Application Effects and Optimization Strategies of Multi-Scenario Simulation Teaching in Practical Instruction of Infectious Diseases

Summary

This randomized controlled trial evaluated multi-scenario simulation teaching in infectious disease education among 121 medical and nursing students. Compared with traditional teaching, simulation improved OSCE performance, compliance with critical actions, clinical reasoning, teamwork, and procedural safety. The approach effectively bridged theory and practice under biosafety constraints and enhanced training outcomes.

Abstract

This prospective randomized controlled trial evaluated the effectiveness of multi-scenario simulation teaching in improving practical competence among medical and nursing students during infectious disease training. A total of 121 students were randomly assigned to a simulation group (n = 60) or a traditional teaching group (n = 61). The simulation curriculum covered key clinical workflows, including triage, isolation, specimen collection, infection prevention, antimicrobial stewardship, and occupational exposure management. Primary outcomes were post-intervention Objective Structured Clinical Examination (OSCE) scores and critical action compliance rates. Secondary outcomes included clinical reasoning, teamwork and communication skills, learner satisfaction, self-efficacy, engagement, and cognitive load. Compared with the control group, the simulation group achieved significantly higher OSCE scores (mean difference 8.1; 95% CI, 5.4–10.9; Cohen's d = 1.14; P < 0.001) and critical action compliance rates (10.5% higher; 95% CI, 7.3–13.6; Cohen's d = 1.23; P < 0.001). The simulation group also demonstrated improved teamwork, greater satisfaction and self-efficacy, and lower cognitive load. These findings indicate that multi-scenario simulation teaching enhances practical competence, safety-critical performance, and learning experiences in infectious disease education.

Introduction

As a specialized branch of clinical medicine, the teaching process for infectious diseases has long been restricted by two factors: public health safety and medical quality^1,2. In current clinical teaching practice, the high pathogenicity of infectious diseases and strict biosafety regulations have naturally formed an obstacle that limits medical students' in-depth hands-on practice in front-line isolation wards to a large extent. With the continuous improvement of global infection control standards, the traditional bedside teaching model has become increasingly difficult to meet the requirements for observing students' clinical practice in respiratory infectious diseases or highly virulent infectious diseases safely; it is often impossible to complete a full observation process³. Moreover, the distribution of infectious disease cases shows clear seasonality, regional differences, and sudden outbreaks, making it difficult for students to encounter a wide range of infectious diseases during their short clinical internship. This shortage of case-exposure opportunities creates a significant gap between theoretical knowledge and practical clinical application for medical students, especially when dealing with new and sudden infectious diseases. The lack of necessary clinical exposure experience has become a bottleneck restricting the cultivation of public health professionals^4,5.

Traditional infectious disease education primarily uses theoretical lectures or single teaching aids, and the model lacks connections when training medical students to handle complex clinical situations. Firstly, there is a deficiency in training for process chains. Management of real infectious diseases covers the entire closed-loop process, including triage, isolation, specimen collection, epidemic reporting, precise treatment, and post-disease follow-up. However, traditional teaching has long centered on etiological diagnosis and failed to pay attention to the reporting procedures prescribed by laws and regulations or the operational details of infection control^6,7. Given the persistent clinical challenges surrounding hand hygiene, proper PPE usage, and safety-critical compliance, robust training that actively measures and reinforces these standard precautions is paramount for novice practitioners⁸. According to relevant assessment data, the procedural error rate of beginners who have not received systematic simulation training and the risk of occupational exposure in clinical operations under high-level protective measures are significantly higher. Secondly, there is a superficiality in the development of teamwork abilities. Treatment for infectious diseases is based on the cooperation of multiple medical disciplines, including doctors and nurses involved in clinical work, technical staff, and specialists from the department of infectious diseases at a hospital⁹. Under the conventional medical model, there is usually only a community doctor position, and interdisciplinary consultation relationships have not yet been formed. There is no unified curriculum across training stages, nor is there systematic instruction in emergency infectious disease management. During an actual epidemic, trainees often exhibit a low degree of adaptability and a lack of awareness regarding infection prevention^10,11.

In response to deficiencies in traditional clinical teaching, multi-scenario simulation has emerged as a high-fidelity, low-risk educational approach and is now an essential direction for medical education reform. This approach constructs a tiered curriculum system for the key links of initial outpatient screening, ICU isolation, and emergency management of occupational exposure, thereby enhancing medical students' comprehensive clinical problem-solving abilities¹¹. In addition to assessing memory of the basic theories, several other aspects to be considered in this evaluation criterion include enhanced clinical critical thinking skills, adherence to standardized infection prevention and control requirements, and mental adjustment capacity under high-intensity working conditions. Indeed, prior evidence confirms that high-fidelity simulation fundamentally elevates higher-order practical competencies, notably enhancing clinical judgment and complex decision-making far beyond mere knowledge recall^12,13. Research shows that, through multi-scenario coupled teaching design, students' decision-making time in the face of atypical cases has been effectively shortened, and their operating accuracy in a complex environment has also been significantly improved. Through such repeated, immersive training in the simulation environment, from an objective standpoint, it transforms the abstract sense of protection into muscle memory and forms a protective habit that has become instinctive, thereby compensating for shortfalls in practical experience caused by insufficient real clinical work. Furthermore, immersive simulation has been consistently shown to foster greater learner satisfaction, self-efficacy, and active engagement, effectively mitigating the stress associated with high-risk clinical scenarios^14,15,16.

