Integrating Patient and Public Involvement into Simulation- Based Learning for Adrenal Conditions: A Mixed-Methods Study

Integrating Patient and Public Involvement into Simulation- Based Learning for Adrenal Conditions: A Mixed-Methods Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating Patient and Public Involvement into Simulation- Based Learning for Adrenal Conditions: A Mixed-Methods Study Maiar Elhariry, Sangamithra Ravi, Rahul Sagu, Shubham Pareek, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6968791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Patient and Public Involvement and Engagement (PPIE) enhances patient-centred care but is inconsistently integrated into educational frameworks. Simulation-based learning (SBL) bridges the gap between theory and practice, promoting active learning. This study evaluated the impact of incorporating PPIE into SBL on healthcare professionals’ (HCPs) confidence, performance, and clinical practice in managing adrenal disorders. Methods: This study, conducted between October 2023 and March 2025, included two hybrid events of SBL with PPIE integration on adrenal conditions designed using Kern’s framework. Patients provided input by contributing lived experiences to enrich the realism of the SBL scenarios. The change between pre-and post-session confidence was analysed using the Wilcoxon rank-sum test. Qualitative feedback from healthcare professionals and patients was thematically analysed to explore the intervention’s impact. Participant performance in the simulation was assessed using the Global Rating Scale. Results: In total, 99 participants attended SIMBA sessions in 2024 and 2025. Confidence in managing adrenal disorders significantly improved post-session in both years (44.3% improvement in 2024 and 49.2% in 2025). Factors influencing performance included training level and WhatsApp engagement, although their impacts varied from year to year. Participants praised the model’s relevance, interactivity, and patient involvement, with feedback indicating intent to improve clinical practice and holistic care. 92.0% overall expressed a preference for this SBL-PPIE structure over traditional lecture-based methods. Patient feedback valued the empathy-driven insights shared. Conclusions: Integrating PPIE into SBL significantly improved HCPs’ confidence and supported a patient-centred approach. Patients also reported increased confidence in the quality of their care. Simulation Training Medical Education Professional Competence Patient-Centred Care SBL PPI. Figures Figure 1 Figure 2 Background While lecture-based learning (LBL) continues to serve as a foundational element within medical education, simulation-based learning (SBL) has emerged as a key pedagogical method, offering immersive and practical experiences that allow learners to acquire skills, engage in problem-solving, and comprehend complex concepts within a secure, realistic environment ( 1 , 2 ). Evidence suggests that SBL contributes positively to clinical performance, attitudes, and collaborative practice, ultimately enhancing patient care outcomes ( 3 ). Moreover, it has improved both the interpretation of clinical information and long-term knowledge retention in contrast to passive modalities such as recorded lectures ( 4 ). The evolution of educational technologies has further supported the transition of SBL into virtual domains, thereby addressing barriers related to accessibility, cost-effectiveness, and scalability ( 5 ). Simulation via Instant Messaging for Bedside Application (SIMBA) is a scenario-based e-simulation model that leverages WhatsApp to deliver clinical teaching ( 6 ). The SIMBA model is theoretically informed, drawing upon gaming simulation principles and Kolb’s experiential learning theory ( 7 ). Furthermore, it incorporates elements of interprofessional learning and operates with minimal resource requirements, having demonstrated its sustainability even amidst the constraints of the COVID-19 pandemic ( 8 ). Studies have affirmed SIMBA’s efficacy in producing sustained improvements in clinical knowledge and performance ( 7 ) Patient and Public Involvement and Engagement (PPIE) refers to the active collaboration between educators, researchers, and members of the public, such as patients, service users, or individuals with lived health experiences, in shaping the design, delivery, and dissemination of education or research ( 9 ). This model ensures that initiatives are developed with and by the public rather than being done to , for , or about them. Including people with lived experiences as active contributors in teaching and learning processes fosters greater transparency, accountability, and trust while providing healthcare professionals with richer perspectives on patient needs and lived experiences ( 10 ). This study, therefore, aims to evaluate the effect of incorporating PPIE into interactive SBL sessions using the SIMBA model on participants’ clinical confidence and performance in the context of adrenal disorder management. Additionally, the study examines the feasibility and acceptability of this combined SBL-PPI model, aiming to identify both enablers and challenges to meaningful patient involvement in simulation-based education. Methods This study was conducted in Birmingham, UK, between June 2023 and March 2025. The study was conducted as part of a wider SIMBA evaluation study, approved by the University of Birmingham Ethics Committee (Ref: ERN_2023 − 0544). A core team was created to organise and deliver the SBL-PPI session. This included two clinical experts in adrenal conditions, one PPIE expert, and an expert in applied and simulation-based learning. The team then recruited an early career researcher interested in applied learning and implementation science to create an extended team to deliver the simulation sessions. The extended team was primarily made up of early-career researchers. A Gantt chart was created to plan the organisation and delivery of the sessions, and bi-monthly meetings were organised between a member of the core team and the extended team to ensure progress as per the Gantt chart. All healthcare professionals interested in adrenal conditions were invited to participate in the simulation sessions. Patients diagnosed with adrenal conditions simulated in the sessions were also invited to participate in the study. All participants completed an informed consent to participate in the study. Designing Simulation Two simulation events on adrenal disorders were delivered in February 2024 and February 2025, respectively. Each two-day hybrid simulation-based learning event was designed using Kern’s Curriculum development framework ( 6 ). The events featured nine case-based simulations (Primary Adrenal Insufficiency, Congenital Adrenal Hyperplasia (CAH), Adrenal Incidentaloma, Adrenocortical Carcinoma (ACC), Mild Autonomous Cortisol Secretion, Cushing's Syndrome, Primary Aldosteronism, Bilateral Adrenal Hyperplasia, and Phaeochromocytoma). The topics for the simulations were selected based on general needs and a targeted needs assessment through feedback from prior SIMBA sessions, a literature search, and expert inputs. Detailed methodologies for preparing and delivering the event have been previously published ( 6 , 7 ). Cases were reviewed by clinical experts specialising in respective adrenal diseases to ensure scientific accuracy and experts by experience to enhance realism and alignment with patient experiences, as detailed below. Participants received information about the session structure and joining instructions via email and Twitter. Integrating PPIE with SIMBA To integrate PPIE into the SBL, we invited people with lived experiences whose conditions featured in the case scenarios to a virtual workshop. These people with lived experiences were recruited through relevant UK charities. An early career researcher from the extended team coordinated the workshops. These workshops were conducted two to four weeks prior to each simulation event and were recorded with the participants’ consent. The case scenarios to be used in the simulation were shared with individuals who have lived experience during the workshop. They were then asked to share their comments on suggestions on three areas: what was done well in the case scenario, what could be done better, and the real-life relevance of the cases, specifically how closely the depicted patient journeys aligned with their own experiences. The early career researcher who coordinated the workshop transcribed the recording verbatim and conducted descriptive analysis to identify common themes discussed. These themes were shared with participants who attended the workshop to ensure they accurately reflected their perspectives. During the events, a nominated person with a lived experience representative presented these themes and gave an insight into their personal experience after the simulation alongside experts' discussions of the cases. Session Delivery The events were open to participants both virtually and in person. To enhance accessibility, attendees from low- and middle-income countries (LMICs) were offered free participation. Both events had the same structure and flow. Day one began with a welcome address from a member of the core team, outlining the objectives and programme for participants. This was followed by pre-event survey, assessing participants’ confidence in the topics to be simulated [see Additional file 1, Additional file 2]. Then the simulation session started, with each case scenario allocated approximately 25 minutes. A brief technical clarification period was included after case one to ensure participants were comfortable with the simulation format and tools. Following the simulation, the afternoon session focused on in-depth discussions of the cases presented. Each case was discussed by an expert in the field. This was complemented by presentations from patient representatives, who emphasised the importance of incorporating patient perspectives into professional discourse and underscored the commitment to holistic understanding and patient-centred care. The sessions emphasised evidence-based medicine (EBM) by referencing the latest guidelines and included