The impact of simulation with debriefing on developing clinical competence and performance in paramedicine students

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Student paramedics must meet high standards set by oversight bodies to protect both the profession and the public. Growing evidence indicates that simulation facilitates attainment and reinforcement of clinical competencies. However, limited evidence explores the impact of simulation and debriefing on developing clinical competence in undergraduate paramedicine students. Aim This study aimed to evaluate the association between weekly practice session performance and summative practical exam outcomes, assessing how simulation-based practice supports the development of clinical competence among paramedicine students. Methods A retrospective analysis of performance-based assessment data (2019–2024) in a university paramedicine program evaluated the role of high-frequency simulation. Second-year students participated in weekly three hour simulated practice sessions and a summative practical exam, with performance assessed using the Clinical Competence Assessment Tool (CCAT). Practice session data were grouped into tertiles to assess trends. Pearson’s correlation measured the association between practice frequency and exam outcomes, paired samples t-tests evaluated performance progression, and hierarchical regression identified significant predictors of exam outcomes. Results Data from 305 students and 1,251 practice scenarios revealed a significant positive correlation between practice frequency and exam performance (rs = 0.257, p < 0.001). Clinical competence scores improved progressively across the trimester, with the strongest correlation observed in the final tertile (rs = 0.229, p < 0.001). Earlier tertiles showed weaker but statistically significant correlations (p < 0.05). A performance dip in 2020 due to reduced simulation exposure during COVID-19 further highlighted the critical role of consistent, high-frequency practice in fostering competence and exam success. Conclusion This study demonstrates the value of structured, high-frequency simulation and debriefing in enhancing clinical competence and exam performance. The findings underscore the importance of consistent practice, particularly in the final trimester weeks, supporting its integration into paramedicine curricula to ensure readiness for real-world practice and sustained competence. Figures Figure 1 Introduction Paramedics manage a broad spectrum of emergency and non-emergency medical and trauma cases ( 1 ). The expected level of clinical proficiency for paramedics is continually expanding, and like other healthcare professions, deficiencies in competence can have a detrimental impact on patient safety and outcomes ( 2 ). Before practicing independently, paramedicine students must demonstrate competence to oversight bodies responsible for safeguarding the profession and public, including educational institutions, prospective employers, and licensing bodies ( 3 ). Performance-based examinations are essential in ensuring clinical competence. Growing evidence indicates that educational technologies, particularly simulation-based education (SBE), play a critical role in facilitating the attainment and reinforcement of clinical competencies ( 4 ). Debriefing is another effective teaching strategy shown to support the development and maintenance of clinical competence and performance ( 5 ). The acquisition of clinical competence is a fundamental goal in medical education and requires specific teaching, learning and assessment ( 6 ). Whilst the adage “see one, do one, teach one” has been the traditional paradigm widely adopted to develop clinical competence ( 6 ), this approach has been brought under scrutiny as far back as the late 1990s out of concerns for patient safety ( 2 ). This approach involves students having direct patient contact, practicing procedural skills under the supervision of a qualified clinician. It was reported that nearly 100,000 patients die annually from preventable mistakes in the hospital in the United States, with a further 1 million injured ( 7 ). Medical education has lagged behind other similar high risk professions, such as aviation, which also requires sophisticated technical and behavioural skills ( 8 ). The proposed solution to replace this traditional method of developing clinical competence is SBE ( 9 ). Simulation is defined as “a tool, device and/or environment that mimics an aspect of clinical care” ( 10 ). While not new, SBE has become a well-established pedagogy in healthcare education, predominantly rooted in the aviation industry ( 11 ). Simulation allows students to develop clinical competence safely, benefiting patient care and safety ( 12 – 17 ). A key advantage of SBE is the controlled environment that facilitates deliberate practice ( 18 ). Students can deconstruct skills, engage in tasks tailored to their learning stage, and manage cognitive load effectively ( 19 ). Within a structured framework, they can rehearse skills repetitively, achieving fluency and instinctive performance ( 18 ). Simulation enables students to build on existing knowledge, progress through learning stages, and deepen clinical competence. Unlike clinical settings, which are often opportunistic and unstructured, simulation better supports deliberate practice, promoting meaningful learning. While real patient interaction is essential at advanced stages, simulation has been shown to be an effective tool in improving the safe delivery of medical care ( 2 ). The structure of simulation improves the quality of feedback to students, a critical component of clinical skills development and maintenance. Simulation further develops reflective practice, helping students self-monitor their performance ( 20 ). Post-simulation debriefing is recognised as a cornerstone component of developing clinical competence in simulated settings ( 21 , 22 ). This structured discussion allows students to gain insight into their actions and decisions, enhancing both learning outcomes and future performance ( 21 ). Debriefing provides faculty and students a chance to reflect, share mental models, and strengthen the reasoning underlying clinical judgment ( 23 ). Kolb’s experiential learning cycle supports this process, enabling learners to engage in concrete experiences through simulation, followed by observation and reflection through debriefing ( 24 , 25 ). Students then form abstract concepts and generalisations that can be tested in future situations, leading to subsequent new concrete experiences and learning ( 22 , 24 ). These structured and repeated experiences in simulation have shown significant benefits on learning outcomes, improving students’ knowledge, skills, and behaviour ( 23 ). Applying Kirkpatrick’s four-level evaluation model, evidence demonstrates simulation’s effectiveness at the first three levels: participant satisfaction, knowledge acquisition, and performance improvement ( 13 ). Although the model’s fourth level, focusing on patient outcomes, is still emerging ( 26 – 29 ), studies suggest that frequent simulations are associated with improved clinical competence ( 30 , 31 ). Despite this growing body of evidence in other healthcare disciplines, there is a notable gap in paramedicine research. While SBE has been extensively studied in medical education ( 11 , 13 ) and other allied health fields ( 12 , 18 ), its usage patterns and specific impact on paramedic practice remain poorly understood ( 32 ). Furthermore, most paramedicine studies focus on exit-level outcomes or program-level evaluation ( 33 ), with limited attention to how simulation can systematically enhance clinical competence and performance during the learning process. In the evolving landscape of higher education, particularly within healthcare professions such as paramedicine, there is a discernible shift towards increased online learning and condensed practical sessions. While these models offer flexibility and accessibility, concerns have been raised regarding their effectiveness in developing essential clinical competencies. Research indicates that while e-learning enhances accessibility and flexibility, its success hinges on factors like engagement and the availability of appropriate technology ( 34 ). Moreover, the COVID-19 pandemic has accelerated the adoption of digital platforms in health education, presenting both opportunities and challenges in ensuring comprehensive clinical education ( 35 ). Given these dynamics, integrating frequent, structured simulation into paramedicine curricula becomes fundamental to ensure that students acquire and refine the clinical competencies necessary for effective practice. This study aims to address these gaps by evaluating the role of simulation in paramedicine education, particularly in fostering clinical competence and performance improvement throughout a trimester. Aim The aim of this study was to determine the association between weekly practice session performance and summative practical exam outcomes, evaluating whether improvements in simulated practice sessions indicate clinical competence development among paramedicine students. Objectives: The primary objective was to determine whether the frequency of scenarios performed during weekly practice sessions is associated with improved summative practical exam performance. The secondary objective was to assess whether improved performance in weekly practice sessions correlates with improved performance in the summative practical exam. Methods Design This was a retrospective analysis of clinical performance outcome data collected from 2019 to 2024 in the [Anonymous] University paramedicine program. Weekly performance assessments, rated by sessional academics, were compared to the end-of-trimester practical exam results. Setting and Procedure This study was conducted at [Anonymous] University School of Medicine and Dentistry (SoMAD) within the paramedicine program. The data analysed were collected during weekly practical sessions and summative practical exams in the second-year Cardiology course, where paramedicine students were rated by sessional academic staff on clinical competence and performance during simulations. This data, collected using the Clinical Competence Assessment Tool (CCAT) from 2019 to 2024, represents a novel approach to understanding the role of non-graded simulated practical sessions in developing clinical competence and influencing performance on exam outcomes. Simulation was a key component of this course, forming one aspect of a broader multimodal educational approach designed to support the development of clinical competence. The course integrated several complementary teaching, learning, and assessment strategies, including weekly ECG quizzes, a summative theory exam, and a skills sign-off assessment. Together, these strategies provided students with opportunities to consolidate theoretical knowledge and practical skills, ensuring a holistic approach to clinical competence development. The practical simulation sessions were structured to reinforce and integrate content covered during theory sessions, enabling students to apply their knowledge in a realistic, hands-on environment. Each cohort of students attended weekly three hour simulated practical sessions and a final simulated practical exam, facilitated by sessional academics who were experienced and qualified paramedics. Four simulation rooms operated each week, each facilitated by a different sessional academic, with all rooms following the same set of scenarios to ensure consistency. Students remained in the same room throughout the trimester but were assigned a different sessional academic each week, providing them with diverse feedback and perspectives. Each weekly session followed a structured lesson plan developed by faculty, introducing new complex content while also incorporating elements of scaffolding to build on prior knowledge and competencies as the trimester progressed. Students worked in pairs to assess, diagnose, and treat simulated patients within a 20-minute timeframe, approximating the time constraints expected in real-world paramedic practice ( 36 , 37 ). This approach supported the progressive development of cumulative competencies, with scenarios designed to reinforce and integrate previously learnt skills alongside newly introduced content. After each scenario, students participated in a structured feedback session using the CCAT. Facilitators and peers contributed feedback that helped students reflect on their performance, breaking down complex tasks into manageable components. This structured debriefing aligned with the program's novel approach of capturing weekly practical session performance to develop clinical competence and enhance exam performance. Students were required to attend a minimum of 80% (9 out of 12) of the weekly sessions. Within these sessions, students had the flexibility to perform as many or as few scenarios as they chose, allowing them to tailor their practice to their individual learning needs. Scenarios commenced in Week 3, after students had covered the fundamental knowledge and skills necessary for participation. Data collection continued until Week 12, allowing for a range of 0 to 10 scenarios performed per student, reflecting variations in individual engagement and practice needs. The course convenor facilitated a different room each week and served as the central point of contact for