Despite the growing adoption of simulation in medical education, there remains a critical evidence gap regarding its quantitative impact on safety-critical procedural compliance and cognitive load in complex infectious disease management. The novelty of this study lies in its multi-scenario, workflow-coupled simulation design that integrates real-time biosafety feedback to specifically target cross-infection risks. Accordingly, the primary hypothesis is that multi-scenario simulation training will significantly outperform traditional didactic teaching in enhancing students' practical competencies, specifically yielding higher OSCE scores and greater compliance with critical safety standards. Furthermore, this study aims to build a dynamic closed-loop evaluation system. A combination analysis of objective structured clinical examination data and behavioral logs from simulated operations can accurately identify cognitive load bottlenecks and operational defects arising from changes across various clinical situations among medical students. This study is not only intended to show the advantages of multi-scenario simulation teaching but also to put forward differentiated teaching pathway optimization strategies for infectious diseases with different transmission routes based on quantitative empirical data analysis¹⁷

Protocol

All procedures involving human participants were conducted in accordance with the ethical standards of the Institutional Review Board of the Affiliated Hospital of North Sichuan Medical College and the principles of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of the Affiliated Hospital of North Sichuan Medical College (Approval No. 2025ER538-1). Written informed consent was obtained from all participants prior to enrollment.

1. Participant recruitment and screening

1. Obtain the official clinical rotation rosters from the Affiliated Hospital of North Sichuan Medical College during the spring academic semester. Screen and recruit eligible participants during the two weeks preceding the infectious disease training module.
2. Restrict eligibility to fourth- or fifth-year undergraduate clinical medicine and nursing students and first-year clinical residents. Review the academic transcripts and clinical rotation schedules of all candidates using two independent teaching coordinators to verify compliance with the inclusion criteria.
3. Conduct qualification reviews for all potential candidates in accordance with standardized inclusion and exclusion criteria.
4. Verify that participants have completed or are currently conducting theoretical courses related to infectious diseases.
5. Administer a foundational pre-simulation clearance checklist to objectively verify that all candidates possess the physical stamina and cognitive availability required to endure high-intensity scenario training. Utilize this standardized checklist to confirm their capacity to complete the rigorous Objective Structured Clinical Examination without any underlying medical restrictions or scheduling conflicts.
6. Obtain an agreement from the participants regarding the recording of the teaching process and data collection, including questionnaire completion and standard scoring.
7. Evaluate baseline communication and teamwork capacities using the standardized Mini-Clinical Evaluation Exercise communication sub-scale during their initial clinical rotation. Exclude candidates who score below the established threshold of 4 out of 9 to ensure adequate interactive skills for completing simulated role tasks.
8. Exclude participants who are not in the specified training phase or lack basic knowledge in infectious disease theory.
9. Exclude individuals who have received prior systematic multi-scenario simulation training that highly overlaps with the study intervention to avoid intervention contamination.
10. Exclude those with objective limitations, such as rotation/duty schedules preventing the completion of required class hours, or a high risk of serious absenteeism.
11. Collect baseline demographic and learning background data, including age, gender, training stage, course experience of infectious diseases, and simulation training experience.
12. Utilize this baseline data for subsequent descriptive analysis and necessary statistical adjustments¹⁸.

2. Study design and allocation

1. Adopt a prospective, randomized, parallel-controlled educational intervention design to evaluate the application effects of multi-scenario simulation teaching.
2. Follow the predefined study workflow for participant enrollment, group allocation, intervention delivery, and outcome assessment. See Figure 1 for an overview of the study pathway.
3. Perform individual-level randomization to allocate the 121 screened subjects into the intervention group comprising 60 students and the control group comprising 61 students. Generate the random allocation sequence using a computer-based random number generator with a 1-to-1 ratio. Conceal this sequence in opaque, sequentially numbered, and sealed envelopes administered by a designated research assistant who is entirely independent of the teaching and assessment processes.
4. Carry out the research for both groups within a standardized teaching environment.
5. Complete the corresponding teaching arrangements for both groups within the exact same instructional cycle.
6. Maintain the comparability of teaching content and class-hour arrangements between the intervention and control conditions to minimize interference caused by teaching dosage differences¹⁹.

3. Implementation of the control condition (routine practice teaching)

1. Deliver routine infectious disease practice teaching to the control group to reflect the common teaching mode in the current education system.
2. Standardize the educational exposure by providing exactly 16 total contact hours distributed evenly over a four-week cycle for both conditions. Maintain a consistent faculty-to-student ratio of 1:10 across all sessions to equalize instructor interaction and prevent instructional dosage bias.
3. Conduct traditional case discussions and deliver clinical guideline explanations within a standard forty-seat lecture hall. Perform all theoretical instruction and situational demonstrations using a strict teacher-centered approach, allocating exactly four hours per weekly session to ensure temporal equivalence with the intervention group.
4. Provide routine demonstrations of specimen collection, basic hospital infection control procedures, and isolation techniques utilizing static, low-fidelity anatomical models. Limit learner engagement during these routine demonstrations strictly to passive observation and basic physical return demonstrations without any immersive contextual stressors.
5. Restrict the practical components of the control curriculum to paper-based theoretical case discussions and isolated single-skill practice on low-fidelity benchtop models. Verify that these learners do not participate in any integrated multi-scenario simulations or immersive clinical problem-solving environments.
6. Prevent cross-group contamination operationally by scheduling the intervention and control group training sessions on entirely different days and utilizing physically separate campus facilities.
7. Monitor compliance strictly by requiring all enrolled participants to sign a formal confidentiality agreement prohibiting the sharing of instructional materials or scenario details prior to the final assessment.