dedicated sessions discussing the latest scientific updates in the field. Participants were encouraged to engage in discussions and reflect on their experiences to foster active learning. To facilitate engagement and learning, the programme was structured with regular comfort breaks, acknowledging the intense nature of the content and providing participants time to reflect and recharge. The events concluded with a feedback session and a post-event survey [see Additional file 3, Additional file 4]. Efficacy Assessment and Analysis Moderators downloaded the transcript of WhatsApp chats from the simulation and objectively assessed participant performance using the Global Rating Scale (GRS), which was approved by an expert in the respective case. There were up to nine measures for each competency per participant, and the mean value of these measures was calculated for each participant and used in the analysis. Continuous data were assessed for normality using the Shapiro-Wilk test. Mean values and standard deviation (SD) were calculated for each of the measures of competencies by certain demographic variables, including sex, country (UK, non-UK), level of training (junior, middle, senior, out of programme), and number of WhatsApp messages exchanged with moderators per case (< 50, ≥ 50). The difference in means for each of the four demographic variables was assessed using the t-test for individual samples and analysis of variance (ANOVA). Differences in self-reported confidence pre- and post-SIMBA sessions were evaluated using the Wilcoxon rank-sum test. To further explore factors influencing participants' learning outcomes, additional analyses were conducted. Multiple linear regression analyses were conducted to assess predictors of participant performance across six domains of the GRS. Predictors included year (2024, 2025), sex, country of training, training level, and WhatsApp usage per case. For each domain (history, physical examination, interpretation, investigations, clinical judgement, management), odds ratios (OR) and 95% confidence intervals (CIs) were calculated from the regression coefficients for interpretability. Additionally, ordinal logistic regression was used to evaluate predictors of self-reported confidence levels, with variables including session day, country, prior SIMBA participation, sex, training level, timepoint, and year. ORs, 95% CIs, and p-values were reported to identify statistically significant predictors. Data were analysed using SPSS software version 29.0.1.1. All tests proceeded as two-tailed, and p-values < 0.05 were considered statistically significant. The post-SIMBA survey also collected general feedback on the session, including assessments of session quality and applicability to their respective practices. Material collected from the open-ended questions of the post-SIMBA evaluation questionnaire was reviewed and combined in a thematic analysis. Using an inductive thematic analysis approach, data was analysed for main ideas and reduced to common themes of feedback. Patient feedback was gathered separately through the scheduled workshops with early career researchers, focusing on their views on the cases simulated, and informal discussions, focusing on general event feedback and their interest in future involvement in similar opportunities to enhance medical education through contributions based on lived experiences. A descriptive analysis of their response was conducted to summarise patient feedback, focusing on capturing key points and overall impressions rather than exploring deeper patterns. This approach provided a straightforward overview of the limited patient perspectives, aligning with the goal of highlighting general themes related to patient experiences in a timely manner. Results Participant characteristics 99 participants completed both pre- and post-simulation surveys and were included in the analysis: 40 participants in 2024 and 59 in 2025. The 2024 cohort was predominantly female (72.5%) and mostly UK-based (82.5%). In comparison, the 2025 cohort had a more balanced gender distribution (52.5% men, 47.5% women), but a higher UK representation (96.6%). Across both years, there was a consistent proportion of returning participants, reflecting sustained engagement with the SIMBA model. In 2024, 37.9% on Day 1 and 57.1% on Day 2 were returning attendees. In 2025, similar patterns were observed, with 37.0% on Day 1 and 43.2% on Day 2 having attended previous SIMBA sessions. Participants performance in the simulation sessions Participants demonstrated strong performance across competencies in both years, with slightly higher overall scores in 2024. The highest scores in 2024 were observed in requesting appropriate physical examination (mean ± SD: 4.02 ± 0.83) and clinical judgement (4.12 ± 0.65), while interpretation of imaging and management/discharge planning scored lower (3.63 ± 0.79 and 3.67 ± 0.75, respectively). In 2025, a similar pattern emerged, though scores for interpretation of imaging (3.63 ± 0.79) and management/discharge planning (3.26 ± 0.87) were lower than in the previous year ( Fig. 1 ) . In the 2024 session, no statistically significant differences were observed between men and women across the assessed competencies. In 2025, there were also no statistically significant differences between the two groups, except in history taking (men vs women: 3.98 ± 0.63 vs. 3.54 ± 0.84, p = 0.028)(Table 1 ) . Participant performance varied by level of training in both years. In 2024, out-of-programme participants achieved the highest scores in the interpretation of investigations (4.94 ± 0.08). In 2025, a similar trend was observed, with senior trainees outperforming junior and middle-grade peers, particularly in the interpretation of investigations (seniors vs juniors: 3.19 ± 0.72 vs 2.12 ± 0.96, p = 0.011). While other domains showed minor differences by training level, these did not reach statistical significance ( Table 1 ) . While UK participants generally scored slightly higher, there were no significant differences in performance between UK-based and non-UK participants in either year ( Table 1 ) . Participant engagement, as measured by the number of WhatsApp messages exchanged with moderators per case during the simulation, had differing impacts across the two sessions. In 2024, participants who sent 50 or more messages per case scored higher across all domains, with significantly better scores in management and discharge planning (mean score 3.88 ± 0.59 vs. 3.42 ± 0.86, p = 0.05). In contrast, in 2025, higher engagement did not translate to improved scores. Notably, interpretation scores were lower among high-engagement participants (mean score 2.36 ± 0.8 vs. 3.06 ± 0.85, p = 0.044), and no other domains showed significant differences ( Table 1 ) . Table 1 Comparison of Global Rating Scale (GRS) scores by sex, training levels, country of practice, and level of engagement across clinical competencies. Comparison of GRS scores by sex across clinical competencies Competency 2024 2025 Men (N = 11) Women (N = 29) p-value Men (N = 31) Women (N = 28) p-value History 3.59 ± 1.03 4.10 ± .68 0.073 3.98 ± 0.63 3.54 ± 0.84 0.028 Examination 3.88 ± 0.69 4.06 ± 0.88 0.547 4.03 ± 0.65 3.63 ± 0.97 0.071 Investigations 4.20 ± 0.64 3.82 ± 0.84 0.178 3.92 ± 0.59 3.73 ± 0.69 0.250 Interpretation 3.94 ± 0.68 3.52 ± 0.81 0.134 3.10 ± 0.79 2.84 ± 0.94 0.240 Judgement 4.21 ± 0.72 4.09 ± 0.63 0.623 3.54 ± 0.77 3.35 ± 0.89 0.380 Management 3.92 ± 0.57 3.58 ± 0.80 0.202 3.25 ± 0.85 3.27 ± 0.91 0.924 Comparison of GRS scores by training level across clinical competencies. Competency 2024 2025 Junior (N = 14) Middle (N = 11) Senior (N = 13) Out of programme (N = 2) p-value Junior (N = 8) Middle (N = 14) Senior (N = 36) Out of programme (N = 1) p-value History 3.82 ± 0.78 4.33 ± 0.66 3.88 ± 0.83 3.46 ± 1.71 0.315 3.22 ± 0.74 3.72 ± 0.73 3.87 ± 0.72 - 0.055 Examination 4.08 ± 0.74 4.26 ± 0.61 3.71 ± 1.04 4.19 ± 0.98 0.426 3.33 ± 1.01 3.86 ± 0.78 3.91 ± 0.79 - 0.179 Investigations 3.83 ± 0.78 3.93 ± 0.58 3.87 ± 0.98 4.89 ± 0.16 0.376 3.52 ± 0.57 3.81 ± 0.68 3.87 ± 0.62 - 0.146 Interpretation 3.46 ± 0.87 3.39 ± 0.79 3.82 ± 0.54 4.94 ± 0.08 0.042 2.12 ± 0.96 2.87 ± 0.93 3.19 ± 0.72 - 0.011 Judgement 3.93 ± 0.69 3.98 ± 0.79 4.34 ± 0.36 4.83 ± 0.24 0.136 2.81 ± 0.99 3.44 ± 0.89 3.56 ± 0.72 - 0.093 Management 3.59 ± 0.68 3.55 ± 0.92 3.71 ± 0.67 4.66 ± 0.13 0.283 2.92 ± 0.76 2.99 ± 0.95 3.41 ± 0.84 - 0.217 Comparison of GRS scores by country of practice across clinical competencies. Competency 2024 2025 UK (N = 33) Non-UK (N = 7) p-value UK (N = 57) Non-UK (N = 2) p-value History 4.02 ± 0.80 3.71 ± 0.85 0.372 3.78 ± 0.77 3.33 ± 0.47 0.417 Examination 4.04 ± 0.85 3.90 ± 0.81 0.693 3.85 ± 0.85 3.43 ± 0.46 0.482 Investigations 3.97 ± 0.80 3.71 ± 0.80 0.441 3.84 ± 0.64 3.45 ± 0.78 0.397 Interpretation 3.72 ± 0.77 3.21 ± 0.83 0.123 2.98 ± 0.87 2.97 ± 1.22 0.986 Judgement 4.18 ± 0.59 3.84 ± 0.90 0.213 3.43 ± 0.84 3.77 ± 0.33 0.582 Management 3.73 ± 0.70 3.39 ± 0.98 0.294 3.27 ± 0.88 3.14 ± 0.86 0.845 Comparison of GRS scores by level of engagement in WhatsApp-based discussion across clinical competencies. Competency 2024 2025 < 50 WhatsApp messages per case (N = 18) ≥ 50 WhatsApp messages per case (N = 22) p-value < 50 WhatsApp messages per case (N = 52) ≥ 50 WhatsApp messages per case (N = 7) p-value History 3.87 ± 0.89 4.04 ± 0.75 0.499 3.74 ± 0.72 3.94 ± 1.07 0.535 Examination 3.86 ± 0.87 4.14 ± 0.79 0.304 3.83 ± 0.84 3.88 ± 0.86 0.900 Investigations 3.90 ± 0.79 3.94 ± 0.82 0.879 3.82 ± 0.62 3.87 ± 0.84 0.855 Interpretation 3.38 ± 0.94 3.84 ± 0.59 0.068 3.06 ± 0.85 2.36 ± 0.80 0.044 Judgement 3.92 ± 0.79 4.29 ± 0.47 0.079 3.47 ± 0.84 3.28 ± 0.79 0.568 Management 3.42 ± 0.86 3.88 ± 0.59 0.05 3.29 ± 0.87 3.02 ± 0.86 0.434 Scores are presented as mean ± standard deviation. Multiple linear regression analyses were conducted to assess the influence of year, sex, country of training, level of training, and WhatsApp usage on each GRS domain score. Across the six domains, predictors showed varying levels of influence. The year of participation was a significant predictor of performance in the clinical judgment domain, with participants in 2025 scoring better than those in 