sessional academics, addressing any questions regarding content or scenarios. To ensure consistency in marking and feedback during the weekly practice sessions, the convenor monitored assessments and facilitated informal discussions with sessionals to address any discrepancies in scoring or feedback practices. This approach fostered alignment among sessional academics and ensured consistency across simulation rooms, contributing to a cohesive and well-supported learning environment. For summative practical exams, a formal moderation process was conducted to ensure consistency and fairness in the assessment of pass/fail outcomes. The moderation involved a review of assessment scores and sessional feedback, with discrepancies resolved through collaborative discussions among sessional academics and the convenor. This process aimed to fine-tune assessment marks and improved the consistency of scoring across all simulation rooms, ensuring robust and reliable exam outcomes. The final practical exam served as a comprehensive assessment of cumulative knowledge and competencies acquired throughout the trimester, integrating elements from all previously covered content areas. This format ensured that students demonstrated a holistic understanding and the ability to apply their knowledge and skills effectively in simulated scenarios. Simulations used high-fidelity manikins (Laerdal’s SimMan® ALS) for clinical realism, with facilitators controlling vital signs via wireless SimPad technology. Students performed hands-on assessments such as pulse palpation, blood pressure measurement, oxygen saturation, and auscultation of lung and heart sounds. When needed, facilitators provided additional information (e.g. patient appearance, capillary refill, and 12-lead ECGs) to enhance the clinical experience. Although simulations were conducted consistently across the study period, adjustments were made in 2020 due to COVID-19 restrictions. During this year, practical sessions were condensed into shorter timeframes and involved fewer practice opportunities due to reduced simulation time. These adjustments may have impacted students' learning and performance outcomes, reflecting the unique challenges posed during this period. Participants All participants were second-year undergraduate paramedicine students enrolled at [Anonymous] University from 2019 to 2024. This data collection approach reflects a unique strategy in paramedicine education, leveraging high-frequency simulation with structured debriefing to develop and assess clinical competence. Instrument The CCAT was developed by the primary investigator in collaboration with the paramedicine faculty to assess paramedicine students' clinical performance across 10 core domains. The 10 domains were identified by the principal investigator and underwent consensus and face validity within the faculty to ensure an accurate representation of essential competencies for paramedic practice. These domains were initially identified based on similar studies ( 33 , 38 ), which predominantly focused on entry-to-practice assessments and demonstrated good reliability and validity in those contexts. This version of the CCAT was adapted and modified to serve as an updated tool designed specifically to measure clinical competence and performance throughout the undergraduate developmental phase through to entry to practice. This update reflects the most recent expectations of the evolving needs of the profession, ensuring alignment with the dynamic nature of paramedic practice. The CCAT evaluates students across 10 key domains: situational awareness, clinical evaluation, integrating diagnostic technologies, history taking, clinical judgment, procedural skills, communication, professionalism, resource management, and cultural competence. These domains encompass the breadth of skills and behaviours essential for safe and effective paramedic practice, ensuring comprehensive assessment coverage. The rubric-based design provides structured feedback for both practical and examination scenarios, supporting consistent, standardised evaluations across all performance-based assessments. Each domain is scored on a 10-point scale, ranging from beginner (1–2, at risk), advanced beginner (3–4, below standard), competent (5–8, meets standard), and proficient (9–10, exceeds expectations). This adapted scale, grounded in the educational framework of the Dreyfus and Dreyfus model of adult skill acquisition ( 39 ), underpins the assessment process, offering a nuanced evaluation of students’ progression through the stages of learning to reach competence in each domain. Widely adopted in medical education, this model has been effectively used to assess developmental stages and determine clinical competence in both simulated and clinical settings ( 40 – 43 ). A total score across all domains was converted to a percentage score out of 100%, allowing for consistent comparison and calculation of average scores across practice sessions and summative exams. This approach ensured alignment with the continuous scoring methodology applied throughout the study. Data quality assurance The collected data were initially managed, sorted, and cleaned in Microsoft Excel to ensure consistency and accuracy. This process included reviewing for incomplete records, resolving discrepancies by cross-referencing sessional notes, and excluding duplicates. To further enhance data integrity, two researchers (JV and GC) independently verified the accuracy of data entry and cleaning processes, ensuring consistency and reducing the risk of errors. Once cleaned and verified, the data were transferred into the Statistical Package for the Social Sciences (SPSS) for detailed statistical analysis. This multi-step process, involving independent verification, ensured high-quality data for use in the study. Data collection This study was a retrospective analysis of outcome data obtained during weekly practice sessions and summative practical exams as part of standard teaching practices. Students were rated by sessional academics using the CCAT, which facilitated structured feedback during weekly practice sessions and performance scores during summative practical exams. Demographic data, including the year of course offering and student gender, were collected to provide a basic profile of participants. The cohort was predominantly made up of school leavers, with ages ranging mostly between 18 and 21 years. Additional details, such as exact age data, were not collected, as they were not directly relevant to the study's primary aim of assessing the relationship between simulation practice and clinical performance. Data collection was initially conducted through Google Forms before transitioning to Microsoft Power Apps. These platforms enabled sessional academics to efficiently record student performance data during weekly practice sessions and summative practical exams. Across the study period, the number of academic sessionals facilitating these sessions ranged from 10 to 17 per year, reflecting consistent staffing levels to support the program's objectives. Data analysis Data analysis was conducted using SPSS Version 29.0 (IBM Corp., Chicago, Illinois, USA). Descriptive statistics including frequency distribution and measures of central tendency (mean and standard deviation with normality tests) were used to assess the general patterns and distribution of categorical and continuous variables respectively. As only a small number (range 2 to 4) of students voluntarily participated in weekly practice session in each of the four rooms, the mean performance scores of three tertiles (mean of 3 to 4 weeks combined) were computed. One sample t-tests, One-way ANOVA were carried out to compare the differences in the tertile and exam mean scores between different genders and student cohorts across various years. Paired sample t-tests along with boxplots were used to compare the changes in the mean scores over time, across the three tertiles. Pearson’s correlations were undertaken to assess the relationships among three tertile means and exam scores. Hierarchical multivariable regression models were performed to identify significant variables in predicting the exam score after adjusting for confounding effects. Collinearity tests were also performed to examine the potential high levels of intercorrelations among the independent variables. Ethical considerations Ethics approval was obtained from the [Anonymous] University Human Research Ethics Committee (HREC Ref No: 2024/073). Results A total of six years of outcome assessment data, averaging 51 (range 45 to 57) students per year (n = 305), was included for analysis. Basic characteristics of these participants with their practice and exam scores are summarised in Table 1. Across the six years, 66.9% (n = 204) were female. Of the 305 participants, a total of 1251 practice scenarios were completed prior to the exam, averaging 4.1 practice scenarios per participant. Each year averaged 207 practice scenarios prior to the exam, with the lowest count in 2020 (n = 165) and the highest in 2019 (n = 227). Table 1 Descriptive statistics of participants, practice scores, and exam scores 2019 2020 2021 2022 2023 2024 Total Average Demographics Sample size (range) 57 54 48 53 45 48 305 51 (45–57) Gender (F/M) 35 (61.4%)/ 22 (38.6%) 33 (61.1%)/ 21 (38.9%) 35 (72.9%)/ 13 (27.1%) 37 (69.8%)/ 16 (30.2%) 29 (64.4%)/ 16 (35.6%) 35 (72.9%)/ 13 (27.1%) 204 (66.9%)/ 101 (33.1%) 34 (67.1%)/ 17 (32.9%) Number of raters per year (range) 12 10 17 11 12 11 - 12.2 (10–17) Practice scores Total practice scenarios 227 165 196 222 210 223 1243 207.2 (4.1) Mean (SD) number of practice scenarios 4.0 (1.4) 2.5 (1.4) 4.1 (1.8) 4.1 (1.5) 4.6 (1.9) 4.6 (1.6) - 4.0 (1.6) Practice mean (SD) scores 71.4% (8.3) 65.7% (8.6) 58.1% (5.9) 63.8% (5.6) 59.9% (6.3) 59.1% (4.6) - 63.4% (8.2) Exam scores Exam mean (SD) score 81.9% (9.2) 59.6% (4.9) 74.0% (12) 72.6% (11.1) 69.5% (7.7) 69.5% (7.0) - 71.2% (8.7) Notes: Abbreviations: F, female; M, male; SD, standard deviation. Primary outcome: association between scenario frequency and summative exam performance A positive, statistically significant correlation (r s =0.257, p < 0.001) was found between scenario frequency and summative exam scores, suggesting that increased exposure to practice scenarios was associated with improved exam performance (Table 2). Whilst this correlation is considered weak or modest in strength, it is statistically significant and suggests a positive relationship between the frequency of practice scenarios performed and improved exam performance. Table 2 Correlation analysis between scenario frequency and summative exam performance Group 1 (week 3 to 5) Group 2 (week 6 to 9) Group 3 (week 10 to 12) Exam score Frequency of scenarios Group 1 (week 3 to 6) 1 0.180** (p = 0.008) 0.158* (p = 0.017) 0.197** (p = 0.002) -0.039 (p = 0.540) Group 2 (week 7 to 9) 0.180 ** (p = 0.008) 1 0.284 ** (p < 0.0001) 0.141 * (p = 0.024) 0.004 (p = 0.944) Group 3 (week 10 to 12) 0.158 * (p = 0.017) 0.284 ** (p < 0.0001) 1 0.229 ** (p < 0.0001) 0.188 ** (p = 0.002) Group 4 (exam score) 0.197 ** (p = 0.002) 0.141 * (p = 0.024) 0.229 ** (p < 0.0001) 1 0.257 ** (p < 0.0001) Frequency of scenarios -0.039 (p = 0.540) 0.004 (p = 0.944) 0.188 ** (p = 0.002) 0.257 ** (p < 0.0001) 1 Notes: Levels of statistical significance: * p < 0.05; ** p < 0.01; *** p < 0.001 Group 1 – Week 3 to 6; Group 2 – Week 7 to 9; Group 3 – Week 10 to 12; Group 4 – Exam Secondary outcomes: 2.1 Correlation between practice session performance and summative exam outcomes To evaluate performance progress from the beginning of the trimester through to the summative exam, practice session data were divided into four groups: the first three groups representing tertiles of weekly practice sessions (Group 1: Weeks 3 to 6, Group 2: Weeks 7 to 9, and Group 3: Weeks 10 to 12), and the fourth group representing the summative exam scores. A weaker, yet statistically significant positive correlation was observed between performance in the first two groups (Group 1 and Group 2) and exam outcomes (p < 0.05). The strength of this correlation improved notably when comparing performance in Group 3 to the exam (rs = 0.229, p < 0.001), suggesting that performance in the final weeks of the trimester is more closely associated with exam success (Table 2). 