4. Implementation of the intervention (multi-scenario simulation teaching)

1. Base the overall simulation course design on actual clinical work processes.
2. Standardize high-frequency and high-risk practical tasks into specific training and quantity evaluation teaching units.
3. Organize the simulation curriculum sequentially around key infectious disease workflows, including triage and risk assessment, isolation decision-making, specimen collection, infection prevention and control, antimicrobial stewardship, and occupational exposure management.
4. Deliver the simulation modules according to the predefined curriculum framework and competency objectives. See Figure 2 for an overview of the curriculum structure.
5. Utilize a matrix-based construction method to align the scenario themes directly with the practical workflows of infectious diseases.
6. Assign a target competency and predefined observable behavioral criteria to each simulation scenario to guide instruction and assessment.
7. Implement the scenario-competency framework throughout the training program. See Figure 3 for the mapping between simulation scenarios and target competencies.
8. Implement the simulation scenarios according to the parameters specified in Table 1. Use high-fidelity patient manikins with programmable vital signs and standard clinical equipment to create a realistic clinical training environment.
9. Conduct the training in small cohorts of five learners to guarantee optimal hands-on engagement. Schedule four distinct simulation sessions per cohort, allocating precisely four hours per session to achieve a total of 16 contact hours per learner.
10. Ensure that all simulations are facilitated by senior attending physicians who hold formal simulation instructor certification and possess over five years of clinical experience in infectious disease management.
11. Conduct each simulation according to a standardized script containing patient information, risk factors, decision points, critical actions, and reporting requirements. Follow the predefined workflow from triage assessment through isolation, specimen collection, and handoff communication. See Figure 4 for an example of the scenario script.
12. Perform a continuous clinical scenario involving triage risk stratification, isolation decision-making, and specimen testing handover. Limit the scenario duration to 15–25 min without interruption to replicate time-sensitive clinical decision-making.
13. Apply an invisible fluorescent tracer lotion to selected surfaces or equipment within the simulation environment. Inspect participants and environmental contact points using a 395 nm ultraviolet light system immediately after high-risk procedures, such as specimen handling or personal protective equipment (PPE) doffing.
14. Use the inspection results to identify contamination pathways, missed hand hygiene steps, and high-risk contact areas. Provide immediate visual feedback to participants based on the observed contamination patterns (see Figure 5).
15. Assign a trained simulation instructor to monitor participant performance throughout each scenario using a predefined critical-error rubric. Identify safety-critical errors, including improper hand hygiene, incorrect PPE use, and unprotected exposure events.
16. Provide immediate corrective feedback for serious safety breaches using standardized instructor scripts. Limit interventions to errors that compromise participant safety or interfere with achieving the learning objectives.
17. Conduct a structured debriefing immediately after each simulation session. Use the Gather, Analyze, and Summarize (GAS) framework to facilitate reflection on clinical decision-making, teamwork, communication, and infection control practices.

5. Outcome measurement and data collection

1. Conduct a unified outcome assessment for all participants within 1 week after completion of the teaching intervention.
2. Blind all OSCE assessors to participant group allocation throughout the assessment process.
3. Schedule participants from the intervention and control groups in a mixed order during the assessment sessions to minimize allocation disclosure.
4. Assess participant performance using a six-station Objective Structured Clinical Examination (OSCE).
5. Allocate 10 min for completion of each OSCE station.
6. Assign two independent examiners to each station and evaluate performance using standardized 100-point scoring sheets.
7. Calculate the intraclass correlation coefficient after the examination to assess inter-rater reliability.
8. Evaluate compliance with predefined critical actions using standardized OSCE checklists.
9. Score triage risk stratification according to the accuracy of risk classification completed within 3 min.
10. Score isolation decision-making according to the timely initiation of the appropriate notification and reporting procedures.
11. Score specimen collection according to correct labeling, packaging, and double-bagging procedures.
12. Score personal protective equipment (PPE) removal according to adherence to the prescribed doffing sequence.
13. Score occupational exposure management according to the timeliness and completeness of decontamination procedures.
14. Measure compliance with safety-critical behaviors throughout the OSCE assessment.
15. Evaluate hand hygiene according to the timing and completeness of hand hygiene actions.
16. Evaluate PPE use according to the correct sequence of donning and doffing procedures.
17. Evaluate specimen management according to compliance with specimen packaging and transport requirements.
18. Evaluate exposure-response according to compliance with predefined response time limits and management procedures.
19. Administer scenario-based reasoning and decision-making evaluations as secondary outcome indicators to prevent relying solely on knowledge-memory-based questions.
20. Observe and score team communication and collaboration behaviors during the scenarios.
21. Evaluate observable communication items, focusing on the completeness of information conveyed, the structure of SBAR (Situation, Background, Assessment, Recommendation) expression, closed-loop communication, and cross-role task assignments.
22. Administer a comprehensive set of previously validated psychometric questionnaires to capture learner-reported outcomes, specifically targeting satisfaction, self-efficacy, active engagement, and perceived cognitive load.
23. Ensure these established instruments have undergone rigorous cultural adaptation and preliminary pilot testing within the local instructional context prior to formal deployment. Refer to Table 2 for reliability and validity evidence, acquisition methods, and evaluation criteria for all performance assessment tools.
24. Train all assessors during a standardized 4 h calibration session. Require each assessor to achieve a Cohen's kappa value >0.80 before formal scoring.
25. Utilize partial video recordings during the sample checks to verify examiner consistency and scoring accuracy.
26. Designate specific research personnel to completely anonymize all collected data by replacing personal identifiers with randomized alphanumeric codes. Store this de-identified data securely on an encrypted institutional cloud server with password-protected access restricted solely to the core research team, and maintain these records for a mandatory retention duration of five years to ensure traceability and compliance.