2024 (OR 0.50, 95% CI 0.32–0.76, p = 0.002). WhatsApp usage of 50 or more messages per case was associated with significantly greater odds of higher performance in the management domain (OR 1.54, 95% CI 1.03–2.31, p = 0.036). WhatsApp use also showed marginal associations with the interpretation domain (OR 1.44, 95% CI 0.97–2.14, p = 0.073). Other predictors, including sex, country, and training level, did not significantly predict GRS performance across the various GRS domains. ( Table 2 ) Table 2 Linear Regression Results for Global Rating Scale (GRS) Domain Scores. History Examination Investigations Interpretation Clinical Judgement Management aOR p-value 95% CI aOR p-value 95% CI aOR p-value 95% CI aOR p-value 95% CI aOR p-value 95% CI aOR p-value 95% CI Year 1.2 0.499 0.7–2.06 1.35 0.292 0.77–2.38 1.01 0.986 0.59–1.7 0.69 0.12 0.43–1.1 0.5 0.002 0.32–0.76 0.65 0.076 0.4–1.05 Sex 1.23 0.371 0.78–1.95 0.95 0.842 0.59–1.55 0.68 0.093 0.44–1.07 0.73 0.128 0.49–1.1 0.88 0.479 0.61–1.27 0.84 0.394 0.56–1.26 Country 0.74 0.379 0.38–1.45 0.84 0.610 0.41–1.69 0.74 0.364 0.39–1.42 0.59 0.074 0.33–1.05 0.71 0.197 0.42–1.2 0.69 0.217 0.38–1.25 Training level 1.07 0.584 0.84–1.37 0.86 0.236 0.66–1.11 0.85 0.165 0.67–1.07 0.98 0.826 0.79–1.21 1.01 0.892 0.83–1.23 0.89 0.278 0.72–1.1 No of WhatsApp messages per case 1.28 0.287 0.81–2.02 1.48 0.106 0.92–2.4 0.98 0.927 0.63–1.53 1.44 0.073 0.97–2.14 1.32 0.128 0.92–1.9 1.54 0.036 1.03–2.31 aOR, adjusted Odds Ratio; CI, Confidence Interval. Impact of Simulation on participant’s confidence Levels Both sessions led to substantial improvements in participants’ self-reported confidence in managing adrenal disorders (pre- vs post-session: 2024, 45.4% vs 89.7%, p < 0.001; 2025, 39.7% vs 88.9%, p < 0.001). The most pronounced increases were noted in congenital adrenal hyperplasia (+ 65.6%, p < 0.001), adrenocortical carcinoma (+ 55.2%, p < 0.001), and bilateral adrenal hyperplasia (+ 54.3%, p < 0.001) in 2024 session and primary bilateral macronodular adrenal hyperplasia (+ 65.9%, p < 0.001), congenital adrenal hyperplasia (+ 65.2%, p < 0.001), and phaeochromocytoma (+ 52.1%, p < 0.001) in 2025 ( Table 3 ) . Across both years, all observed changes in confidence highlight the effectiveness of the SIMBA model in enhancing clinical self-efficacy. Table 3 Change in self-reported confidence across adrenal cases before and after SIMBA simulation for the 2024 and 2025 sessions. Year Case Confident (%) Unsure (%) Not confident (%) p-value 2024 Primary Adrenal Insufficiency Pre-SIMBA 55.2 37.9 6.9 < 0.001 Post-SIMBA 93.1 6.9 0.0 Difference + 37.9% -31% -6.9% Congenital Adrenal Hyperplasia Pre-SIMBA 24.1 65.5 10.3 < 0.001 Post-SIMBA 89.7 10.3 0.0 Difference + 65.6% -55.2% -10.3% Adrenal Incidentaloma Pre-SIMBA 62.1 34.5 3.4 0.003 Post-SIMBA 89.7 10.3 0.0 Difference + 27.6% -24.2% -3.4% Adrenocortical Carcinoma Pre-SIMBA 27.6 62.1 10.3 < 0.001 Post-SIMBA 82.8 17.2 0.0 Difference + 55.2% -44.9% -10.3% Mild Autonomous Cortisol Secretion Pre-SIMBA 48.6 45.7 5.7 < 0.001 Post-SIMBA 91.4 8.6 0.0 Difference + 42.8% -37.1% -5.7% Primary Hyperaldosteronism Pre-SIMBA 51.4 45.7 2.9 0.002 Post-SIMBA 88.6 8.6 2.9 Difference + 37.2% -37.1% 0% Bilateral Adrenal Hyperplasia Pre-SIMBA 37.1 60.0 2.9 < 0.001 Post-SIMBA 91.4 5.7 2.9 Difference + 54.3% -54.3% 0% Cushing's Syndrome Pre-SIMBA 48.6 48.6 2.9 < 0.001 Post-SIMBA 91.4 8.6 0.0 Difference + 42.8% -40% -2.9% Phaeochromocytoma Pre-SIMBA 51.4 45.7 2.9 < 0.001 Post-SIMBA 88.6 8.6 2.9 Difference + 37.2% -37.1% 0% Overall 2024 Pre-SIMBA 45.4 49.5 5.2 < 0.001 Post-SIMBA 89.7 9.3 1.0 Difference + 44.3% -40.2% -4.2% 2025 Congenital Adrenal Hyperplasia Pre-SIMBA 26.1 60.9 13.0 < 0.001 Post-SIMBA 91.3 8.7 0.0 Difference + 65.2% -52.2% -13% Adrenal Incidentaloma Pre-SIMBA 56.5 32.6 10.9 < 0.001 Post-SIMBA 87.0 10.9 2.2 Difference + 30.5% -21.7% -8.7% Adrenocortical Carcinoma Pre-SIMBA 30.4 50.0 19.6 < 0.001 Post-SIMBA 76.1 21.7 2.2 Difference + 45.7% -28.3% -17.4% Phaeochromocytoma Pre-SIMBA 37.0 50.0 13.0 < 0.001 Post-SIMBA 89.1 8.7 2.2 Difference + 52.1% -41.3% -10.8% Mild Autonomous Cortisol Secretion Pre-SIMBA 47.7 47.7 4.5 < 0.001 Post-SIMBA 93.2 6.8 0.0 Difference + 45.5% -40.9% -4.5% Cushing's Syndrome Pre-SIMBA 47.7 52.3 0.0 < 0.001 Post-SIMBA 95.5 4.5 0.0 Difference + 47.8% -47.8% 0% Primary bilateral macronodular adrenal hyperplasia Pre-SIMBA 22.7 65.9 11.4 < 0.001 Post-SIMBA 88.6 11.4 0.0 Difference + 65.9% -54.5% -11.4% Primary Hyperaldosteronism Pre-SIMBA 50.0 47.7 2.3 < 0.001 Post-SIMBA 90.9 9.1 0.0 Difference + 40.9% -38.6% -2.3% Overall 2025 Pre-SIMBA 39.7 50.8 9.4 < 0.001 Post-SIMBA 88.9 10.3 0.8 Difference + 49.2% -40.5% -8.6% Values are presented as percentages (%). Ordinal logistic regression was conducted to further assess the influence of session day, country of training, prior SIMBA participation, sex, training level, timepoint, and year on self-reported confidence levels. Sex, training level, and timepoint were significant predictors. Male participants had higher odds of reporting increased confidence compared to females (OR 1.44, 95% CI 1.07–1.93, p = 0.015). Junior (OR 0.27, 95% CI 0.16–0.43, p < 0.001) and middle-level trainees (OR 0.37, 95% CI 0.27–0.51, p < 0.001) had significantly lower odds of being more confident compared to senior trainees. Year of participation, session day, country of training, and first-time SIMBA participation were not statistically significant predictors of self-reported confidence. ( Table 4 ). Table 4 Ordinal logistic regression predicting confidence rating following SIMBA adrenal sessions. Predictor aOR p-value 95% CI Year 2024 2025 (ref) 1.329 1 0.062 0.986–1.790 Sex Male Female (ref) 1.438 1 0.015 1.072–1.929 Timepoint Pre-SIMBA Post-SIMBA (ref) 0.068 1 < 0.001 0.049–0.094 Training level Out of Programme Junior Middle Senior (ref) 0.822 0.269 0.367 1 0.615 < 0.001 < 0.001 0.382–1.153 0.157–0.425 0.269–0.506 Country UK Non-UK (ref) 0.724 1 0.188 0.448–1.171 Day Day 1 Day 2 (ref) 0.873 1 0.335 0.662–1.151 1st Time Attending Yes No (ref) 0.978 1 0.873 0.738–1.294 aOR, adjusted Odds Ratio; CI, Confidence Interval. Impact on clinical practice and patient care. In 2024, participants’ comments on the impact of the session on their care yielded two key themes (Fig. 2 A). Recurring themes include Referral and Multidisciplinary team approach, as well as clinical knowledge. As a result of the session, multiple participants intended to change their practice and refer more often to patient support and self-help groups. Moreover, comments under the theme of specific clinical practice knowledge included: "never delay management of Cushing's" and "I will be more aware of rare diagnoses and more likely to test”. In 2025, participants’ comments on the impact of the session on their care yielded the same two themes and one new key theme: “holistic care” (Fig. 2 B). Comments under this theme included: “continue to listen and take patient all patient communication into consideration without prejudice” and “consider patient’s socioeconomic status more carefully with their capacity to engage with medical services”. Patients from both events also spoke about the benefits they received during the structured interviews. This focused on the impact this approach had on giving HCPs insight into the burden of their respective conditions, combined with both positive and negative encounters with HCPs, and how this is critical for personalised and patient-focused care. This also highlighted potential differences in priorities between HCPs and patients in terms of management. Patients identified PPI as a helpful aid to improve patient care and management positively, increasing their confidence in HCPS. Feasibility and acceptability of the SIMBA-PPIE model Participant feedback from both years consistently demonstrated high satisfaction with the SIMBA-PPIE model (participants who agreed or strongly agreed that the simulated topics were relevant to their clinical practice: 2024, 96.5% on day 1 and 97.1% on day 2; 2025, 97.8% on day 1 and 95.4% on day 2). All participants felt that the SIMBA-PPIE Adrenal session improved their competencies. The perceived quality of the sessions remained excellent or good amongst most respondents: 100% in 2024 and 97.7% in 2025. Participants highlighted the engaging format, the relevance of the cases, and the inclusion of patient perspectives as key strengths. Most (over 90% across all sessions) felt that the content was impactful both personally and professionally. The SIMBA model was widely preferred over traditional lecture-based methods, with 92.0% of participants overall expressing a preference for SIMBA-style sessions over LBL. Furthermore, nearly all participants expressed a strong intention to attend future sessions (2024: 100% Day 1, 88.6% Day 2; 2025: 89.1% Day 1, 97.7% Day 2). Participants also expressed intentions to implement the discussed guideline recommendations and adopt a more holistic approach into clinical practice. Suggestions for improvement primarily focused on logistical aspects such as session timing and moderator interaction. There was also a strong interest in expanding SIMBA to cover additional endocrine topics, including reproductive endocrinology, pituitary disorders, and thyroid disease, indicating enthusiasm for broader application of the model and a demand for further educational opportunities. Discussion The first-of-its-kind integrated PPIE and SBL model enhanced HCPs’ confidence in managing adrenal conditions while also benefiting patients by empowering them to share insights that tailor care to their respective needs more effectively. These findings align with previous research supporting the value of PPIE and SBL, as well as other forms of applied learning, in improving clinician confidence and competence( 2 , 10 – 13 ). The model demonstrates a transferable impact across diverse participant demographics—particularly sex, country of training, and level of training ( 5 , 14 ). The positive effects of the level of engagement with moderators during simulated cases (reflected by the number of WhatsApp messages exchanged) and previous participation in SIMBA simulations reinforce the model’s effectiveness in enhancing assessed competencies, suggesting that