2.2 Performance improvement over the trimester A paired samples t-test compared performance across the four intervals: Group 1 (Weeks 3 to 6), Group 2 (Weeks 7 to 9), Group 3 (Weeks 10 to 12), and the summative exam scores (Group 4). The analysis revealed statistically significant improvements in performance between each consecutive group (p < 0.05). These incremental gains in performance demonstrate a steady progression in clinical competence as students advanced through the trimester. The results suggest that frequent simulation-based practice throughout the trimester supports continuous improvement, culminating in enhanced performance during the summative exam. These findings are illustrated in Fig. 1 and detailed in Table 3, highlighting the effectiveness of consistent practice in developing clinical competence and readiness for performance-based assessments. Table 3 Results of paired sample t-test analysis comparison average score progression between Group 1, 2, 3 and the exam Group score comparison N Mean (SD) 95% CI t P value Lower Upper Pair 1 Group 1 & Group 2 213 3.74 (13.41) 1.93 5.55 -4.070 p < 0.001*** Pair 2 Group 2 & Group 3 227 1.97 (11.84) 0.43 3.52 -2.511 p = 0.013* Pair 3 Group 3 & Group 1 228 6.15 (13.22) 4.42 7.87 7.026 p < 0.001*** Pair 4 Group 3 and Group 4 (exam) 265 6.30 (13.05) 4.72 7.88 -7.862 p < 0.001*** Note: Levels of statistical significance: * p < 0.05; ** p < 0.01; *** p < 0.001 Group 1 – Week 3 to 6; Group 2 – Week 7 to 9; Group 3 – Week 10 to 12; Group 4 – Exam Paired samples t-test correlation Abbreviations: CI, confidence interval; SD, standard deviation. Table 4 demonstrates the results of hierarchical multivariable regression analyses (the final model). The first model included gender and academic year (Block 1 variables). The second model added the mean scores of the three tertiles (Block 2 variables) while adjusting for Block 1 variables. The final model included frequency of practice in addition to the Block 1 and Block 2 variables. Each block of variables made a significant contribution to the model (p<0.001, F=6.02, p<0.001). The predictors in the final model collectively accounted for a significant proportion in explaining the variance in exam outcome. After controlling for the effects from the demographic factors (especially academic year as a significant confounder), as presented in Table 4, the mean score in Tertile 3 and frequency of practice were significant predictors for exam outcome (p values 0.023 and 0.006 respectively). The results suggested that the higher mean score in Tertile 3 and the more practice a student performed, the higher exam score. Collinearity checks confirmed no specific concern for multicollinearity among the continuous independent variables (all Torrence > 0.5 and VIF < 2). Table 4. Hierarchical multivariable regression: factors independently associated with the exam score (results of the final model) B P value 95% CI for B Lower Upper Gender -0.26 0.870 -3.34 2.83 Academic year -1.12 0.031* -2.13 -0.11 Tertile 1 mean score 0.13 0.076 -0.01 0.28 Tertile 2 mean score 0.02 0.847 -0.15 0.18 Tertile 3 mean score 0.20 0.023* 0.03 0.37 Frequency of practice 1.67 0.006** 0.50 2.84 Constant 2299.82 0.028 250.0 4349.6 Note: Levels of statistical significance: * P<0.05; ** P<0.01; *** P<0.001 Abbreviations: CI, confidence interval Discussion This study explored the impact of high-frequency simulation-based practice sessions on the development of clinical competence and exam performance in paramedicine students. Findings revealed a statistically significant association between the frequency of practice scenarios and summative exam performance, underscoring the benefits of structured, repetitive practice in improving clinical competence (44, 45). Moreover, data from 2020, when COVID-19 lockdowns limited the number of available practice scenarios, further emphasised the importance of consistent exposure. That year, with the lowest recorded scenario frequency, students also displayed one of the lowest exam performance scores, reinforcing the critical role of high-frequency practice in achieving clinical competence. Simulation has become a cornerstone of healthcare education, allowing for repeated rehearsal of complex scenarios without patient risk (46). This approach supports students’ progression through stages of competency, enabling them to develop both technical and decision-making skills in a structured environment (47). The incremental gains observed between each tertile, demonstrated by significant improvements in performance across Groups 1, 2, 3, and 4 (summative exam), align with Kolb’s experiential learning theory, where cycles of experience, reflection, and feedback contribute to deeper learning (23). By providing a feedback-rich environment, simulation encourages reflective practice and continual skill refinement, which are essential for achieving clinical competence (48). The findings suggest that while performance in the first two tertiles showed weaker yet significant correlations with exam outcomes, improved performance in the final tertile had a stronger correlation with exam success. This highlights the critical role of consistent performance in the final weeks of the trimester in achieving exam readiness. The results of the hierarchical regression further confirm that both mean performance in Tertile 3 and the frequency of practice were significant predictors of summative exam performance (p=0.023 and p=0.006, respectively). These results emphasise the importance of maintaining consistent practice, especially toward the end of the trimester, to achieve optimal outcomes. The association between practice frequency and improved exam performance aligns with findings in SBE, where deliberate, high-frequency exposure to simulation yields positive outcomes in skill acquisition and retention (2, 49). For paramedicine students, whose real-world clinical exposure is often limited, intentional practice with feedback is especially valuable, promoting readiness for practice and supporting students’ progression toward competency (5). This research highlights the growing importance of high-frequency simulation, particularly in an era where some universities are reducing in-person teaching in favour of predominantly online degrees with minimal practical contact hours (50). While this approach may address institutional priorities such as scalability and cost efficiency, it raises significant concerns about the impact on students’ preparedness for real-world paramedic practice. By embedding frequent simulation and feedback into curricula, institutions can help ensure students graduate with the necessary clinical competence and work readiness, regardless of delivery model (51). Additionally, the progressive improvement observed week-to-week aligns with Kirkpatrick’s framework of educational program evaluation, demonstrating that simulation significantly enhances participant satisfaction, knowledge acquisition, and clinical performance—three key levels of this model (52). Although data on Kirkpatrick’s fourth level (patient outcomes) in paramedicine is limited, existing research in other health professions suggests that increased simulation exposure positively impacts clinical competence, which may ultimately translate to improved patient care (12, 53). This study may be the first of its kind in paramedicine to show that structured, high-frequency practice sessions combined with measured performance and intentional feedback can predictably improve clinical competence and summative exam outcomes. These findings contribute new evidence supporting simulation’s role as a powerful tool for competency-based assessment and skill development. The results underscore the importance of integrating consistent, structured simulation practice and feedback into paramedicine curricula to enhance clinical competence and readiness for real-world practice. Future research implications and recommendations This study provides valuable insights into the relationship between high-frequency simulation-based practice and clinical competence development. Future research should investigate whether performance trends during the trimester can predict exam pass/fail outcomes, as well as explore the variability in rater scores and its impact on assessment consistency and reliability. Extending this research across different courses, year levels, and multi-site settings could offer a broader understanding of simulation’s effectiveness in paramedicine education. Comparative analyses between institutions or programs may further validate the role of structured, non-graded practice sessions and help identify best practices. These insights can guide educators in refining simulation-based curricula to enhance student progression and clinical readiness. Strengths and limitations Strengths of this study include its novel approach of capturing non-graded performance scores during weekly practice sessions, fostering a non-threatening learning environment while generating robust data to evaluate student progress. This study is one of the first in paramedicine education to demonstrate incremental improvements in clinical competence throughout a trimester, reflecting meaningful learning. The use of hierarchical multivariable statistical modelling provided a comprehensive analysis of the relationship between practice frequency and exam performance, with significant gains observed particularly in the final tertile. These findings offer practical insights for enhancing simulation-based curricula. The limitations of this study include its focus on a single second-year cardiology course at one university, which may limit generalisability. Despite moderation efforts, variability in rater scoring could have influenced outcomes. Additionally, external factors such as student motivation, prior experience, or placement exposure were not accounted for. The COVID-19 disruption in 2020, which reduced practice opportunities, may have impacted students’ ability to achieve competence comparable to other cohorts. Addressing these limitations in future research through multi-site studies, diverse course settings, and broader demographics could enhance the robustness of these findings. Conclusion High-frequency simulation-based education with structured feedback significantly enhances clinical competence in paramedicine students at a single Australian University. This study's novel approach of leveraging non-graded performance-based assessments during weekly practice sessions maintained a supportive learning environment while generating robust data for evaluating student progress. Analysis of six years of data demonstrated progressive performance improvements throughout the teaching period, with practice frequency and performance in the final weeks of the trimester correlating significantly with summative practical exam outcomes. These findings validate simulation as an effective tool for developing competence, reinforcing the importance of structured, repetitive practice in preparing students for performance-based assessments. Further research should explore the long-term impact on patient outcomes and the broader application of these strategies across different courses and institutions to enhance paramedicine education and clinical readiness. Declarations Author Contribution J.V. conceptualized the study, developed the Clinical Competence Assessment Tool (CCAT), collected, cleaned, and prepared the data, performed the preliminary analysis, interpreted the results, and drafted the manuscript. G.C. assisted with data cleaning, preparation, and preliminary analysis. P.L. designed the statistical modelling approach, performed advanced analyses, interpreted the results, and contributed critical revisions to the manuscript. M.B. supervised the project, guided data interpretation, and contributed critical revisions to the manuscript. All authors reviewed and approved the final manuscript. Acknowledgement We would like to acknowledge the contributions of the paramedicine students, sessional academics, and academic staff at Griffith University’s School of Medicine and Dentistry. Their participation, feedback, and commitment to simulation-based education were invaluable in the development and implementation of this study. Data Availability Data available on request. References Eaton G, Wong G, Williams V, Roberts N, Mahtani KR. Contribution of paramedics in primary and urgent care: a systematic review. Br J Gen Pract. 2020;70(695):e421-e6. Ziv A, Wolpe PR, Small SD, Glick S. Simulation-based medical education: an ethical imperative. Acad Med. 2003;78(8):783-8. Patel R, Sharma S. Credentialing. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Sandeep Sharma declares no relevant financial relationships with ineligible companies.2023. Bearman M, Greenhill J, Nestel D. The power of simulation: a large-scale narrative analysis of learners' experiences. Med Educ. 2019;53(4):369-79. Palaganas JC, Fey M, Simon R. Structured Debriefing in Simulation-Based Education. AACN Adv Crit Care. 2016;27(1):78-85. Sawyer T, White M, Zaveri P, Chang T, Ades A, French H, et al. Learn, see, practice, prove, do, maintain: an evidence-based pedagogical framework for procedural skill training in medicine. Acad Med. 2015;90(8):1025-33. Kohn L. To err is human: an interview with the Institute of Medicine's Linda Kohn. Jt Comm J Qual Improv. 2000;26(4):227-34. Friedman PA, Wiener, E. L., Nagel, D. C., Carterette, E. C. Human Factors in Aviation. 1st ed. California: Academic Press; 1988 28 July 1988. 684 p. Lenchus JD. End of the "see one, do one, teach one" era: the next generation of invasive bedside procedural instruction. J Am Osteopath Assoc. 2010;110(6):340-6. Cheng A, Kessler D, Mackinnon R, Chang TP, Nadkarni VM, Hunt EA, et al. Reporting Guidelines for Health Care Simulation Research: Extensions to the CONSORT and STROBE Statements. Simul Healthc. 2016;11(4):238-48. Rosen KR. The history of medical simulation. J Crit Care. 2008;23(2):157-66. Cook DA, Hatala R, Brydges R, Zendejas B, Szostek JH, Wang AT, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011;306(9):978-88. McGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. A critical review of simulation-based medical education research: 2003-2009. Med Educ. 2010;44(1):50-63. Schaefer JJ, 3rd, Vanderbilt AA, Cason CL, Bauman EB, Glavin RJ, Lee FW, et al. Literature review: instructional design and pedagogy science in healthcare simulation. Simul Healthc. 2011;6 Suppl:S30-41. McGaghie WC, Draycott TJ, Dunn WF, Lopez CM, Stefanidis D. Evaluating the impact of simulation on translational patient outcomes. Simul Healthc. 