6. Statistical analysis preparation

1. Calculate the means and standard deviations (or medians and quartiles) for continuous variables, and present categorical variables as frequencies and percentages.
2. Test all continuous outcome variables for normality using the Shapiro-Wilk test prior to executing any covariance analysis or generalized linear modeling. Subsequently, assess the homogeneity of variances via Levene's test to guarantee that all foundational statistical assumptions are strictly satisfied.
3. Control for baseline levels and key background variables (e.g., age, gender, training stage, prior infectious disease experience) within the linear models to improve estimation robustness.
4. Analyze dichotomous outcomes, such as key step compliance, using generalized linear models for between-group comparisons.
5. Report the corresponding effect sizes, odds ratios, and confidence intervals for all primary outcomes.
6. Impute missing follow-up data using multiple imputation with fully conditional specification. Compare imputed and complete-case datasets in sensitivity analyses.
7. Set the significance level at 0.05 for all two-tailed statistical tests.

Results

Baseline characteristics
A total of 121 participants were included in the study, comprising 60 students in the multi-scenario simulation group and 61 in the control group. Baseline demographic characteristics and learning experiences were comparable between the two groups. Age distribution, gender composition, training type, training stage, and specialty distribution were similar across groups. The proportions of senior undergraduate students and junior residents were also comparable. No significant differences were observed in prior infectious disease rotations, previous simulation training, hospital infection control or personal protective equipment (PPE) training, or prior Objective Structured Clinical Examination (OSCE) experience.

Baseline competency and cognitive assessments were likewise comparable between groups. Scores on the infectious disease knowledge test, situational reasoning assessment, hospital-acquired infection knowledge test, baseline OSCE, and key procedural compliance measures showed no meaningful differences. Learner-reported outcomes, including self-efficacy, learning engagement, and cognitive load, were also similar between groups. These findings indicated that the two groups were well balanced at baseline, supporting attribution of subsequent differences to the educational intervention. Table 3 presents the baseline characteristics and assessment measures of the study participants.

Primary outcomes

The primary outcomes were post-intervention OSCE checklist scores and compliance with safety-critical actions. The multi-scenario simulation group demonstrated greater improvements in objective performance than the control group across multiple stages of infectious disease practice. Post-intervention OSCE checklist scores were significantly higher in the simulation group, and compliance with key procedural steps was also improved. These findings indicated superior performance in both procedural execution and adherence to safety-critical behaviors.

Figure 6 presents changes in OSCE performance and cross-scenario compliance from baseline to post-intervention. Across the assessed domains, participants in the simulation group consistently achieved higher total scores and greater compliance with key procedural actions than those in the control group.

The multi-scenario simulation group achieved higher performance than the control group in triage risk stratification, isolation decision-making and reporting, specimen collection and labeling, hand hygiene, PPE donning and doffing, and occupational exposure management. Post-intervention differences between groups were consistently observed across these safety-critical and procedural competencies, with effect estimates indicating moderate-to-large educational benefits.

The between-group comparisons and effect estimates for these objective outcomes are presented in Table 4 as unadjusted data. These findings provide an initial quantitative assessment of the primary outcomes and are further examined using covariate-adjusted models in Table 5.

Secondary outcomes

Secondary outcomes were assessed using learner-reported measures and team communication performance metrics. Compared with the control group, participants in the multi-scenario simulation group reported higher levels of self-efficacy and learning engagement and lower levels of perceived cognitive load following the intervention. Figure 7 summarizes the between-group differences and temporal trends in learner-reported outcomes, demonstrating favorable changes across most educational experience indicators in the simulation group.

The multi-scenario simulation group also demonstrated superior team communication and collaborative performance. Post-intervention scores for SBAR communication, closed-loop communication completion, and handover quality were higher in the simulation group than in the control group. Detailed between-group comparisons, effect estimates, and confidence intervals for all secondary outcomes are presented in Table 6. Improvements in both learner-reported outcomes and team communication measures were statistically significant.

Adjusted effects and robustness checks

To evaluate the robustness of the primary findings, multivariable analyses were performed for post-intervention competency outcomes while controlling for baseline performance and potential confounding factors. Post-intervention outcome measures were included as dependent variables, with the study group as the primary independent variable. Covariates included age, gender, specialty, training stage, prior infectious disease rotation experience, previous simulation training, and recent hospital infection control and personal protective equipment (PPE) training. Baseline values of the corresponding outcomes were included in all models to account for initial between-group differences.

The adjusted analyses indicated that participation in the multi-scenario simulation program remained significantly associated with higher OSCE scores and greater compliance with key procedural steps after controlling for potential confounders. Significant favorable effects were also observed for team communication, handover quality, and cognitive load. Adjusted effect estimates, confidence intervals, and significance levels are presented in Table 5.

Optimization signals from error patterns

To identify areas for curriculum refinement, high-frequency errors recorded during the OSCE and simulation sessions were analyzed following the intervention. The most common errors occurred in safety-critical procedures and cross-phase transitions, including PPE donning and doffing, timing of hand hygiene, specimen labeling and packaging, isolation level selection and reporting, and structured handover communication. These errors occurred most frequently during time-constrained tasks and workflow transitions.

Analysis of error incidence and contributing factors revealed recurring challenges in consistently applying procedural rules across continuous task chains, despite satisfactory performance on individual tasks. Table 7 summarizes the most common errors, their potential root causes, and corresponding areas for curriculum improvement.

Figure 1: Study flow diagram: Participant enrollment, group allocation, intervention delivery, and outcome assessment. Please click here to view a larger version of this figure.

Figure 2: Overview of the multi-scenario simulation curriculum for infectious disease practical training. Please click here to view a larger version of this figure.