greater interaction with the model not only leads to improved outcomes in the current simulation but may also strengthen future performance by fostering continued familiarity and deeper learning. Participants rated the sessions highly for quality and engagement, expressing a strong preference for SIMBA over traditional lecture-based learning. This study provides key insights into the effectiveness of simulation-based learning in endocrinology, aligning with existing literature that emphasises its role in bridging theoretical knowledge and clinical practice ( 1 – 4 ). By incorporating PPIE, the present study demonstrates a novel approach to SBL that provides participants with a holistic perspective on patient care, an area often overlooked in traditional education models ( 8 – 11 ). The novel inclusion of perspectives from people with lived experiences offered additional benefits, emphasising the alignment of clinical management with patient needs and expectations ( 12 , 15 , 16 ). Such integration of lived experiences into educational frameworks has been shown to foster empathy, improve communication skills, and enhance the delivery of personalised care, as highlighted in previous studies( 10 , 11 , 15 , 17 ). Patient feedback underscored the positive impact of this approach on their confidence in healthcare providers. By highlighting discrepancies between clinical priorities and patient expectations, PPIE also identified areas where clinician training could be improved. These insights underscore the importance of adopting a collaborative approach to medical education, a strategy that has been previously advocated to enhance transparency, accountability, and trust in healthcare delivery ( 9 , 16 ) The relatively small sample size may limit the generalizability of the findings. Nevertheless, the reproducibility of the results one year apart demonstrates the strength of our findings. While participants expressed intentions to adopt session learnings into clinical practice, the study did not objectively assess whether these changes were implemented or resulted in improved patient outcomes, highlighting an area for future research. Conclusions This study demonstrates that integrating PPIE into SBL has a positive impact on both participants and individuals with lived experiences, promoting a more holistic and patient-centred approach to healthcare. Further studies are needed to objectively assess the impact on clinical practice and patient care. Declarations Ethics Requirement The study was conducted as part of a wider SIMBA evaluation study, approved by the University of Birmingham Ethics Committee (Ref: ERN_2023-0544). Clinical trial number: not applicable. Consent for Participation This study was conducted in accordance with the Declaration of Helsinki. All participants provided informed consent for inclusion in the study. This included consent to complete anonymised surveys and, where applicable, to be interviewed, recorded, and re-consented following transcript review. Consent for Publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article [and its supplementary information files]. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the meeting grants by the Society for Endocrinology, UK and Health Education West Midlands and unrestricted educational grants from Recordati, Esteve and Lund Pharmaceuticals. None of these organisations had any input on the design or delivery of the events included in this study. PK receives support from the National Institute for Health and Care Research (NIHR) through his Advanced Clinician Scientist Fellowship (fellowship reference number: NIHR303671). Additionally, PK is supported by the Midlands Patient Safety Research Collaboration (PSRC) and the NIHR-supported Race, Equity, and Diversity in Careers Incubator. Alessandro Prete and Caroline Gillett receive support from the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham (grant reference number NIHR203326). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care UK. Author contributions ME is the first author, having made all-around contributions to the study. ME contributed to the study conception, supervised executive aspects of the project, and wrote the first draft. SR and RS led the delivery of the session in 2024 and they mentored SP to deliver the session in 2025. AS, AM and FC contributed to data curation, quantitative result analysis and fine-tuning of the research methods. CG recruited people with lived experiences and relevant patient groups and contributed to the development of the methodology. AP and CR coordinated project planning and execution, recruiting relevant experts for the session, contributed to fine-tuning the research methods, writing the first draft, and supervised executive aspects of the project. PK is the senior author who conceptualised the study, supervised all aspects of the project, finalised the research methods and critically reviewed and revised the manuscript. All authors contributed substantially to drafting and approving the final draft of the manuscript. Acknowledgements We thank the healthcare professionals who participated in this study. We thank the University of Birmingham Medical School students who participated as moderators in this study. We also thank the Health Education West Midlands Diabetes and Endocrinology Specialist Trainee Committee, Department of Metabolism and Systems Science, University of Birmingham, and Department of Applied Health Sciences, University of Birmingham, for supporting this study. Thanks to all our patients and patient groups, including Mr Dominic Hargreaves, who represented Addison's Disease Self Help Group (ADSHG), Ms Jo Grey, who represented ACC Support UK and the Association for Multiple Endocrine Neoplasia Disorders (AMEND), and Ms Caroline Brown, who represented Pituitary Foundation. We thank Helen Simpson, Helena Gleeson, John Newell Price, Yasir S. Elhassan, Martin Fassnacht, Ljiljana Marina, Florian Wernig, Xilin Wu, Lucas Bouys and John Ayuk for their support in delivering the simulated scenarios. The DEVI Collaboration includes the following members: Kashish Malhorta, Dengyi Zhou, Pavithra Sakithevel, Harshin Balakrishnan, Amynta Arshad, Anisah Ali, Haaziq Shiekh, Hanish Johal, Harshin Balakrishnan, Josh Banerjee, Khushi Kumar, Mukunth Kowshik, Pranav Viswanath Iyer, Shams Ali Baig, Shivam Choudry, Swetha Palanichamy, Syeda Sabba Batul, Vardhan Venkatesh, and Letícia Santiago. All members of the DEVI collaboration were involved in delivery of the SBL sessions, critically reviewed and contributed to the revision of the manuscript. Therefore, they all are included as collaborating authors. References Arteaga E, Biesbroek R, Nalau J, Howes M. Across the Great Divide: A Systematic Literature Review to Address the Gap Between Theory and Practice. Sage Open. 2024;14(1). Tayade MC, Giri PA, Latti RG. Effectiveness of early clinical exposure in improving attitude and professional skills of medical students in current Indian medical education set up. Journal of Family Medicine and Primary Care. 2021;10(2):681-5. So HY, Chen PP, Wong GKC, Chan TTN. Simulation in medical education. Journal of the Royal College of Physicians of Edinburgh. 2019;49(1):52-7. Zhao H, Xiong J, Zhang Z, Qi C. Growth Mindset and College Students’ Learning Engagement During the COVID-19 Pandemic: A Serial Mediation Model. Frontiers in Psychology. 2021;12:621094-. Bahattab A, Caviglia M, Martini D, Hubloue I, Corte FD, Ragazzoni L. Scenario-Based e-Simulation Design for Global Health Education: Theoretical Foundation and Practical Recommendations. Journal of Medical Internet Research. 2023;25(1):e46639-e. Melson E, Davitadze M, Aftab M, Ng CY, Ooi E, Blaggan P, et al. Simulation via instant messaging-Birmingham advance (SIMBA) model helped improve clinicians' confidence to manage cases in diabetes and endocrinology. BMC Medical Education. 2020;20(1):1-10. Davitadze M, Ooi E, Ng CY, Zhou D, Thomas L, Hanania T, et al. SIMBA: using Kolb’s learning theory in simulation-based learning to improve participants’ confidence. BMC Medical Education. 2022;22(1):1-11. Li YY, Au ML, Tong LK, Ng WI, Wang SC. High-fidelity simulation in undergraduate nursing education: A meta-analysis. Nurse Education Today. 2022;111:105291-. Public Involvement - Health Research Authority. Regan de Bere S, Nunn S. Towards a pedagogy for patient and public involvement in medical education. Medical Education. 2016;50(1):79-92. Lu V, Kumar K. The hidden curriculum of peer teaching in developing a professional identity: Perspectives of medical students and junior doctors. The Clinical Teacher. 2024;21(2):e13680-e. Elendu C, Amaechi DC, Okatta AU, Amaechi EC, Elendu TC, Ezeh CP, et al. The impact of simulation-based training in medical education: A review. Medicine (United States). 2024;103(27):e38813-e. McGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. A critical review of simulation-based medical education research: 2003-2009. Medical Education. 2010;44(1):50-63. Malhotra K, Ali A, Soran V, Ogiliev T, Zhou D, Melson E, et al. Levelling the learning ground for healthcare professionals across the world through SIMBA: a mixed-methods study. BMJ Open. 2023;13(7):e069109-e. Jha V, Quinton ND, Bekker HL, Roberts TE. Strategies and interventions for the involvement of real patients in medical education: a systematic review. Medical Education. 2009;43(1):10-20. Towle A, Bainbridge L, Godolphin W, Katz A, Kline C, Lown B, et al. Active patient involvement in the education of health professionals. Medical Education. 2010;44(1):64-74. Ocloo J, Garfield S, Franklin BD, Dawson S. Exploring the theory, barriers and enablers for patient and public involvement across health, social care and patient safety: a systematic review of reviews. Health Research Policy and Systems. 2021;19(1):1-21. Additional Declarations No competing interests reported. 