2011;6 Suppl(Suppl):S42-7. Zendejas B, Brydges R, Wang AT, Cook DA. Patient outcomes in simulation-based medical education: a systematic review. J Gen Intern Med. 2013;28(8):1078-89. Scholtz AK, Monachino AM, Nishisaki A, Nadkarni VM, Lengetti E. Central venous catheter dress rehearsals: translating simulation training to patient care and outcomes. Simul Healthc. 2013;8(5):341-9. Ericsson KA. Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med. 2004;79(10 Suppl):S70-81. van Merriënboer JJG, Sweller J. Cognitive load theory and complex learning:: Recent developments and future directions. Educ Psychol Rev. 2005;17(2):147-77. Mamede S, Schmidt HG. The structure of reflective practice in medicine. Med Educ. 2004;38(12):1302-8. Dieckmann P, Molin Friis S, Lippert A, Ostergaard D. The art and science of debriefing in simulation: Ideal and practice. Med Teach. 2009;31(7):e287-94. Fanning RM, Gaba DM. The role of debriefing in simulation-based learning. Simul Healthc. 2007;2(2):115-25. Abulebda K, Auerbach M, Limaiem F. Debriefing Techniques Utilized in Medical Simulation. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Marc Auerbach declares no relevant financial relationships with ineligible companies. Disclosure: Faten Limaiem declares no relevant financial relationships with ineligible companies.2023. Sawyer T, Loren D, Halamek LP. Post-event debriefings during neonatal care: why are we not doing them, and how can we start? J Perinatol. 2016;36(6):415-9. Chmil JV, Turk M, Adamson K, Larew C. Effects of an Experiential Learning Simulation Design on Clinical Nursing Judgment Development. Nurse Educ. 2015;40(5):228-32. Grantcharov TP, Kristiansen VB, Bendix J, Bardram L, Rosenberg J, Funch-Jensen P. Randomized clinical trial of virtual reality simulation for laparoscopic skills training. Br J Surg. 2004;91(2):146-50. Chaer RA, Derubertis BG, Lin SC, Bush HL, Karwowski JK, Birk D, et al. Simulation improves resident performance in catheter-based intervention: results of a randomized, controlled study. Ann Surg. 2006;244(3):343-52. Banks E, Pardanani S, King M, Chudnoff S, Damus K, Freda MC. A surgical skills laboratory improves residents' knowledge and performance of episiotomy repair. Am J Obstet Gynecol. 2006;195(5):1463-7. Ahlberg G, Hultcrantz R, Jaramillo E, Lindblom A, Arvidsson D. Virtual reality colonoscopy simulation: a compulsory practice for the future colonoscopist? Endoscopy. 2005;37(12):1198-204. Abelsson A, Bisholt B. Nurse students learning acute care by simulation - Focus on observation and debriefing. Nurse Educ Pract. 2017;24:6-13. Saqe-Rockoff A, Ciardiello AV, Schubert FD. Low-Fidelity, In-Situ Pediatric Resuscitation Simulation Improves RN Competence and Self-Efficacy. J Emerg Nurs. 2019;45(5):538-44 e1. Bienstock J, Heuer A, Zhang Y. Simulation-Based Training and Its Use Amongst Practicing Paramedics and Emergency Medical Technicians: An Evidence-Based Systematic Review. International Journal of Paramedicine. 2022(1):12-28. Diamond A, Bilton N. The Current State on the Use of Simulation in Paramedic Education. Australasian Journal of Paramedicine. 2021;18:1-5. Mahdavi Ardestani SF, Adibi S, Golshan A, Sadeghian P. Factors Influencing the Effectiveness of E-Learning in Healthcare: A Fuzzy ANP Study. Healthcare. 2023;11(14):2035. Frenk J, Chen LC, Chandran L, Groff EOH, King R, Meleis A, et al. Challenges and opportunities for educating health professionals after the COVID-19 pandemic. Lancet. 2022;400(10362):1539-56. Ashburn NP, Hendley NW, Angi RM, Starnes AB, Nelson RD, McGinnis HD, et al. Prehospital Trauma Scene and Transport Times for Pediatric and Adult Patients. West J Emerg Med. 2020;21(2):455-62. Nagata I, Abe T, Nakata Y, Tamiya N. Factors related to prolonged on-scene time during ambulance transportation for critical emergency patients in a big city in Japan: a population-based observational study. BMJ Open. 2016;6(1):e009599. Gugiu MR, Cash R, Rivard M, Cotto J, Crowe RP, Panchal AR. Development and Validation of Content Domains for Paramedic Prehospital Performance Assessment: A Focus Group and Delphi Method Approach. Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors. 2021;25(2):196-204. Dreyfus SE, Dreyfus, H.L. A five-stage model of the mental activities involved in directed skill acquisition. In: Force USA, editor. Air Force Office of Scientific Research: University of California; 1980. Carraccio CL, Benson BJ, Nixon LJ, Derstine PL. From the educational bench to the clinical bedside: translating the Dreyfus developmental model to the learning of clinical skills. Acad Med. 2008;83(8):761-7. Daaleman TP. The medical home: locus of physician formation. J Am Board Fam Med. 2008;21(5):451-7. Batalden P, Leach D, Swing S, Dreyfus H, Dreyfus S. General competencies and accreditation in graduate medical education. Health Aff (Millwood). 2002;21(5):103-11. Edgar L, McLean, S., O Hogan, S., Hamstra, S., Holmboe, E. . The Milestones Guidebook - Accreditation Council for Graduate Medical Education (ACGME). 2020 ed2020. 38 p. Okuda Y, Bryson EO, DeMaria S, Jr., Jacobson L, Quinones J, Shen B, et al. The utility of simulation in medical education: what is the evidence? Mt Sinai J Med. 2009;76(4):330-43. Issenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10-28. Motola I, Devine LA, Chung HS, Sullivan JE, Issenberg SB. Simulation in healthcare education: a best evidence practical guide. AMEE Guide No. 82. Med Teach. 2013;35(10):e1511-30. Dalseg TR, Thoma B, Wycliffe-Jones K, Frank JR, Taber S. Enabling Implementation of Competency Based Medical Education through an Outcomes-Focused Accreditation System. Perspect Med Educ. 2024;13(1):75-84. Rudolph JW, Simon R, Dufresne RL, Raemer DB. There's no such thing as "nonjudgmental" debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49-55. McGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med. 2011;86(6):706-11. McGee RG, Wark S, Mwangi F, Drovandi A, Alele F, Malau-Aduli BS, et al. Digital learning of clinical skills and its impact on medical students' academic performance: a systematic review. BMC Med Educ. 2024;24(1):1477. McKenna KD, Carhart E, Bercher D, Spain A, Todaro J, Freel J. Simulation Use in Paramedic Education Research (SUPER): A Descriptive Study. Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors. 2015;19(3):432-40. Kirkpatrick D, Kirkpatrick J. Evaluating Training Programs: The Four Levels: Berrett-Koehler Publishers; 2006. Edelson DP, Litzinger B, Arora V, Walsh D, Kim S, Lauderdale DS, et al. Improving in-hospital cardiac arrest process and outcomes with performance debriefing. Arch Intern Med. 2008;168(10):1063-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Feb, 2025 Editor assigned by journal 12 Feb, 2025 Submission checks completed at journal 11 Feb, 2025 First submitted to journal 27 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5916090","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414696693,"identity":"efff6716-8700-4991-9784-eb84fbe2df63","order_by":0,"name":"Jean-Paul Veronese","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie3OMUsDMRTA8XcEMsV2zS2Xr/DCgSB+mXNpFwWncpOmHNwtde/Wr9Cp80lAl9i5g0Ol0EWHFheFCn0i1g4X6iiYPyS8QH4kAKHQHywyu5HVAJi0OQg68F8RnhFJ45IfIHsJpC2NzSHCjNCLPH9MVDV4fc4vUbbUTQ2rnoX2MPN8TKTauWWK7mFy4hAl560sGk4tyJmP8E7cL+3ZWF5MtMHNFecC2VFpAfyk+07kejR6WRL5fIXIBxHlJewuIpLBTLCnHYmIoI8UzMbGWT12nePoi5zj7WDaFdrNG4mu+v21ya1SlV2szQalKpyev/VOk+S++RVd/Mxcfk81LdF4n1J7M1v5boVCodD/bgv8Xlo/gStPFwAAAABJRU5ErkJggg==","orcid":"","institution":"Griffith University","correspondingAuthor":true,"prefix":"","firstName":"Jean-Paul","middleName":"","lastName":"Veronese","suffix":""},{"id":414696694,"identity":"c1e590fb-61c5-42f7-b439-6c7f16dce17c","order_by":1,"name":"Grace Crowther","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Grace","middleName":"","lastName":"Crowther","suffix":""},{"id":414696695,"identity":"7ccb1631-2a6e-4a46-beae-2476dfff171e","order_by":2,"name":"Patricia Lee","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Lee","suffix":""},{"id":414696696,"identity":"fc1a2c33-8080-42d7-9cbc-1fe6dbc7a060","order_by":3,"name":"Malcolm Boyle","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Malcolm","middleName":"","lastName":"Boyle","suffix":""}],"badges":[],"createdAt":"2025-01-28 04:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5916090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5916090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76280234,"identity":"6d9b3d00-ac4e-4bb0-a823-0a5abe177fbb","added_by":"auto","created_at":"2025-02-14 10:27:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of scores between Groups 1 to 4 indicating progressive improvements in clinical competence and performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: Group 1 – Week 3 to 6; Group 2 – Week 7 to 9; Group 3 – Week 10 to 12; Group 4 – Exam\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5916090/v1/d5423cc6e4dbafb9374f3438.png"},{"id":76280656,"identity":"a3125b2f-48fc-4063-b361-21a201b5d94c","added_by":"auto","created_at":"2025-02-14 10:35:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046676,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5916090/v1/228ec8e3-e3fc-487e-8013-8ed4fc5e9843.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of simulation with debriefing on developing clinical competence and performance in paramedicine students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParamedics manage a broad spectrum of emergency and non-emergency medical and trauma cases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The expected level of clinical proficiency for paramedics is continually expanding, and like other healthcare professions, deficiencies in competence can have a detrimental impact on patient safety and outcomes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Before practicing independently, paramedicine students must demonstrate competence to oversight bodies responsible for safeguarding the profession and public, including educational institutions, prospective employers, and licensing bodies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Performance-based examinations are essential in ensuring clinical competence. Growing evidence indicates that educational technologies, particularly simulation-based education (SBE), play a critical role in facilitating the attainment and reinforcement of clinical competencies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Debriefing is another effective teaching strategy shown to support the development and maintenance of clinical competence and performance (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe acquisition of clinical competence is a fundamental goal in medical education and requires specific teaching, learning and assessment (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Whilst the adage \u003cem\u003e\u0026ldquo;see one, do one, teach one\u0026rdquo;\u003c/em\u003e has been the traditional paradigm widely adopted to develop clinical competence (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), this approach has been brought under scrutiny as far back as the late 1990s out of concerns for patient safety (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This approach involves students having direct patient contact, practicing procedural skills under the supervision of a qualified clinician. It was reported that nearly 100,000 patients die annually from preventable mistakes in the hospital in the United States, with a further 1\u0026nbsp;million injured (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Medical education has lagged behind other similar high risk professions, such as aviation, which also requires sophisticated technical and behavioural skills (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The proposed solution to replace this traditional method of developing clinical competence is SBE (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimulation is defined as \u003cem\u003e\u0026ldquo;a tool, device and/or environment that mimics an aspect of clinical care\u0026rdquo;\u003c/em\u003e (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). While not new, SBE has become a well-established pedagogy in healthcare education, predominantly rooted in the aviation industry (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Simulation allows students to develop clinical competence safely, benefiting patient care and safety (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A key advantage of SBE is the controlled environment that facilitates deliberate practice (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Students can deconstruct skills, engage in tasks tailored to their learning stage, and manage cognitive load effectively (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Within a structured framework, they can rehearse skills repetitively, achieving fluency and instinctive performance (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Simulation enables students to build on existing knowledge, progress through learning stages, and deepen clinical competence. Unlike clinical settings, which are often opportunistic and unstructured, simulation better supports deliberate practice, promoting meaningful learning. While real patient interaction is essential at advanced stages, simulation has been shown to be an effective tool in improving the safe delivery of medical care (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe structure of simulation improves the quality of feedback