Figure 3: Scenario matrix and competency mapping across infectious disease practical workflows. (A) Illustrates the respiratory transmission scenario, emphasizing triage and risk stratification. (B) Outlines the isolation and reporting decision-making scenario. (C) Depicts the workflow for specimen collection and laboratory communication. (D) Illustrates the infection prevention and contamination awareness scenario. (E) Presents the gastrointestinal outbreak control scenario, emphasizing contact precautions. (F) Depicts the antimicrobial stewardship decision-making scenario. (G) Illustrates the workflow for managing blood-borne exposure and needlestick injuries. Please click here to view a larger version of this figure.

Figure 4: Example standardized scenario script for respiratory viral infection triage, isolation decision, and specimen collection. (A) Details the patient snapshot alongside epidemiologic risk factors. (B) Illustrates the critical decision point for isolation level selection. (C) Outlines the specific safety-critical actions and required personal protective equipment. Please click here to view a larger version of this figure.

Figure 5: Representative simulation interfaces and safety-critical feedback mechanisms across scenarios. (A) Displays the safe environment exploration combined with the standard infection prevention control workflow rehearsal. (B) Demonstrates the real-time contamination detector mechanism, providing immediate visual feedback regarding specific infection control lapses. Please click here to view a larger version of this figure.

Figure 6: Instructor and assessor-rated performance comparing the multi-scenario simulation group with conventional teaching. (A) Displays the objective structured clinical examination checklist total scores across baseline and post-intervention time points. (B) Summarizes the key step compliance percentages across various scenarios at the post-intervention assessment. Data are presented as mean ± 95% confidence interval. Between-group comparisons were adjusted for baseline values. *p < 0.05; **p < 0.01. Abbreviations: OSCE = Objective Structured Clinical Examination; PPE = personal protective equipment; IPC = infection prevention and control; AMS = antimicrobial stewardship. Please click here to view a larger version of this figure.

Figure 7: Learner-reported outcomes evaluated after the multi-scenario simulation intervention. (A) Presents the satisfaction scores comparing baseline to post-intervention levels. (B) Highlights the changes in self-efficacy for infectious disease practice. (C) Details the shifts in learning engagement scores. (D) Illustrates the perceived cognitive load, demonstrating lower scores for the simulation group post-intervention. Effect sizes are reported as Hedges' g with 95% confidence intervals. Adjusted p-values are shown within each panel. Please click here to view a larger version of this figure.

Scenario ID	Scenario (standardized)	Primary workflow coverage	Target competencies (examples)	Critical actions
A	Respiratory viral triage & risk stratification	Triage > early IPC initiation	Risk stratification; early IPC; communication	Mask patient; hand hygiene; correct triage priority; initiate appropriate precautions
B	Isolation level selection & reporting	Isolation decision > reporting/documentation	IPC decision-making; reporting compliance	Correct isolation category; notify IPC; complete screening/report form
C	Specimen collection & lab handoff	PPE > swab > labeling/packaging > lab request	Technical skill; biosafety; lab communication	Correct PPE; correct swab technique; 2-ID check; label before bagging; correct lab request
D	Hand hygiene & PPE doffing (contamination control)	Hand hygiene > don/doff > contamination avoidance	IPC compliance; error recognition	Correct doffing order; hand hygiene moments; avoid high-touch contamination; terminal hand hygiene
E	GI outbreak control (contact precautions)	Screening > contact precautions > environmental control	Outbreak awareness; environmental control	Implement contact precautions; trigger cluster alert; cleaning/disinfection plan
F	Antimicrobial stewardship (culture-based decisions)	Culture review > optimize/de-escalate therapy	Clinical reasoning; evidence-based AMS	Correct culture interpretation; appropriate antibiotic choice; de-escalation; document indication/duration
G	Blood-borne exposure/needlestick response	First aid > reporting > risk assessment/PEP > follow-up	Occupational safety; reporting; follow-up planning	Immediate wash; report promptly; complete exposure form; initiate PEP pathway if indicated

Table 1: Scenario coverage and competency mapping of the multi-scenario simulation curriculum. Please click here to download this Table.