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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-6968791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497616434,"identity":"a5c7c1f0-1477-4101-acf9-d1ccd2825b06","order_by":0,"name":"Maiar Elhariry","email":"","orcid":"","institution":"Sandwell and West Birmingham NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Maiar","middleName":"","lastName":"Elhariry","suffix":""},{"id":497616435,"identity":"ff83deba-76cd-476c-9cc2-ab6f8d434e60","order_by":1,"name":"Sangamithra Ravi","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Sangamithra","middleName":"","lastName":"Ravi","suffix":""},{"id":497616436,"identity":"4abe0a94-0a45-4eeb-8043-4e97999dda84","order_by":2,"name":"Rahul Sagu","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Sagu","suffix":""},{"id":497616437,"identity":"577fb618-352c-4a62-9421-1c7e14799a23","order_by":3,"name":"Shubham Pareek","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Shubham","middleName":"","lastName":"Pareek","suffix":""},{"id":497616438,"identity":"8e81f53f-de50-4656-b93e-392b408a6fe2","order_by":4,"name":"Akshat Sinha","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Akshat","middleName":"","lastName":"Sinha","suffix":""},{"id":497616439,"identity":"15f7126c-226a-48e4-b8b0-bbcf4b7c2fd8","order_by":5,"name":"Aspasia Manta","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Aspasia","middleName":"","lastName":"Manta","suffix":""},{"id":497616440,"identity":"aca31c2c-20db-4e37-aa34-163114f68fed","order_by":6,"name":"Francesca Crowe","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Crowe","suffix":""},{"id":497616441,"identity":"62e31660-cbf2-4dcc-be55-55e748b620e3","order_by":7,"name":"Caroline Gillett","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Gillett","suffix":""},{"id":497616442,"identity":"5a96e695-c4a2-4be0-9519-e9bf5b006d25","order_by":8,"name":"Alessandro Prete","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Prete","suffix":""},{"id":497616443,"identity":"f80a0372-2952-4bd3-a275-27cfccc6b58f","order_by":9,"name":"Cristina L. 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The two key themes that emerged from the thematic inductive analysis of the participants' responses in 2024\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 B. The three key themes that emerged from the thematic inductive analysis of the participants’ responses in 2025.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2BMC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6968791/v1/c77b8bddf9d5f5cf542cdb23.jpg"},{"id":89062855,"identity":"ef710049-f0dd-4dc2-a7bc-bc4f83c67b3c","added_by":"auto","created_at":"2025-08-14 09:47:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2367545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6968791/v1/c5cd8320-fffa-4a10-948e-2a9610a34e32.pdf"},{"id":88781988,"identity":"1bb292e5-1983-400c-9ff4-dc8b9607c424","added_by":"auto","created_at":"2025-08-11 10:56:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":237791,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6968791/v1/de1ff3eab35eb03494efc699.pdf"},{"id":88781990,"identity":"0b3240ba-947a-4c0a-99f9-0af8ff01acfd","added_by":"auto","created_at":"2025-08-11 10:56:52","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":238391,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6968791/v1/5483a24c260cda1997a8cf4f.pdf"},{"id":88780771,"identity":"fa8f1a88-1151-44d5-9a30-f643f28f5daf","added_by":"auto","created_at":"2025-08-11 10:48:52","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":351284,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6968791/v1/46951e7f1ae2fe8190987581.pdf"},{"id":88780780,"identity":"59c204b1-4c69-4ea2-bb7a-19422f5a4bda","added_by":"auto","created_at":"2025-08-11 10:48:52","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":351339,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6968791/v1/7f884282edbfdd1c0fd79611.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Patient and Public Involvement into Simulation- Based Learning for Adrenal Conditions: A Mixed-Methods Study","fulltext":[{"header":"Background","content":"\u003cp\u003eWhile lecture-based learning (LBL) continues to serve as a foundational element within medical education, simulation-based learning (SBL) has emerged as a key pedagogical method, offering immersive and practical experiences that allow learners to acquire skills, engage in problem-solving, and comprehend complex concepts within a secure, realistic environment (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Evidence suggests that SBL contributes positively to clinical performance, attitudes, and collaborative practice, ultimately enhancing patient care outcomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Moreover, it has improved both the interpretation of clinical information and long-term knowledge retention in contrast to passive modalities such as recorded lectures (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The evolution of educational technologies has further supported the transition of SBL into virtual domains, thereby addressing barriers related to accessibility, cost-effectiveness, and scalability (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimulation via Instant Messaging for Bedside Application (SIMBA) is a scenario-based e-simulation model that leverages WhatsApp to deliver clinical teaching (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The SIMBA model is theoretically informed, drawing upon gaming simulation principles and Kolb’s experiential learning theory (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, it incorporates elements of interprofessional learning and operates with minimal resource requirements, having demonstrated its sustainability even amidst the constraints of the COVID-19 pandemic (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Studies have affirmed SIMBA’s efficacy in producing sustained improvements in clinical knowledge and performance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003cp\u003ePatient and Public Involvement and Engagement (PPIE) refers to the active collaboration between educators, researchers, and members of the public, such as patients, service users, or individuals with lived health experiences, in shaping the design, delivery, and dissemination of education or research (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This model ensures that initiatives are developed \u003cem\u003ewith\u003c/em\u003e and \u003cem\u003eby\u003c/em\u003e the public rather than being done \u003cem\u003eto\u003c/em\u003e, \u003cem\u003efor\u003c/em\u003e, or \u003cem\u003eabout\u003c/em\u003e them. Including people with lived experiences as active contributors in teaching and learning processes fosters greater transparency, accountability, and trust while providing healthcare professionals with richer perspectives on patient needs and lived experiences (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study, therefore, aims to evaluate the effect of incorporating PPIE into interactive SBL sessions using the SIMBA model on participants’ clinical confidence and performance in the context of adrenal disorder management. Additionally, the study examines the feasibility and acceptability of this combined SBL-PPI model, aiming to identify both enablers and challenges to meaningful patient involvement in simulation-based education.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was conducted in Birmingham, UK, between June 2023 and March 2025. The study was conducted as part of a wider SIMBA evaluation study, approved by the University of Birmingham Ethics Committee (Ref: ERN_2023 − 0544). A core team was created to organise and deliver the SBL-PPI session. This included two clinical experts in adrenal conditions, one PPIE expert, and an expert in applied and simulation-based learning. The team then recruited an early career researcher interested in applied learning and implementation science to create an extended team to deliver the simulation sessions. The extended team was primarily made up of early-career researchers. A Gantt chart was created to plan the organisation and delivery of the sessions, and bi-monthly meetings were organised between a member of the core team and the extended team to ensure progress as per the Gantt chart.\u003c/p\u003e\u003cp\u003eAll healthcare professionals interested in adrenal conditions were invited to participate in the simulation sessions. Patients diagnosed with adrenal conditions simulated in the sessions were also invited to participate in the study. All participants completed an informed consent to participate in the study.\u003c/p\u003e\u003cp\u003eDesigning Simulation\u003c/p\u003e\u003cp\u003eTwo simulation events on adrenal disorders were delivered in February 2024 and February 2025, respectively. Each two-day hybrid simulation-based learning event was designed using Kern’s Curriculum development framework (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The events featured nine case-based simulations (Primary Adrenal Insufficiency, Congenital Adrenal Hyperplasia (CAH), Adrenal Incidentaloma, Adrenocortical Carcinoma (ACC), Mild Autonomous Cortisol Secretion, Cushing's Syndrome, Primary Aldosteronism, Bilateral Adrenal Hyperplasia, and Phaeochromocytoma). The topics for the simulations were selected based on general needs and a targeted needs assessment through feedback from prior SIMBA sessions, a literature search, and expert inputs.\u003c/p\u003e\u003cp\u003eDetailed methodologies for preparing and delivering the event have been previously published (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Cases were reviewed by clinical experts specialising in respective adrenal diseases to ensure scientific accuracy and experts by experience to enhance realism and alignment with patient experiences, as detailed below. Participants received information about the session structure and joining instructions via email and Twitter.\u003c/p\u003e\u003cp\u003eIntegrating PPIE with SIMBA\u003c/p\u003e\u003cp\u003eTo integrate PPIE into the SBL, we invited people with lived experiences whose conditions featured in the case scenarios to a virtual workshop. These people with lived experiences were recruited through relevant UK charities. An early career researcher from the extended team coordinated the workshops. These workshops were conducted two to four weeks prior to each simulation event and were recorded with the participants’ consent. The case scenarios to be used in the simulation were shared with individuals who have lived experience during the workshop. They were then asked to share their comments on suggestions on three areas: what was done well in the case scenario, what could be done better, and the real-life relevance of the cases, specifically how closely the depicted patient journeys aligned with their own experiences.