to students, a critical component of clinical skills development and maintenance. Simulation further develops reflective practice, helping students self-monitor their performance (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Post-simulation debriefing is recognised as a cornerstone component of developing clinical competence in simulated settings (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This structured discussion allows students to gain insight into their actions and decisions, enhancing both learning outcomes and future performance (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Debriefing provides faculty and students a chance to reflect, share mental models, and strengthen the reasoning underlying clinical judgment (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Kolb\u0026rsquo;s experiential learning cycle supports this process, enabling learners to engage in concrete experiences through simulation, followed by observation and reflection through debriefing (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Students then form abstract concepts and generalisations that can be tested in future situations, leading to subsequent new concrete experiences and learning (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese structured and repeated experiences in simulation have shown significant benefits on learning outcomes, improving students\u0026rsquo; knowledge, skills, and behaviour (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Applying Kirkpatrick\u0026rsquo;s four-level evaluation model, evidence demonstrates simulation\u0026rsquo;s effectiveness at the first three levels: participant satisfaction, knowledge acquisition, and performance improvement (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Although the model\u0026rsquo;s fourth level, focusing on patient outcomes, is still emerging (\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), studies suggest that frequent simulations are associated with improved clinical competence (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this growing body of evidence in other healthcare disciplines, there is a notable gap in paramedicine research. While SBE has been extensively studied in medical education (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and other allied health fields (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), its usage patterns and specific impact on paramedic practice remain poorly understood (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Furthermore, most paramedicine studies focus on exit-level outcomes or program-level evaluation (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), with limited attention to how simulation can systematically enhance clinical competence and performance during the learning process.\u003c/p\u003e \u003cp\u003eIn the evolving landscape of higher education, particularly within healthcare professions such as paramedicine, there is a discernible shift towards increased online learning and condensed practical sessions. While these models offer flexibility and accessibility, concerns have been raised regarding their effectiveness in developing essential clinical competencies. Research indicates that while e-learning enhances accessibility and flexibility, its success hinges on factors like engagement and the availability of appropriate technology (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Moreover, the COVID-19 pandemic has accelerated the adoption of digital platforms in health education, presenting both opportunities and challenges in ensuring comprehensive clinical education (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Given these dynamics, integrating frequent, structured simulation into paramedicine curricula becomes fundamental to ensure that students acquire and refine the clinical competencies necessary for effective practice. This study aims to address these gaps by evaluating the role of simulation in paramedicine education, particularly in fostering clinical competence and performance improvement throughout a trimester.\u003c/p\u003e\n\u003ch3\u003eAim\u003c/h3\u003e\n\u003cp\u003eThe aim of this study was to determine the association between weekly practice session performance and summative practical exam outcomes, evaluating whether improvements in simulated practice sessions indicate clinical competence development among paramedicine students.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eThe primary objective was to determine whether the frequency of scenarios performed during weekly practice sessions is associated with improved summative practical exam performance.\u003c/p\u003e \u003cp\u003eThe secondary objective was to assess whether improved performance in weekly practice sessions correlates with improved performance in the summative practical exam.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThis was a retrospective analysis of clinical performance outcome data collected from 2019 to 2024 in the \u003cb\u003e[Anonymous]\u003c/b\u003e University paramedicine program. Weekly performance assessments, rated by sessional academics, were compared to the end-of-trimester practical exam results.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSetting and Procedure\u003c/h3\u003e\n\u003cp\u003eThis study was conducted at \u003cb\u003e[Anonymous]\u003c/b\u003e University School of Medicine and Dentistry (SoMAD) within the paramedicine program. The data analysed were collected during weekly practical sessions and summative practical exams in the second-year Cardiology course, where paramedicine students were rated by sessional academic staff on clinical competence and performance during simulations. This data, collected using the Clinical Competence Assessment Tool (CCAT) from 2019 to 2024, represents a novel approach to understanding the role of non-graded simulated practical sessions in developing clinical competence and influencing performance on exam outcomes.\u003c/p\u003e \u003cp\u003eSimulation was a key component of this course, forming one aspect of a broader multimodal educational approach designed to support the development of clinical competence. The course integrated several complementary teaching, learning, and assessment strategies, including weekly ECG quizzes, a summative theory exam, and a skills sign-off assessment. Together, these strategies provided students with opportunities to consolidate theoretical knowledge and practical skills, ensuring a holistic approach to clinical competence development. The practical simulation sessions were structured to reinforce and integrate content covered during theory sessions, enabling students to apply their knowledge in a realistic, hands-on environment.\u003c/p\u003e \u003cp\u003eEach cohort of students attended weekly three hour simulated practical sessions and a final simulated practical exam, facilitated by sessional academics who were experienced and qualified paramedics. Four simulation rooms operated each week, each facilitated by a different sessional academic, with all rooms following the same set of scenarios to ensure consistency. Students remained in the same room throughout the trimester but were assigned a different sessional academic each week, providing them with diverse feedback and perspectives.\u003c/p\u003e \u003cp\u003eEach weekly session followed a structured lesson plan developed by faculty, introducing new complex content while also incorporating elements of scaffolding to build on prior knowledge and competencies as the trimester progressed. Students worked in pairs to assess, diagnose, and treat simulated patients within a 20-minute timeframe, approximating the time constraints expected in real-world paramedic practice (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This approach supported the progressive development of cumulative competencies, with scenarios designed to reinforce and integrate previously learnt skills alongside newly introduced content.\u003c/p\u003e \u003cp\u003eAfter each scenario, students participated in a structured feedback session using the CCAT. Facilitators and peers contributed feedback that helped students reflect on their performance, breaking down complex tasks into manageable components. This structured debriefing aligned with the program's novel approach of capturing weekly practical session performance to develop clinical competence and enhance exam performance.\u003c/p\u003e \u003cp\u003eStudents were required to attend a minimum of 80% (9 out of 12) of the weekly sessions. Within these sessions, students had the flexibility to perform as many or as few scenarios as they chose, allowing them to tailor their practice to their individual learning needs. Scenarios commenced in Week 3, after students had covered the fundamental knowledge and skills necessary for participation. Data collection continued until Week 12, allowing for a range of 0 to 10 scenarios performed per student, reflecting variations in individual engagement and practice needs.\u003c/p\u003e \u003cp\u003eThe course convenor facilitated a different room each week and served as the central point of contact for sessional academics, addressing any questions regarding content or scenarios. To ensure consistency in marking and feedback during the weekly practice sessions, the convenor monitored assessments and facilitated informal discussions with sessionals to address any discrepancies in scoring or feedback practices. This approach fostered alignment among sessional academics and ensured consistency across simulation rooms, contributing to a cohesive and well-supported learning environment.\u003c/p\u003e \u003cp\u003eFor summative practical exams, a formal moderation process was conducted to ensure consistency and fairness in the assessment of pass/fail outcomes. The moderation involved a review of assessment scores and sessional feedback, with discrepancies resolved through collaborative discussions among sessional academics and the convenor. This process aimed to fine-tune assessment marks and improved the consistency of scoring across all simulation rooms, ensuring robust and reliable exam outcomes.\u003c/p\u003e \u003cp\u003eThe final practical exam served as a comprehensive assessment of cumulative knowledge and competencies acquired throughout the trimester, integrating elements from all previously covered content areas. This format ensured that students demonstrated a holistic understanding and the ability to apply their knowledge and skills effectively in simulated scenarios.\u003c/p\u003e \u003cp\u003eSimulations used high-fidelity manikins (Laerdal\u0026rsquo;s SimMan\u0026reg; ALS) for clinical realism, with facilitators controlling vital signs via wireless SimPad technology. Students performed hands-on assessments such as pulse palpation, blood pressure measurement, oxygen saturation, and auscultation of lung and heart sounds. When needed, facilitators provided additional information (e.g. patient appearance, capillary refill, and 12-lead ECGs) to enhance the clinical experience.\u003c/p\u003e \u003cp\u003eAlthough simulations were conducted consistently across the study period, adjustments were made in 2020 due to COVID-19 restrictions. During this year, practical sessions were condensed into shorter timeframes and involved fewer practice opportunities due to reduced simulation time. These adjustments may have impacted students' learning and performance outcomes, reflecting the unique challenges posed during this period.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eAll participants were second-year undergraduate paramedicine students enrolled at \u003cb\u003e[Anonymous]\u003c/b\u003e University from 2019 to 2024. This data collection approach reflects a unique strategy in paramedicine education, leveraging high-frequency simulation with structured debriefing to develop and assess clinical competence.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstrument\u003c/h2\u003e \u003cp\u003eThe CCAT was developed by the primary investigator in collaboration with the paramedicine faculty to assess paramedicine students' clinical performance across 10 core domains. The 10 domains were identified by the principal investigator and underwent consensus and face validity within the faculty to ensure an accurate representation of essential competencies for paramedic practice. These domains were initially identified based on similar studies (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), which predominantly focused on entry-to-practice assessments and demonstrated good reliability and validity in those contexts. This version of the CCAT was adapted and modified to serve as an updated tool designed specifically to measure clinical competence and performance throughout the undergraduate developmental phase through to entry to practice. This update reflects the most recent expectations of the evolving needs of the profession, ensuring alignment with the dynamic nature of paramedic practice.\u003c/p\u003e \u003cp\u003eThe CCAT evaluates students across 10 key domains: situational awareness, clinical evaluation, integrating diagnostic technologies, history taking, clinical judgment, procedural skills, communication, professionalism, resource management, and cultural competence. These domains encompass the breadth of skills and behaviours essential for safe and effective paramedic practice, ensuring comprehensive assessment coverage. The rubric-based design provides structured feedback for both practical and examination scenarios, supporting consistent, standardised evaluations across all performance-based assessments.