Assessment tool	Construct	Evidence type	How evidence is obtained in this study	Statistic	A priori acceptability criteria
	(s) measured			(s) to report
OSCE station checklists (6–7 stations aligned to scenarios)	Procedural skills, IPC compliance, workflow execution	Content validity	Checklist blueprint derived from scenario scripts and institutional IPC standards; reviewed by an expert panel (infectious diseases, IPC, nursing, lab medicine)	Content validity documentation (expert panel composition; revision rounds)	Expert agreement on item relevance and coverage; critical actions explicitly defined
OSCE station checklists (same as above)	Same as above	Inter-rater reliability	Rater training using standardized rubric and anchor examples; dual scoring on a subset of recorded performances	ICC (two-way random, absolute agreement) or weighted kappa (item-level)	ICC ≥ 0.75 (good), ≥ 0.90 (excellent); item-level κ ≥ 0.60
OSCE total score (summed across stations)	Overall objective performance	Internal consistency (if treated as a scale)	Calculate reliability across stations/score components after data collection	Cronbach’s α (or McDonald’s ω)	α/ω ≥ 0.70 acceptable; ≥ 0.80 preferred
Critical action compliance index (binary completion of safety-critical steps)	Safety-critical behavior execution (e.g., hand hygiene moments, doffing sequence, labeling)	Criterion-related validity (process validity)	Critical actions pre-specified in scripts; compliance derived from checklist + optional video verification	Proportion compliance; agreement between live vs video review (κ)	High agreement between live and video (κ ≥ 0.60); clear audit trail for each critical action
Scenario-based clinical reasoning test (Key-feature format)	Clinical reasoning, isolation decision, reporting judgment, diagnostic pathway selection	Content validity	Item blueprint mapped to workflow steps and learning objectives; reviewed by experts; revised after pilot feedback	Documentation of blueprint coverage; item review outcomes	Coverage of all targeted workflow decisions; removal/revision of ambiguous items
Scenario-based clinical reasoning test	Same as above	Scoring reliability	Two independent scorers on a subset (if open-ended components exist); standardized answer key	ICC or κ (depending on scoring); item difficulty/discrimination (optional)	ICC ≥ 0.75; acceptable item performance (no extreme floor/ceiling unless justified)
Teamwork/communication rating (SBAR/closed-loop checklist)	Communication quality, teamwork behaviors	Content validity	Adapted from established teamwork frameworks; expert review for contextual fit to infectious disease workflows	Expert review notes; item mapping to behaviors	Items observable and behaviorally anchored; minimal overlap/redundancy
Teamwork/communication rating	Same as above	Inter-rater reliability	Dual rating on a subset of team simulations; rater calibration session	ICC or κ	ICC ≥ 0.75; κ ≥ 0.60
Learner-reported questionnaire set (satisfaction, self-efficacy, engagement, cognitive load)	Learning experience and perceived competence	Internal consistency & structural validity	Use established scales when available; if adapted, conduct pilot wording check; compute internal consistency; optional CFA/EFA depending on sample size	Cronbach’s α/ω; factor structure indices (optional)	α/ω ≥ 0.70; factor structure interpretable and consistent with theoretical constructs
All instruments (global)	Feasibility and acceptability	Feasibility validity	Track completion rate, missingness patterns, time burden, and floor/ceiling effects	Completion rate; missingness (%); time-to-complete; floor/ceiling (%)	Completion ≥ 90%; missingness low and non-differential; acceptable burden and interpretable distributions

Table 2: Reliability and validity evidence for performance assessment tools. Please click here to download this Table.

Characteristic	Multi-Scenario Simulation	Conventional Teaching
	(n = 60)	(n = 61)
Age, years, mean (SD)	22.8 (1.4)	22.6 (1.6)
Female, n (%)	36 (60.0)	38 (62.3)
Male, n (%)	24 (40.0)	23 (37.7)
Program, n (%)
Clinical Medicine	34 (56.7)	33 (54.1)
Nursing	18 (30.0)	20 (32.8)
Public Health/Other	8 (13.3)	8 (13.1)
Training stage, n (%)
Senior undergraduate	45 (75.0)	46 (75.4)
Junior resident/trainee	15 (25.0)	15 (24.6)
Prior infectious diseases rotation (≥1 week), n (%)	27 (45.0)	29 (47.5)
Rotation exposure, weeks, median (IQR)	1.0 (0.0–2.0)	1.0 (0.0–2.0)
Prior simulation-based training (any), n (%)	21 (35.0)	23 (37.7)
Prior IPC/PPE formal training (past 12 months), n (%)	33 (55.0)	31 (50.8)
Prior OSCE experience (any), n (%)	41 (68.3)	43 (70.5)
Baseline infectious diseases knowledge test (0–100), mean (SD)	71.6 (8.7)	70.9 (9.1)
Baseline scenario-based reasoning score (0–20), mean (SD)	12.8 (2.7)	12.6 (2.9)
Baseline IPC knowledge quiz (0–10), mean (SD)	6.9 (1.4)	6.7 (1.5)
Baseline OSCE total score (0–100), mean (SD)	68.4 (7.9)	67.8 (8.3)
Baseline critical-action compliance (%, 0–100), mean (SD)	62.7 (10.8)	61.9 (11.2)
Self-efficacy for infectious-disease practice (1–7), mean (SD)	4.3 (0.8)	4.2 (0.9)
Learning engagement (1–7), mean (SD)	4.8 (0.9)	4.7 (0.9)
Perceived cognitive load at baseline (1–10), mean (SD)	6.2 (1.5)	6.1 (1.6)
Average academic performance/GPA (0–4.0), mean (SD)	3.18 (0.34)	3.14 (0.36)
Weekly self-study time for infectious diseases, hours, mean (SD)	2.6 (1.3)	2.7 (1.4)
Intention to work in infection-related units (1–5), mean (SD)	3.1 (1.0)	3.0 (1.1)

Table 3: Baseline characteristics of participants by study group. Please click here to download this Table.

Outcome	Multi-Scenario Simulation	Conventional Teaching	Between-group difference	Effect size	p value
(T1, post-intervention)	(n = 60)	(n = 61)	(Simulation − Control)
OSCE checklist total score (0–100), mean (SD)	82.7 (6.8)	74.6 (7.4)	8.1 (95% CI 5.4 to 10.9)	Cohen’s d = 1.14	<0.001
Key-step compliance index (%), mean (SD)	88.9 (7.6)	78.4 (9.3)	10.5 (95% CI 7.3 to 13.6)	Cohen’s d = 1.23	<0.001
OSCE critical actions completed (count), mean (SD)*	17.6 (2.1)	14.9 (2.6)	2.7 (95% CI 1.9 to 3.5)	Cohen’s d = 1.13	<0.001
Triage & risk stratification score (0–20), mean (SD)	16.8 (2.0)	14.7 (2.3)	2.1 (95% CI 1.3 to 2.9)	Cohen’s d = 0.97	<0.001
Isolation decision & reporting score (0–15), mean (SD)	12.7 (1.7)	10.8 (2.0)	1.9 (95% CI 1.2 to 2.6)	Cohen’s d = 1.02	<0.001
Specimen collection & labeling score (0–15), mean (SD)	13.4 (1.5)	11.8 (1.9)	1.6 (95% CI 1.0 to 2.2)	Cohen’s d = 0.94	<0.001
IPC behavior score (hand hygiene + PPE) (0–20), mean (SD)	17.1 (1.9)	15.0 (2.4)	2.1 (95% CI 1.3 to 2.9)	Cohen’s d = 0.97	<0.001
Occupational exposure response score (0–10), mean (SD)	8.7 (1.2)	7.8 (1.4)	0.9 (95% CI 0.4 to 1.4)	Cohen’s d = 0.69	0.001

Table 4: Primary outcomes: between-group comparisons of objective performance after intervention. Please click here to download this Table.