\u003c/p\u003e\u003cp\u003eThe early career researcher who coordinated the workshop transcribed the recording verbatim and conducted descriptive analysis to identify common themes discussed. These themes were shared with participants who attended the workshop to ensure they accurately reflected their perspectives. During the events, a nominated person with a lived experience representative presented these themes and gave an insight into their personal experience after the simulation alongside experts' discussions of the cases.\u003c/p\u003e\u003cp\u003eSession Delivery\u003c/p\u003e\u003cp\u003eThe events were open to participants both virtually and in person. To enhance accessibility, attendees from low- and middle-income countries (LMICs) were offered free participation.\u003c/p\u003e\u003cp\u003eBoth events had the same structure and flow. Day one began with a welcome address from a member of the core team, outlining the objectives and programme for participants. This was followed by pre-event survey, assessing participants’ confidence in the topics to be simulated [see Additional file 1, Additional file 2]. Then the simulation session started, with each case scenario allocated approximately 25 minutes. A brief technical clarification period was included after case one to ensure participants were comfortable with the simulation format and tools.\u003c/p\u003e\u003cp\u003eFollowing the simulation, the afternoon session focused on in-depth discussions of the cases presented. Each case was discussed by an expert in the field. This was complemented by presentations from patient representatives, who emphasised the importance of incorporating patient perspectives into professional discourse and underscored the commitment to holistic understanding and patient-centred care. The sessions emphasised evidence-based medicine (EBM) by referencing the latest guidelines and included dedicated sessions discussing the latest scientific updates in the field. Participants were encouraged to engage in discussions and reflect on their experiences to foster active learning.\u003c/p\u003e\u003cp\u003eTo facilitate engagement and learning, the programme was structured with regular comfort breaks, acknowledging the intense nature of the content and providing participants time to reflect and recharge. The events concluded with a feedback session and a post-event survey [see Additional file 3, Additional file 4].\u003c/p\u003e\u003cp\u003eEfficacy Assessment and Analysis\u003c/p\u003e\u003cp\u003eModerators downloaded the transcript of WhatsApp chats from the simulation and objectively assessed participant performance using the Global Rating Scale (GRS), which was approved by an expert in the respective case. There were up to nine measures for each competency per participant, and the mean value of these measures was calculated for each participant and used in the analysis. Continuous data were assessed for normality using the Shapiro-Wilk test. Mean values and standard deviation (SD) were calculated for each of the measures of competencies by certain demographic variables, including sex, country (UK, non-UK), level of training (junior, middle, senior, out of programme), and number of WhatsApp messages exchanged with moderators per case (\u0026lt; 50, ≥ 50). The difference in means for each of the four demographic variables was assessed using the t-test for individual samples and analysis of variance (ANOVA). Differences in self-reported confidence pre- and post-SIMBA sessions were evaluated using the Wilcoxon rank-sum test. To further explore factors influencing participants' learning outcomes, additional analyses were conducted. Multiple linear regression analyses were conducted to assess predictors of participant performance across six domains of the GRS. Predictors included year (2024, 2025), sex, country of training, training level, and WhatsApp usage per case. For each domain (history, physical examination, interpretation, investigations, clinical judgement, management), odds ratios (OR) and 95% confidence intervals (CIs) were calculated from the regression coefficients for interpretability. Additionally, ordinal logistic regression was used to evaluate predictors of self-reported confidence levels, with variables including session day, country, prior SIMBA participation, sex, training level, timepoint, and year. ORs, 95% CIs, and p-values were reported to identify statistically significant predictors. Data were analysed using SPSS software version 29.0.1.1. All tests proceeded as two-tailed, and p-values \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e\u003cp\u003eThe post-SIMBA survey also collected general feedback on the session, including assessments of session quality and applicability to their respective practices. Material collected from the open-ended questions of the post-SIMBA evaluation questionnaire was reviewed and combined in a thematic analysis. Using an inductive thematic analysis approach, data was analysed for main ideas and reduced to common themes of feedback.\u003c/p\u003e\u003cp\u003ePatient feedback was gathered separately through the scheduled workshops with early career researchers, focusing on their views on the cases simulated, and informal discussions, focusing on general event feedback and their interest in future involvement in similar opportunities to enhance medical education through contributions based on lived experiences. A descriptive analysis of their response was conducted to summarise patient feedback, focusing on capturing key points and overall impressions rather than exploring deeper patterns. This approach provided a straightforward overview of the limited patient perspectives, aligning with the goal of highlighting general themes related to patient experiences in a timely manner.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e\u003cp\u003e99 participants completed both pre- and post-simulation surveys and were included in the analysis: 40 participants in 2024 and 59 in 2025. The 2024 cohort was predominantly female (72.5%) and mostly UK-based (82.5%). In comparison, the 2025 cohort had a more balanced gender distribution (52.5% men, 47.5% women), but a higher UK representation (96.6%).\u003c/p\u003e\u003cp\u003eAcross both years, there was a consistent proportion of returning participants, reflecting sustained engagement with the SIMBA model. In 2024, 37.9% on Day 1 and 57.1% on Day 2 were returning attendees. In 2025, similar patterns were observed, with 37.0% on Day 1 and 43.2% on Day 2 having attended previous SIMBA sessions.\u003c/p\u003e\u003cp\u003eParticipants performance in the simulation sessions\u003c/p\u003e\u003cp\u003e Participants demonstrated strong performance across competencies in both years, with slightly higher overall scores in 2024. The highest scores in 2024 were observed in requesting appropriate physical examination (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83) and clinical judgement (4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65), while interpretation of imaging and management/discharge planning scored lower (3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 and 3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75, respectively). In 2025, a similar pattern emerged, though scores for interpretation of imaging (3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79) and management/discharge planning (3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87) were lower than in the previous year \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the 2024 session, no statistically significant differences were observed between men and women across the assessed competencies. In 2025, there were also no statistically significant differences between the two groups, except in history taking (men vs women: 3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63 vs. 3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84, p\u0026thinsp;=\u0026thinsp;0.028)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Participant performance varied by level of training in both years. In 2024, out-of-programme participants achieved the highest scores in the interpretation of investigations (4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08). In 2025, a similar trend was observed, with senior trainees outperforming junior and middle-grade peers, particularly in the interpretation of investigations (seniors vs juniors: 3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 vs 2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96, p\u0026thinsp;=\u0026thinsp;0.011). While other domains showed minor differences by training level, these did not reach statistical significance \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. While UK participants generally scored slightly higher, there were no significant differences in performance between UK-based and non-UK participants in either year \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Participant engagement, as measured by the number of WhatsApp messages exchanged with moderators per case during the simulation, had differing impacts across the two sessions. In 2024, participants who sent 50 or more messages per case scored higher across all domains, with significantly better scores in management and discharge planning (mean score 3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59 vs. 3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;0.05). In contrast, in 2025, higher engagement did not translate to improved scores. Notably, interpretation scores were lower among high-engagement participants (mean score 2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 vs. 3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85, p\u0026thinsp;=\u0026thinsp;0.044), and no other domains showed significant differences \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Global Rating Scale (GRS) scores by sex, training levels, country of practice, and level of engagement across clinical competencies.