\u003c/p\u003e \u003cp\u003eEach domain is scored on a 10-point scale, ranging from beginner (1\u0026ndash;2, at risk), advanced beginner (3\u0026ndash;4, below standard), competent (5\u0026ndash;8, meets standard), and proficient (9\u0026ndash;10, exceeds expectations). This adapted scale, grounded in the educational framework of the Dreyfus and Dreyfus model of adult skill acquisition (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), underpins the assessment process, offering a nuanced evaluation of students\u0026rsquo; progression through the stages of learning to reach competence in each domain. Widely adopted in medical education, this model has been effectively used to assess developmental stages and determine clinical competence in both simulated and clinical settings (\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). A total score across all domains was converted to a percentage score out of 100%, allowing for consistent comparison and calculation of average scores across practice sessions and summative exams. This approach ensured alignment with the continuous scoring methodology applied throughout the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData quality assurance\u003c/h3\u003e\n\u003cp\u003eThe collected data were initially managed, sorted, and cleaned in Microsoft Excel to ensure consistency and accuracy. This process included reviewing for incomplete records, resolving discrepancies by cross-referencing sessional notes, and excluding duplicates. To further enhance data integrity, two researchers (JV and GC) independently verified the accuracy of data entry and cleaning processes, ensuring consistency and reducing the risk of errors. Once cleaned and verified, the data were transferred into the Statistical Package for the Social Sciences (SPSS) for detailed statistical analysis. This multi-step process, involving independent verification, ensured high-quality data for use in the study.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThis study was a retrospective analysis of outcome data obtained during weekly practice sessions and summative practical exams as part of standard teaching practices. Students were rated by sessional academics using the CCAT, which facilitated structured feedback during weekly practice sessions and performance scores during summative practical exams.\u003c/p\u003e \u003cp\u003eDemographic data, including the year of course offering and student gender, were collected to provide a basic profile of participants. The cohort was predominantly made up of school leavers, with ages ranging mostly between 18 and 21 years. Additional details, such as exact age data, were not collected, as they were not directly relevant to the study's primary aim of assessing the relationship between simulation practice and clinical performance.\u003c/p\u003e \u003cp\u003eData collection was initially conducted through Google Forms before transitioning to Microsoft Power Apps. These platforms enabled sessional academics to efficiently record student performance data during weekly practice sessions and summative practical exams. Across the study period, the number of academic sessionals facilitating these sessions ranged from 10 to 17 per year, reflecting consistent staffing levels to support the program's objectives.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted using SPSS Version 29.0 (IBM Corp., Chicago, Illinois, USA). Descriptive statistics including frequency distribution and measures of central tendency (mean and standard deviation with normality tests) were used to assess the general patterns and distribution of categorical and continuous variables respectively. As only a small number (range 2 to 4) of students voluntarily participated in weekly practice session in each of the four rooms, the mean performance scores of three tertiles (mean of 3 to 4 weeks combined) were computed. One sample t-tests, One-way ANOVA were carried out to compare the differences in the tertile and exam mean scores between different genders and student cohorts across various years. Paired sample t-tests along with boxplots were used to compare the changes in the mean scores over time, across the three tertiles. Pearson\u0026rsquo;s correlations were undertaken to assess the relationships among three tertile means and exam scores. Hierarchical multivariable regression models were performed to identify significant variables in predicting the exam score after adjusting for confounding effects. Collinearity tests were also performed to examine the potential high levels of intercorrelations among the independent variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEthical considerations\u003c/h2\u003e\n \u003cp\u003eEthics approval was obtained from the \u003cstrong\u003e[Anonymous]\u003c/strong\u003e University Human Research Ethics Committee (HREC Ref No: 2024/073).\u003c/p\u003e\n\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of six years of outcome assessment data, averaging 51 (range 45 to 57) students per year (n = 305), was included for analysis. Basic characteristics of these participants with their practice and exam scores are summarised in Table 1. Across the six years, 66.9% (n = 204) were female. Of the 305 participants, a total of 1251 practice scenarios were completed prior to the exam, averaging 4.1 practice scenarios per participant. Each year averaged 207 practice scenarios prior to the exam, with the lowest count in 2020 (n = 165) and the highest in 2019 (n = 227).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics of participants, practice scores, and exam scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size (range)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (45–57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (F/M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (61.4%)/ 22 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (61.1%)/ 21 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (72.9%)/ 13 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (69.8%)/ 16 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (64.4%)/ 16 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (72.9%)/ 13 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204 (66.9%)/ 101 (33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (67.1%)/ 17 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of raters per year (range)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2 (10–17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePractice scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal practice scenarios\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.2 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD) number of practice scenarios\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePractice mean (SD) scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.4% (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.7% (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.1% (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.8% (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.9% (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.1% (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.4% (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExam scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExam mean (SD) score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.9% (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.6% (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.0% (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.6% (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5% (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5% (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.2% (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes: Abbreviations: F, female; M, male; SD, standard deviation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003ePrimary outcome: association between scenario frequency and summative exam performance\u003c/h2\u003e\n \u003cp\u003eA positive, statistically significant correlation (r\u003csub\u003es\u003c/sub\u003e=0.257, p \u0026lt; 0.001) was found between scenario frequency and summative exam scores, suggesting that increased exposure to practice scenarios was associated with improved exam performance (Table 2). Whilst this correlation is considered weak or modest in strength, it is statistically significant and suggests a positive relationship between the frequency of practice scenarios performed and improved exam performance.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelation analysis between scenario frequency and summative exam performance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003cp\u003e(week 3 to 5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003e(week 6 to 9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003cp\u003e(week 10 to 12)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExam score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency of scenarios\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003cp\u003e(week 3 to 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180**\u003c/p\u003e\n \u003cp\u003e(p = 0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158*\u003c/p\u003e\n \u003cp\u003e(p = 0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197**\u003c/p\u003e\n \u003cp\u003e(p = 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003cp\u003e(p = 0.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003e(week 7 to 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p \u0026lt; 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e(p = 0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3\u003c/p\u003e\n \u003cp\u003e(week 10 to 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p \u0026lt; 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p \u0026lt; 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 4\u003c/p\u003e\n \u003cp\u003e(exam score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p \u0026lt; 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p \u0026lt; 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency of scenarios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003cp\u003e(p = 0.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e(p = 0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p = 0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(p \u0026lt; 0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNotes: Levels of statistical significance: * p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eGroup 1 – Week 3 to 6; Group 2 – Week 7 to 9; Group 3 – Week 10 to 12; Group 4 – Exam\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eSecondary outcomes:\u003c/h2\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e2.1 Correlation between practice session performance and summative exam outcomes\u003c/h2\u003e\n \u003cp\u003eTo evaluate performance progress from the beginning of the trimester through to the summative exam, practice session data were divided into four groups: the first three groups representing tertiles of weekly practice sessions (Group 1: Weeks 3 to 6, Group 2: Weeks 7 to 9, and Group 3: Weeks 10 to 12), and the fourth group representing the summative exam scores. A weaker, yet statistically significant positive correlation was observed between performance in the first two groups (Group 1 and Group 2) and exam outcomes (p \u0026lt; 0.05). The strength of this correlation improved notably when comparing performance in Group 3 to the exam (rs = 0.229, p \u0026lt; 0.001), suggesting that performance in the final weeks of the trimester is more closely associated with exam success (Table 2).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e2.2 Performance improvement over the trimester\u003c/h2\u003e\n \u003cp\u003eA paired samples t-test compared performance across the four intervals: Group 1 (Weeks 3 to 6), Group 2 (Weeks 7 to 9), Group 3 (Weeks 10 to 12), and the summative exam scores (Group 4). The analysis revealed statistically significant improvements in performance between each consecutive group (p \u0026lt; 0.05). These incremental gains in performance demonstrate a steady progression in clinical competence as students advanced through the trimester. The results suggest that frequent simulation-based practice throughout the trimester supports continuous improvement, culminating in enhanced performance during the summative exam. These findings are illustrated in Fig. 1 and detailed in Table 3, highlighting the effectiveness of consistent practice in developing clinical competence and readiness for performance-based assessments.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of paired sample t-test analysis comparison average score progression between Group 1, 2, 3 and the exam\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup score comparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePair 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 1 \u0026amp; Group 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.74 (13.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep \u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePair 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 2 \u0026amp; Group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.97 (11.