Outcome (T1)	Model type	Adjusted effect of simulation teaching	95% CI	p value
		(vs control)
OSCE checklist total score (0–100)	Linear regression (ANCOVA)	7.48	4.92 to 10.05	<0.001
Key-step compliance index (%), (0–100)	Linear regression (ANCOVA)	9.61	6.31 to 12.90	<0.001
OSCE critical actions completed (count)	Poisson regression (log link)	IRR 1.17	1.10 to 1.24	<0.001
Teamwork/communication rating (0–20)	Linear regression (ANCOVA)	1.56	0.82 to 2.30	<0.001
Handoff quality score (0–15)	Linear regression (ANCOVA)	1.21	0.67 to 1.76	<0.001
Perceived cognitive load (1–10; lower better)	Linear regression (ANCOVA)	−0.57	−0.98 to −0.16	0.007
High competency (OSCE ≥80), n (%)	Logistic regression	OR 3.12	1.44 to 6.91	0.004

Table 5: Multivariable models of post-intervention competency: Adjusted effects of multi-scenario simulation teaching. Please click here to download this Table.

Outcome	Multi-Scenario Simulation	Conventional Teaching	Between-group difference	Effect size	p value
(post-intervention, T1)	(n = 60)	(n = 61)	(Simulation − Control)
Satisfaction (1–7), mean (SD)	6.12 (0.63)	5.24 (0.71)	0.88 (95% CI 0.64 to 1.12)	Cohen’s d = 1.31	<0.001
Self-efficacy for infectious-disease practice (1–7), mean (SD)	5.86 (0.66)	5.02 (0.78)	0.84 (95% CI 0.57 to 1.11)	Cohen’s d = 1.17	<0.001
Learning engagement (1–7), mean (SD)	5.98 (0.69)	5.31 (0.76)	0.67 (95% CI 0.40 to 0.94)	Cohen’s d = 0.92	<0.001
Perceived cognitive load (1–10)* , mean (SD)	5.41 (1.17)	6.03 (1.23)	−0.62 (95% CI −1.05 to −0.19)	Cohen’s d = −0.51	0.005
Teamwork/communication rating (SBAR/closed-loop checklist, 0–20), mean (SD)	16.7 (2.1)	14.9 (2.4)	1.8 (95% CI 1.0 to 2.6)	Cohen’s d = 0.80	<0.001
Closed-loop communication completion (%), mean (SD)	84.6 (9.7)	76.8 (11.2)	7.8 (95% CI 4.1 to 11.6)	Cohen’s d = 0.75	<0.001
Handoff quality score (structured checklist, 0–15), mean (SD)	12.9 (1.6)	11.4 (1.8)	1.5 (95% CI 0.9 to 2.1)	Cohen’s d = 0.89	<0.001

Table 6: Secondary outcomes: Learner-reported measures and teamwork/communication ratings. Please click here to download this Table.

Priority rank	Workflow domain / scenario linkage	High-frequency error signal	Error rate	Error rate	Likely root cause	Targeted refinement strategy
		(operational definition)	(Simulation, n = 60)	(Control, n = 61)	(s)	(next iteration)
1	PPE doffing & hand hygiene (Scenario D)	Missed critical hand hygiene moment after glove removal or before exit	18.30%	34.60%	Cognitive overload during sequencing; weak “moment recognition”	Add 3-min micro-drill with timed prompts; introduce doffing checklist card; require verbalization of “moment”
2	Specimen labeling & packaging (Scenario C)	Label applied after bagging or incomplete 2-identifier check	14.70%	27.90%	Habit-based shortcuts; unclear ownership of labeling step	Standardize “label-first” rule; add labeling failure demonstration; add peer cross-check step
3	Isolation decision (Scenario B)	Under-triage of isolation level for high-risk exposure history	16.10%	29.40%	Incomplete epidemiologic history; uncertainty in policy thresholds	Add a risk-card quick algorithm; include 2 additional near-miss cases in practice set
4	Reporting/documentation (Scenario B, G)	Delay or omission of IPC/exposure reporting form submission	12.80%	24.10%	Workflow unfamiliarity; unclear reporting pathway	Teach “reporting within 30 min” rule; add form-filling rehearsal with examples
5	Triage risk stratification (Scenario A)	Incorrect triage priority assignment despite red-flag vitals	10.90%	19.70%	Anchoring on mild symptoms; poor red-flag integration	Add red-flag trigger drill; require structured triage reasoning statement
6	Environmental IPC (Scenario E)	Neglect of high-touch surface disinfection or improper disinfectant contact time	13.60%	22.80%	Knowledge gap on contact time; task switching in team setting	Add contact-time cue timer; include surface checklist for shared-room outbreaks
7	AMS decision (Scenario F)	Failure to de-escalate after susceptibility results available	19.40%	26.60%	Risk aversion; uncertainty about de-escalation criteria	Add de-escalation decision tree; case-based “culture-to-order” practice
8	Occupational exposure response (Scenario G)	Incomplete first-aid sequence or missed follow-up scheduling	9.60%	17.30%	Low familiarity with follow-up pathway; competing priorities	Add 1-page exposure pathway card; include follow-up booking as a checklist item
9	Team communication (cross-scenario)	Incomplete SBAR handoff (missing risk status or isolation plan)	21.20%	33.10%	Non-structured verbal handoff; limited closed-loop habits	Add SBAR template card; practice closed-loop read-back in pairs
10	Cross-cutting safety behavior	Skipping “final check” before leaving station (PPE, waste disposal, documentation)	11.50%	20.20%	End-of-task haste; weak “termination routine”	Introduce 10-second “stop-check-go” routine; add end-of-station checklist

Table 7: Data-driven optimization priorities: high-frequency errors, root causes, and targeted refinement strategies. Please click here to download this Table.