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eComparison of GRS scores by sex across clinical competencies\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCompetency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExamination\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvestigations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJudgement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eManagement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComparison of GRS scores by training level across clinical competencies.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eCompetency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eJunior\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMiddle\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;11)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eSenior\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;13)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eOut of programme\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eJunior\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eMiddle\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eSenior\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;36)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eOut of programme\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExamination\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvestigations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJudgement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eManagement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComparison of GRS scores by country of practice across clinical competencies.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eCompetency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eUK (N\u0026thinsp;=\u0026thinsp;33)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eNon-UK (N\u0026thinsp;=\u0026thinsp;7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eUK (N\u0026thinsp;=\u0026thinsp;57)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003eNon-UK (N\u0026thinsp;=\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExamination\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvestigations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJudgement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eManagement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComparison of GRS scores by level of engagement in WhatsApp-based discussion across clinical competencies.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eCompetency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;50 WhatsApp\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emessages per case\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;18)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;50 WhatsApp messages per case\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;22)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;50 WhatsApp\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emessages per case\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;52)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;50 WhatsApp messages per case\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExamination\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvestigations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJudgement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eManagement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eScores are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultiple linear regression analyses were conducted to assess the influence of year, sex, country of training, level of training, and WhatsApp usage on each GRS domain score. Across the six domains, predictors showed varying levels of influence. The year of participation was a significant predictor of performance in the clinical judgment domain, with participants in 2025 scoring better than those in 2024 (OR 0.50, 95% CI 0.32\u0026ndash;0.76, p\u0026thinsp;=\u0026thinsp;0.002). WhatsApp usage of 50 or more messages per case was associated with significantly greater odds of higher performance in the management domain (OR 1.54, 95% CI 1.03\u0026ndash;2.31, p\u0026thinsp;=\u0026thinsp;0.036). WhatsApp use also showed marginal associations with the interpretation domain (OR 1.44, 95% CI 0.97\u0026ndash;2.14, p\u0026thinsp;=\u0026thinsp;0.073). Other predictors, including sex, country, and training level, did not significantly predict GRS performance across the various GRS domains. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear Regression Results for Global Rating Scale (GRS) Domain Scores.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"19\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHistory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eExamination\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eInvestigations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e\u003cp\u003eClinical Judgement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c19\" namest=\"c17\"\u003e\u003cp\u003eManagement\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u0026ndash;2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.77\u0026ndash;2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.59\u0026ndash;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.43\u0026ndash;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.32\u0026ndash;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e0.4\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u0026ndash;1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.59\u0026ndash;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.44\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.49\u0026ndash;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.61\u0026ndash;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e0.56\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u0026ndash;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.41\u0026ndash;1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.39\u0026ndash;1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.33\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.42\u0026ndash;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e0.38\u0026ndash;1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTraining level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u0026ndash;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66\u0026ndash;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.67\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.79\u0026ndash;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.83\u0026ndash;1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e0.72\u0026ndash;1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo of WhatsApp messages per case\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u0026ndash;2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.92\u0026ndash;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.63\u0026ndash;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.97\u0026ndash;2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.92\u0026ndash;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e1.03\u0026ndash;2.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"19\"\u003eaOR, adjusted Odds Ratio; CI, Confidence Interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eImpact of Simulation on participant\u0026rsquo;s confidence Levels\u003c/p\u003e\u003cp\u003eBoth sessions led to substantial improvements in participants\u0026rsquo; self-reported confidence in managing adrenal disorders (pre- vs post-session: 2024, 45.4% vs 89.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 2025, 39.7% vs 88.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The most pronounced increases were noted in congenital adrenal hyperplasia (+\u0026thinsp;65.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), adrenocortical carcinoma (+\u0026thinsp;55.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and bilateral adrenal hyperplasia (+\u0026thinsp;54.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in 2024 session and primary bilateral macronodular adrenal hyperplasia (+\u0026thinsp;65.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), congenital adrenal hyperplasia (+\u0026thinsp;65.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and phaeochromocytoma (+\u0026thinsp;52.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in 2025 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Across both years, all observed changes in confidence highlight the effectiveness of the SIMBA model in enhancing clinical self-efficacy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChange in self-reported confidence across adrenal cases before and after SIMBA simulation for the 2024 and 2025 sessions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConfident\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUnsure\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot confident\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"29\" rowspan=\"30\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePrimary Adrenal Insufficiency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;37.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-6.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCongenital Adrenal Hyperplasia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;65.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-55.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-10.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAdrenal Incidentaloma\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;27.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-24.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAdrenocortical Carcinoma\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;55.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-44.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-10.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eMild Autonomous Cortisol Secretion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;42.