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep = 0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePair 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3 \u0026amp; Group 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.15 (13.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep \u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePair 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup 3 and Group 4 (exam)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.30 (13.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep \u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: Levels of statistical significance: * p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.001\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eGroup 1 – Week 3 to 6; Group 2 – Week 7 to 9; Group 3 – Week 10 to 12; Group 4 – Exam\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003ePaired samples t-test correlation\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAbbreviations: CI, confidence interval; SD, standard deviation.\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003eTable 4 demonstrates the results of hierarchical multivariable regression analyses (the final model). The first model included gender and academic year (Block 1 variables). The second model added the mean scores of the three tertiles (Block 2 variables) while adjusting for Block 1 variables. The final model included frequency of practice in addition to the Block 1 and Block 2 variables. Each block of variables made a significant contribution to the model (p\u0026lt;0.001, F=6.02, p\u0026lt;0.001). The predictors in the final model collectively accounted for a significant proportion in explaining the variance in exam outcome. After controlling for the effects from the demographic factors (especially academic year as a significant confounder), as presented in Table 4, the mean score in Tertile 3 and frequency of practice were significant predictors for exam outcome (p values 0.023 and 0.006 respectively). The results suggested that the higher mean score in Tertile 3 and the more practice a student performed, the higher exam score. Collinearity checks confirmed no specific concern for multicollinearity among the continuous independent variables (all Torrence \u0026gt; 0.5 and VIF \u0026lt; 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Hierarchical multivariable regression: factors independently associated with the exam score (results of the final model)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI for B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eAcademic year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTertile 1 mean score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTertile 2 mean score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTertile 3 mean score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eFrequency of practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2299.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e250.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4349.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Levels of statistical significance: * P\u0026lt;0.05; ** P\u0026lt;0.01; *** P\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval\u0026nbsp;\u003c/p\u003e\n\n"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the impact of high-frequency simulation-based practice sessions on the development of clinical competence and exam performance in paramedicine students. Findings revealed a statistically significant association between the frequency of practice scenarios and summative exam performance, underscoring the benefits of structured, repetitive practice in improving clinical competence (44, 45). Moreover, data from 2020, when COVID-19 lockdowns limited the number of available practice scenarios, further emphasised the importance of consistent exposure. That year, with the lowest recorded scenario frequency, students also displayed one of the lowest exam performance scores, reinforcing the critical role of high-frequency practice in achieving clinical competence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimulation has become a cornerstone of healthcare education, allowing for repeated rehearsal of complex scenarios without patient risk (46). This approach supports students\u0026rsquo; progression through stages of competency, enabling them to develop both technical and decision-making skills in a structured environment (47). The incremental gains observed between each tertile, demonstrated by significant improvements in performance across Groups 1, 2, 3, and 4 (summative exam), align with Kolb\u0026rsquo;s experiential learning theory, where cycles of experience, reflection, and feedback contribute to deeper learning (23). By providing a feedback-rich environment, simulation encourages reflective practice and continual skill refinement, which are essential for achieving clinical competence (48).\u003c/p\u003e\n\u003cp\u003eThe findings suggest that while performance in the first two tertiles showed weaker yet significant correlations with exam outcomes, improved performance in the final tertile had a stronger correlation with exam success. This highlights the critical role of consistent performance in the final weeks of the trimester in achieving exam readiness. The results of the hierarchical regression further confirm that both mean performance in Tertile 3 and the frequency of practice were significant predictors of summative exam performance (p=0.023 and p=0.006, respectively). These results emphasise the importance of maintaining consistent practice, especially toward the end of the trimester, to achieve optimal outcomes.\u003c/p\u003e\n\u003cp\u003eThe association between practice frequency and improved exam performance aligns with findings in SBE, where deliberate, high-frequency exposure to simulation yields positive outcomes in skill acquisition and retention (2, 49). For paramedicine students, whose real-world clinical exposure is often limited, intentional practice with feedback is especially valuable, promoting readiness for practice and supporting students\u0026rsquo; progression toward competency (5). This research highlights the growing importance of high-frequency simulation, particularly in an era where some universities are reducing in-person teaching in favour of predominantly online degrees with minimal practical contact hours (50). While this approach may address institutional priorities such as scalability and cost efficiency, it raises significant concerns about the impact on students\u0026rsquo; preparedness for real-world paramedic practice. By embedding frequent simulation and feedback into curricula, institutions can help ensure students graduate with the necessary clinical competence and work readiness, regardless of delivery model (51).\u003c/p\u003e\n\u003cp\u003eAdditionally, the progressive improvement observed week-to-week aligns with Kirkpatrick\u0026rsquo;s framework of educational program evaluation, demonstrating that simulation significantly enhances participant satisfaction, knowledge acquisition, and clinical performance\u0026mdash;three key levels of this model (52). Although data on Kirkpatrick\u0026rsquo;s fourth level (patient outcomes) in paramedicine is limited, existing research in other health professions suggests that increased simulation exposure positively impacts clinical competence, which may ultimately translate to improved patient care (12, 53).\u003c/p\u003e\n\u003cp\u003eThis study may be the first of its kind in paramedicine to show that structured, high-frequency practice sessions combined with measured performance and intentional feedback can predictably improve clinical competence and summative exam outcomes. These findings contribute new evidence supporting simulation\u0026rsquo;s role as a powerful tool for competency-based assessment and skill development. The results underscore the importance of integrating consistent, structured simulation practice and feedback into paramedicine curricula to enhance clinical competence and readiness for real-world practice.\u003c/p\u003e\n\u003ch2\u003eFuture research implications and recommendations\u003c/h2\u003e\n\u003cp\u003eThis study provides valuable insights into the relationship between high-frequency simulation-based practice and clinical competence development. Future research should investigate whether performance trends during the trimester can predict exam pass/fail outcomes, as well as explore the variability in rater scores and its impact on assessment consistency and reliability. Extending this research across different courses, year levels, and multi-site settings could offer a broader understanding of simulation\u0026rsquo;s effectiveness in paramedicine education. Comparative analyses between institutions or programs may further validate the role of structured, non-graded practice sessions and help identify best practices. These insights can guide educators in refining simulation-based curricula to enhance student progression and clinical readiness.\u003c/p\u003e\n\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\n\u003cp\u003eStrengths of this study include its novel approach of capturing non-graded performance scores during weekly practice sessions, fostering a non-threatening learning environment while generating robust data to evaluate student progress. This study is one of the first in paramedicine education to demonstrate incremental improvements in clinical competence throughout a trimester, reflecting meaningful learning. The use of hierarchical multivariable statistical modelling provided a comprehensive analysis of the relationship between practice frequency and exam performance, with significant gains observed particularly in the final tertile. These findings offer practical insights for enhancing simulation-based curricula.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study include its focus on a single second-year cardiology course at one university, which may limit generalisability. Despite moderation efforts, variability in rater scoring could have influenced outcomes. Additionally, external factors such as student motivation, prior experience, or placement exposure were not accounted for. The COVID-19 disruption in 2020, which reduced practice opportunities, may have impacted students\u0026rsquo; ability to achieve competence comparable to other cohorts. Addressing these limitations in future research through multi-site studies, diverse course settings, and broader demographics could enhance the robustness of these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHigh-frequency simulation-based education with structured feedback significantly enhances clinical competence in paramedicine students at a single Australian University. This study\u0026apos;s novel approach of leveraging non-graded performance-based assessments during weekly practice sessions maintained a supportive learning environment while generating robust data for evaluating student progress. Analysis of six years of data demonstrated progressive performance improvements throughout the teaching period, with practice frequency and performance in the final weeks of the trimester correlating significantly with summative practical exam outcomes. These findings validate simulation as an effective tool for developing competence, reinforcing the importance of structured, repetitive practice in preparing students for performance-based assessments. Further research should explore the long-term impact on patient outcomes and the broader application of these strategies across different courses and institutions to enhance paramedicine education and clinical readiness.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.V. conceptualized the study, developed the Clinical Competence Assessment Tool (CCAT), collected, cleaned, and prepared the data, performed the preliminary analysis, interpreted the results, and drafted the manuscript. G.C. assisted with data cleaning, preparation, and preliminary analysis. P.L. designed the statistical modelling approach, performed advanced analyses, interpreted the results, and contributed critical revisions to the manuscript. M.B. supervised the project, guided data interpretation, and contributed critical revisions to the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge the contributions of the paramedicine students, sessional academics, and academic staff at Griffith University\u0026rsquo;s School of Medicine and Dentistry. Their participation, feedback, and commitment to simulation-based education were invaluable in the development and implementation of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEaton G, Wong G, Williams V, Roberts N, Mahtani KR. Contribution of paramedics in primary and urgent care: a systematic review. Br J Gen Pract. 2020;70(695):e421-e6.\u003c/li\u003e\n\u003cli\u003eZiv A, Wolpe PR, Small SD, Glick S. Simulation-based medical education: an ethical imperative. Acad Med. 2003;78(8):783-8.\u003c/li\u003e\n\u003cli\u003ePatel R, Sharma S. Credentialing. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Sandeep Sharma declares no relevant financial relationships with ineligible companies.2023.\u003c/li\u003e\n\u003cli\u003eBearman M, Greenhill J, Nestel D. The power of simulation: a large-scale narrative analysis of learners\u0026apos; experiences. Med Educ. 2019;53(4):369-79.\u003c/li\u003e\n\u003cli\u003ePalaganas JC, Fey M, Simon R. Structured Debriefing in Simulation-Based Education. AACN Adv Crit Care. 2016;27(1):78-85.