Discussion

Through a prospective randomized controlled trial, this study demonstrated that multi-scenario simulation teaching is significantly more effective than traditional teaching for the practice education of infectious diseases^20,21. The intervention group achieved considerable gains in OSCE total scores, compliance with essential steps, triage risk stratification, isolation decision-making, specimen collection, hand hygiene, and protective equipment donning and doffing occupational exposure management, with moderate to large effect sizes²². The intervention also improved team communication, structured information delivery, and closed-loop collaboration. Subjective indicators showed that learners were highly satisfied with the learning experience, possessed a strong sense of self-efficacy and participation, and experienced lower cognitive load. These results show that multi-scenario simulation teaching effectively addresses the deficiencies of traditional teaching in terms of process integrity, collaborative depth, and standardization of high-risk behaviors, providing a high-fidelity, risk-free training path^23,24.

The efficacy of this model relies fundamentally on translating cognitive learning theory into structured clinical workflows. By utilizing a comprehensive scenario matrix and standardized decision scripts (Figure 3 and Figure 4), the curriculum seamlessly integrates isolated clinical tasks into a continuous management loop. Furthermore, the incorporation of visual contamination feedback (Figure 5) forcefully addresses the theoretical-practical gap by making invisible infection risks distinctly tangible, thereby accelerating the internalization of biosafety protocols and mitigating occupational exposure hazards²⁵. Compared to traditional fragmented teaching, the multi-scenario design is more likely to alleviate information overload, facilitate knowledge transfer, and thereby enhance confidence. As is consistent with the domestic and international literature, the matrix-based coupling design of infectious disease training in this study not only covers the differentiation of transmission routes but also addresses deficiencies in team collaboration and reporting compliance training, making it especially applicable to post-pandemic teaching environments with reduced real-world exposure opportunities²⁶.

The educational value of this study lies in offering a practical reference framework for reforming clinical instruction in infectious diseases. While the findings of this study suggest that this structured model may mitigate the inherent limitations of case seasonality and suddenness by providing consistent training exposures, these educational benefits must be interpreted cautiously. Given that the current empirical evidence is derived from a single-center cohort with short-term assessments, the potential of this curriculum to durably eliminate infection control loopholes in actual clinical practice remains a hypothesis requiring extensive longitudinal validation. In terms of cultivating public health talent, this model is suitable for institutions that lack the resources to build a reserve force capable of responding effectively to new and sudden infectious diseases. Despite the highly encouraging outcomes observed, several distinct limitations warrant careful consideration. The single-center nature of this trial inherently constrains the immediate generalizability of the findings across diverse institutional contexts. Most critically, the assessment framework exclusively captured short-term performance gains immediately following the educational intervention. Because complex clinical skills and strict adherence to infection control protocols are notoriously prone to temporal decay, the current lack of longitudinal follow-up severely limits the ability to confirm the enduring retention of these essential competencies. Finally, the substantial logistical costs of high-fidelity simulation and its inherent inability to perfectly replicate the chaotic psychological stress of actual clinical emergencies must be pragmatically acknowledged.

Based on the results, this study suggests a weighted adjustment of scenario weights, the introduction of virtual reality-enhanced feedback, the construction of a dynamic error analysis closed-loop system, and multi-center research to evaluate cost-effectiveness and long-term effects. In summary, multi-scenario simulation teaching has demonstrated great effectiveness in improving practice competence in infectious diseases and is expected to become the main teaching model after the pandemic, contributing to the training of high-quality public health talent.

This prospective randomized controlled trial demonstrated that multi-scenario simulation teaching is highly effective in infectious disease practice education. The application of this model effectively improved medical students' objective practical performance, the degree of compliance with critical steps, clinical decision-making ability, teamwork ability, and learning experience, with a moderate to high effect size. Especially in high-risk infection control behavior and process integration, its advantages were particularly obvious, effectively solving the problems that traditional teaching cannot provide enough realistic practice opportunities, has deficiencies in collaborative training, and has a limited ability to internalize safety awareness. Consequently, while multi-scenario simulation teaching demonstrates notable short-term efficacy in overcoming biosafety constraints and standardizing essential practice behaviors, its role as a primary educational driver in the post-pandemic era should currently be viewed as a promising yet preliminary strategy. Rather than immediate nationwide adoption, widespread implementation must be firmly predicated on future multicenter trials that rigorously scrutinize its long-term skill retention, cost-effectiveness, and true translational impact on clinical patient outcomes. This will advance infectious disease education towards standardization and systematization.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
High-fidelity Simulation System	Simulation Center, Affiliated Hospital of North Sichuan Medical College	Model-specific	Multi-scenario infectious disease simulation training
OSCE Checklist & Scoring Forms	Research Team	Standardized protocol	Objective assessment of clinical skills and critical actions
Questionnaire Survey Forms	Research Team	Validated scales	Evaluation of learner satisfaction, self-efficacy and cognitive load
Statistical Software (SPSS)	IBM Corp.	Version 26.0	Statistical analysis of outcome data