre-briefing has been shown positive outcomes for student learning and clinical performance [60].

The literature on simulation and debriefing in healthcare education emphasizes the importance of debriefing as a pedagogical strategy to improve learning outcomes. Dufrene et. al. [61] emphasized that debriefing is essential for meaningful learning and the development of clinical reasoning. Levett-Jones et. al. [62] conducted a systematic review that evaluated the overall effectiveness of simulation debriefing in health professional education, emphasizing the need for evidence-based frameworks.

This study found no significant differences across multiple dimensions of the Simulation Design Scale and Educational Practices in Simulation Scale. The SDS “Support” dimension and the EPSS “Collaboration” dimension exhibited no substantial differences between them. Although the INACSL standards provide a robust framework, the implementation of these standards may require more nuanced approaches to effectively enhance these aspects. The support structures and collaborative opportunities provided in the simulation environment may need to be more explicitly designed and facilitated to have a measurable impact on student outcomes. These are details that need to be considered in future research.

Limitations and recommendations for future research

The study has several limitations. This study has several limitations. First, the use of cluster sampling restricted participants to midwifery students from the Nursing School of Zhejiang Chinese Medical University, limiting the generalizability of the findings. This single-institution context may not fully capture the variability in educational experiences and outcomes across different institutions and regions. Thus, the findings should be interpreted with caution and within the context of this specific educational environment.

Second, the historical control design-rather than a randomized controlled trial—may introduce selection bias. To improve representativeness and validity, future studies use rigorous randomized controlled trials with participants recruited from various institutions and geographical areas around the country. Additionally, students received only 12 class hours of simulation training in this study. Whether a long-term training duration could improve midwifery students’ self-confidence and clinical skills also requires further studies.

Third, the gender distribution between the control and intervention groups was statistically significant (P = 0.026), with the control group having a higher proportion of female students. This gender imbalance may have influenced the study results, as gender can impact learning styles, self-confidence, and engagement in educational activities. Future studies should aim to balance gender distribution or account for gender differences in their analyses to ensure more robust and generalizable findings.

Furthermore, the instructors who conducted the simulation training also performed the evaluations, which may introduce assessment bias. Evaluators were not blinded to the group allocation, which could have influenced the results. Future studies should consider using third-party evaluators to minimize this potential bias.

Finally, all outcome variables (e.g., satisfaction, confidence, perception of design) were self-reported, which may lead to response bias or social desirability effects. In this study, we mitigated this limitation by using an anonymous survey approach. Future research should consider incorporating more objective data (such as actual performance assessments, observer data, etc.) to reduce the potential biases associated with self-reported data.

Conclusion

Simulation training based on INACSL standards of best practice improve student satisfaction through clear goal setting while offering structured feedback and promoting active learning with reflection opportunities. However, the brief intervention period limited its impact on student self-confidence. Future studies should extend the intervention duration and explore integrating additional teaching methods to comprehensively improve students’ self-confidence and professional skills. Furthermore, universities should consider integrating INACSL standards into their midwifery curricula in a systematic manner, ensuring that all aspects of the standards are addressed and implemented effectively.

Abbreviations

ICM

International Confederation of Midwives

NCSBN

National Council of State Board of Nursing

INACSL

International Nursing Association for Clinical Simulation and Learning

ASPiH

Association for Simulated Practice in Healthcare

ASPE

Association of Standardized Patient Educators

HSSOBP

Healthcare Simulation Standards of Best Practice™

NLN

The National League for Nursing

EPSS

Educational Practices in Simulation Scale

SDS

Simulation Design Scale

Authors’ contributions

All authors listed have made significant, direct, and intellectual contributions to the research. Lewei Tu, Huijie Wu, and Xinyi Xia were responsible for drafting the manuscript. Xuehua Zhu and Binbin Mei contributed to the design, implementation, and evaluation of the educational intervention. Yan Zhang focused on the experimental preparations. Huijie Wu and Mengyan Xu participated in the design of the educational intervention. Yeqin Yang provided overall design and oversight of the study. All authors reviewed and approved the final manuscript.

Funding

This project was funded by the “14th Five-Year” Teaching Reform Project for General Undergraduate Colleges and Universities in Zhejiang Province, Department of Education of Zhejiang Province (Grant number: jg20220302). The funder had no role in the design, conduct, or writing of this research.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Zhejiang Chinese Medical University (202550228-1) and followed the ethical principles of the Helsinki Declaration. The researchers gained written informed consent from all participants in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.