\u003c/li\u003e\n\u003cli\u003eSawyer T, White M, Zaveri P, Chang T, Ades A, French H, et al. Learn, see, practice, prove, do, maintain: an evidence-based pedagogical framework for procedural skill training in medicine. Acad Med. 2015;90(8):1025-33.\u003c/li\u003e\n\u003cli\u003eKohn L. To err is human: an interview with the Institute of Medicine\u0026apos;s Linda Kohn. Jt Comm J Qual Improv. 2000;26(4):227-34.\u003c/li\u003e\n\u003cli\u003eFriedman PA, Wiener, E. L., Nagel, D. C., Carterette, E. C. Human Factors in Aviation. 1st ed. California: Academic Press; 1988 28 July 1988. 684 p.\u003c/li\u003e\n\u003cli\u003eLenchus JD. End of the \u0026quot;see one, do one, teach one\u0026quot; era: the next generation of invasive bedside procedural instruction. J Am Osteopath Assoc. 2010;110(6):340-6.\u003c/li\u003e\n\u003cli\u003eCheng A, Kessler D, Mackinnon R, Chang TP, Nadkarni VM, Hunt EA, et al. Reporting Guidelines for Health Care Simulation Research: Extensions to the CONSORT and STROBE Statements. Simul Healthc. 2016;11(4):238-48.\u003c/li\u003e\n\u003cli\u003eRosen KR. The history of medical simulation. J Crit Care. 2008;23(2):157-66.\u003c/li\u003e\n\u003cli\u003eCook DA, Hatala R, Brydges R, Zendejas B, Szostek JH, Wang AT, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011;306(9):978-88.\u003c/li\u003e\n\u003cli\u003eMcGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. A critical review of simulation-based medical education research: 2003-2009. Med Educ. 2010;44(1):50-63.\u003c/li\u003e\n\u003cli\u003eSchaefer JJ, 3rd, Vanderbilt AA, Cason CL, Bauman EB, Glavin RJ, Lee FW, et al. Literature review: instructional design and pedagogy science in healthcare simulation. Simul Healthc. 2011;6 Suppl:S30-41.\u003c/li\u003e\n\u003cli\u003eMcGaghie WC, Draycott TJ, Dunn WF, Lopez CM, Stefanidis D. Evaluating the impact of simulation on translational patient outcomes. Simul Healthc. 2011;6 Suppl(Suppl):S42-7.\u003c/li\u003e\n\u003cli\u003eZendejas B, Brydges R, Wang AT, Cook DA. Patient outcomes in simulation-based medical education: a systematic review. J Gen Intern Med. 2013;28(8):1078-89.\u003c/li\u003e\n\u003cli\u003eScholtz AK, Monachino AM, Nishisaki A, Nadkarni VM, Lengetti E. Central venous catheter dress rehearsals: translating simulation training to patient care and outcomes. Simul Healthc. 2013;8(5):341-9.\u003c/li\u003e\n\u003cli\u003eEricsson KA. Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med. 2004;79(10 Suppl):S70-81.\u003c/li\u003e\n\u003cli\u003evan Merri\u0026euml;nboer JJG, Sweller J. Cognitive load theory and complex learning:: Recent developments and future directions. Educ Psychol Rev. 2005;17(2):147-77.\u003c/li\u003e\n\u003cli\u003eMamede S, Schmidt HG. The structure of reflective practice in medicine. Med Educ. 2004;38(12):1302-8.\u003c/li\u003e\n\u003cli\u003eDieckmann P, Molin Friis S, Lippert A, Ostergaard D. The art and science of debriefing in simulation: Ideal and practice. Med Teach. 2009;31(7):e287-94.\u003c/li\u003e\n\u003cli\u003eFanning RM, Gaba DM. The role of debriefing in simulation-based learning. Simul Healthc. 2007;2(2):115-25.\u003c/li\u003e\n\u003cli\u003eAbulebda K, Auerbach M, Limaiem F. Debriefing Techniques Utilized in Medical Simulation. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Marc Auerbach declares no relevant financial relationships with ineligible companies. Disclosure: Faten Limaiem declares no relevant financial relationships with ineligible companies.2023.\u003c/li\u003e\n\u003cli\u003eSawyer T, Loren D, Halamek LP. Post-event debriefings during neonatal care: why are we not doing them, and how can we start? J Perinatol. 2016;36(6):415-9.\u003c/li\u003e\n\u003cli\u003eChmil JV, Turk M, Adamson K, Larew C. Effects of an Experiential Learning Simulation Design on Clinical Nursing Judgment Development. Nurse Educ. 2015;40(5):228-32.\u003c/li\u003e\n\u003cli\u003eGrantcharov TP, Kristiansen VB, Bendix J, Bardram L, Rosenberg J, Funch-Jensen P. Randomized clinical trial of virtual reality simulation for laparoscopic skills training. Br J Surg. 2004;91(2):146-50.\u003c/li\u003e\n\u003cli\u003eChaer RA, Derubertis BG, Lin SC, Bush HL, Karwowski JK, Birk D, et al. Simulation improves resident performance in catheter-based intervention: results of a randomized, controlled study. Ann Surg. 2006;244(3):343-52.\u003c/li\u003e\n\u003cli\u003eBanks E, Pardanani S, King M, Chudnoff S, Damus K, Freda MC. A surgical skills laboratory improves residents\u0026apos; knowledge and performance of episiotomy repair. Am J Obstet Gynecol. 2006;195(5):1463-7.\u003c/li\u003e\n\u003cli\u003eAhlberg G, Hultcrantz R, Jaramillo E, Lindblom A, Arvidsson D. Virtual reality colonoscopy simulation: a compulsory practice for the future colonoscopist? Endoscopy. 2005;37(12):1198-204.\u003c/li\u003e\n\u003cli\u003eAbelsson A, Bisholt B. Nurse students learning acute care by simulation - Focus on observation and debriefing. Nurse Educ Pract. 2017;24:6-13.\u003c/li\u003e\n\u003cli\u003eSaqe-Rockoff A, Ciardiello AV, Schubert FD. Low-Fidelity, In-Situ Pediatric Resuscitation Simulation Improves RN Competence and Self-Efficacy. J Emerg Nurs. 2019;45(5):538-44 e1.\u003c/li\u003e\n\u003cli\u003eBienstock J, Heuer A, Zhang Y. Simulation-Based Training and Its Use Amongst Practicing Paramedics and Emergency Medical Technicians: An Evidence-Based Systematic Review. International Journal of Paramedicine. 2022(1):12-28.\u003c/li\u003e\n\u003cli\u003eDiamond A, Bilton N. The Current State on the Use of Simulation in Paramedic Education. Australasian Journal of Paramedicine. 2021;18:1-5.\u003c/li\u003e\n\u003cli\u003eMahdavi Ardestani SF, Adibi S, Golshan A, Sadeghian P. Factors Influencing the Effectiveness of E-Learning in Healthcare: A Fuzzy ANP Study. Healthcare. 2023;11(14):2035.\u003c/li\u003e\n\u003cli\u003eFrenk J, Chen LC, Chandran L, Groff EOH, King R, Meleis A, et al. Challenges and opportunities for educating health professionals after the COVID-19 pandemic. Lancet. 2022;400(10362):1539-56.\u003c/li\u003e\n\u003cli\u003eAshburn NP, Hendley NW, Angi RM, Starnes AB, Nelson RD, McGinnis HD, et al. Prehospital Trauma Scene and Transport Times for Pediatric and Adult Patients. West J Emerg Med. 2020;21(2):455-62.\u003c/li\u003e\n\u003cli\u003eNagata I, Abe T, Nakata Y, Tamiya N. Factors related to prolonged on-scene time during ambulance transportation for critical emergency patients in a big city in Japan: a population-based observational study. BMJ Open. 2016;6(1):e009599.\u003c/li\u003e\n\u003cli\u003eGugiu MR, Cash R, Rivard M, Cotto J, Crowe RP, Panchal AR. Development and Validation of Content Domains for Paramedic Prehospital Performance Assessment: A Focus Group and Delphi Method Approach. Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors. 2021;25(2):196-204.\u003c/li\u003e\n\u003cli\u003eDreyfus SE, Dreyfus, H.L. A five-stage model of the mental activities involved in directed skill acquisition. In: Force USA, editor. Air Force Office of Scientific Research: University of California; 1980.\u003c/li\u003e\n\u003cli\u003eCarraccio CL, Benson BJ, Nixon LJ, Derstine PL. From the educational bench to the clinical bedside: translating the Dreyfus developmental model to the learning of clinical skills. Acad Med. 2008;83(8):761-7.\u003c/li\u003e\n\u003cli\u003eDaaleman TP. The medical home: locus of physician formation. J Am Board Fam Med. 2008;21(5):451-7.\u003c/li\u003e\n\u003cli\u003eBatalden P, Leach D, Swing S, Dreyfus H, Dreyfus S. General competencies and accreditation in graduate medical education. Health Aff (Millwood). 2002;21(5):103-11.\u003c/li\u003e\n\u003cli\u003eEdgar L, McLean, S., O Hogan, S., Hamstra, S., Holmboe, E. . The Milestones Guidebook - Accreditation Council for Graduate Medical Education (ACGME). 2020 ed2020. 38 p.\u003c/li\u003e\n\u003cli\u003eOkuda Y, Bryson EO, DeMaria S, Jr., Jacobson L, Quinones J, Shen B, et al. The utility of simulation in medical education: what is the evidence? Mt Sinai J Med. 2009;76(4):330-43.\u003c/li\u003e\n\u003cli\u003eIssenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005;27(1):10-28.\u003c/li\u003e\n\u003cli\u003eMotola I, Devine LA, Chung HS, Sullivan JE, Issenberg SB. Simulation in healthcare education: a best evidence practical guide. AMEE Guide No. 82. Med Teach. 2013;35(10):e1511-30.\u003c/li\u003e\n\u003cli\u003eDalseg TR, Thoma B, Wycliffe-Jones K, Frank JR, Taber S. Enabling Implementation of Competency Based Medical Education through an Outcomes-Focused Accreditation System. Perspect Med Educ. 2024;13(1):75-84.\u003c/li\u003e\n\u003cli\u003eRudolph JW, Simon R, Dufresne RL, Raemer DB. There\u0026apos;s no such thing as \u0026quot;nonjudgmental\u0026quot; debriefing: a theory and method for debriefing with good judgment. Simul Healthc. 2006;1(1):49-55.\u003c/li\u003e\n\u003cli\u003eMcGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med. 2011;86(6):706-11.\u003c/li\u003e\n\u003cli\u003eMcGee RG, Wark S, Mwangi F, Drovandi A, Alele F, Malau-Aduli BS, et al. Digital learning of clinical skills and its impact on medical students\u0026apos; academic performance: a systematic review. BMC Med Educ. 2024;24(1):1477.\u003c/li\u003e\n\u003cli\u003eMcKenna KD, Carhart E, Bercher D, Spain A, Todaro J, Freel J. Simulation Use in Paramedic Education Research (SUPER): A Descriptive Study. Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors. 2015;19(3):432-40.\u003c/li\u003e\n\u003cli\u003eKirkpatrick D, Kirkpatrick J. Evaluating Training Programs: The Four Levels: Berrett-Koehler Publishers; 2006.\u003c/li\u003e\n\u003cli\u003eEdelson DP, Litzinger B, Arora V, Walsh D, Kim S, Lauderdale DS, et al. Improving in-hospital cardiac arrest process and outcomes with performance debriefing. Arch Intern Med. 2008;168(10):1063-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5916090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5916090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eParamedics handle diverse medical and trauma cases, where any lapse in competency can compromise patient safety. Student paramedics must meet high standards set by oversight bodies to protect both the profession and the public. Growing evidence indicates that simulation facilitates attainment and reinforcement of clinical competencies. However, limited evidence explores the impact of simulation and debriefing on developing clinical competence in undergraduate paramedicine students.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the association between weekly practice session performance and summative practical exam outcomes, assessing how simulation-based practice supports the development of clinical competence among paramedicine students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis of performance-based assessment data (2019\u0026ndash;2024) in a university paramedicine program evaluated the role of high-frequency simulation. Second-year students participated in weekly three hour simulated practice sessions and a summative practical exam, with performance assessed using the Clinical Competence Assessment Tool (CCAT). Practice session data were grouped into tertiles to assess trends. Pearson\u0026rsquo;s correlation measured the association between practice frequency and exam outcomes, paired samples t-tests evaluated performance progression, and hierarchical regression identified significant predictors of exam outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eData from 305 students and 1,251 practice scenarios revealed a significant positive correlation between practice frequency and exam performance (rs\u0026thinsp;=\u0026thinsp;0.257, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Clinical competence scores improved progressively across the trimester, with the strongest correlation observed in the final tertile (rs\u0026thinsp;=\u0026thinsp;0.229, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Earlier tertiles showed weaker but statistically significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A performance dip in 2020 due to reduced simulation exposure during COVID-19 further highlighted the critical role of consistent, high-frequency practice in fostering competence and exam success.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrates the value of structured, high-frequency simulation and debriefing in enhancing clinical competence and exam performance. The findings underscore the importance of consistent practice, particularly in the final trimester weeks, supporting its integration into paramedicine curricula to ensure readiness for real-world practice and sustained competence.\u003c/p\u003e","manuscriptTitle":"The impact of simulation with debriefing on developing clinical competence and performance in paramedicine students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 10:27:05","doi":"10.21203/rs.3.rs-5916090/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-12T10:08:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-12T09:27:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-11T13:58:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-01-28T04:45:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa19ea5d-b00a-4669-b946-fd42a04f2aa8","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-14T10:27:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-14 10:27:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5916090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5916090","identity":"rs-5916090","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}