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-37.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-5.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePrimary Hyperaldosteronism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;37.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-37.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eBilateral Adrenal Hyperplasia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;54.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-54.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCushing's Syndrome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;42.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePhaeochromocytoma\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;37.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-37.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eOverall 2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;44.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-40.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"26\" rowspan=\"27\"\u003e\u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCongenital Adrenal Hyperplasia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;65.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-52.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-13%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAdrenal Incidentaloma\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;30.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-21.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAdrenocortical Carcinoma\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;45.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-28.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-17.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePhaeochromocytoma\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;52.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-41.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-10.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eMild Autonomous Cortisol Secretion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;45.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-40.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCushing's Syndrome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;47.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-47.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePrimary bilateral macronodular adrenal hyperplasia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;65.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-54.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-11.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePrimary Hyperaldosteronism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;40.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-38.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eOverall 2025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-SIMBA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDifference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;49.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-40.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues are presented as percentages (%).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOrdinal logistic regression was conducted to further assess the influence of session day, country of training, prior SIMBA participation, sex, training level, timepoint, and year on self-reported confidence levels. Sex, training level, and timepoint were significant predictors. Male participants had higher odds of reporting increased confidence compared to females (OR 1.44, 95% CI 1.07\u0026ndash;1.93, p\u0026thinsp;=\u0026thinsp;0.015). Junior (OR 0.27, 95% CI 0.16\u0026ndash;0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and middle-level trainees (OR 0.37, 95% CI 0.27\u0026ndash;0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significantly lower odds of being more confident compared to senior trainees. Year of participation, session day, country of training, and first-time SIMBA participation were not statistically significant predictors of self-reported confidence. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOrdinal logistic regression predicting confidence rating following SIMBA adrenal sessions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003cp\u003e2025 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.329\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.986\u0026ndash;1.790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.438\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.072\u0026ndash;1.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTimepoint\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-SIMBA\u003c/p\u003e\u003cp\u003ePost-SIMBA (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.049\u0026ndash;0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTraining level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOut of Programme\u003c/p\u003e\u003cp\u003eJunior\u003c/p\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003cp\u003eSenior (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003cp\u003e0.269\u003c/p\u003e\u003cp\u003e0.367\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.382\u0026ndash;1.153\u003c/p\u003e\u003cp\u003e0.157\u0026ndash;0.425\u003c/p\u003e\u003cp\u003e0.269\u0026ndash;0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003cp\u003eNon-UK (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.448\u0026ndash;1.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDay\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDay 1\u003c/p\u003e\u003cp\u003eDay 2 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.662\u0026ndash;1.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1st Time Attending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.738\u0026ndash;1.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eaOR, adjusted Odds Ratio; CI, Confidence Interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eImpact on clinical practice and patient care.\u003c/p\u003e\u003cp\u003eIn 2024, participants\u0026rsquo; comments on the impact of the session on their care yielded two key themes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Recurring themes include Referral and Multidisciplinary team approach, as well as clinical knowledge. As a result of the session, multiple participants intended to change their practice and refer more often to patient support and self-help groups. Moreover, comments under the theme of specific clinical practice knowledge included: \"never delay management of Cushing's\" and \"I will be more aware of rare diagnoses and more likely to test\u0026rdquo;. In 2025, participants\u0026rsquo; comments on the impact of the session on their care yielded the same two themes and one new key theme: \u0026ldquo;holistic care\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Comments under this theme included: \u0026ldquo;continue to listen and take patient all patient communication into consideration without prejudice\u0026rdquo; and \u0026ldquo;consider patient\u0026rsquo;s socioeconomic status more carefully with their capacity to engage with medical services\u0026rdquo;.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePatients from both events also spoke about the benefits they received during the structured interviews. This focused on the impact this approach had on giving HCPs insight into the burden of their respective conditions, combined with both positive and negative encounters with HCPs, and how this is critical for personalised and patient-focused care. This also highlighted potential differences in priorities between HCPs and patients in terms of management. Patients identified PPI as a helpful aid to improve patient care and management positively, increasing their confidence in HCPS.\u003c/p\u003e\u003cp\u003eFeasibility and acceptability of the SIMBA-PPIE model\u003c/p\u003e\u003cp\u003eParticipant feedback from both years consistently demonstrated high satisfaction with the SIMBA-PPIE model (participants who agreed or strongly agreed that the simulated topics were relevant to their clinical practice: 2024, 96.5% on day 1 and 97.1% on day 2; 2025, 97.8% on day 1 and 95.4% on day 2). All participants felt that the SIMBA-PPIE Adrenal session improved their competencies. The perceived quality of the sessions remained excellent or good amongst most respondents: 100% in 2024 and 97.7% in 2025.\u003c/p\u003e\u003cp\u003eParticipants highlighted the engaging format, the relevance of the cases, and the inclusion of patient perspectives as key strengths. Most (over 90% across all sessions) felt that the content was impactful both personally and professionally. The SIMBA model was widely preferred over traditional lecture-based methods, with 92.0% of participants overall expressing a preference for SIMBA-style sessions over LBL. Furthermore, nearly all participants expressed a strong intention to attend future sessions (2024: 100% Day 1, 88.6% Day 2; 2025: 89.1% Day 1, 97.7% Day 2). Participants also expressed intentions to implement the discussed guideline recommendations and adopt a more holistic approach into clinical practice.\u003c/p\u003e\u003cp\u003eSuggestions for improvement primarily focused on logistical aspects such as session timing and moderator interaction. There was also a strong interest in expanding SIMBA to cover additional endocrine topics, including reproductive endocrinology, pituitary disorders, and thyroid disease, indicating enthusiasm for broader application of the model and a demand for further educational opportunities.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe first-of-its-kind integrated PPIE and SBL model enhanced HCPs\u0026rsquo; confidence in managing adrenal conditions while also benefiting patients by empowering them to share insights that tailor care to their respective needs more effectively. These findings align with previous research supporting the value of PPIE and SBL, as well as other forms of applied learning, in improving clinician confidence and competence(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The model demonstrates a transferable impact across diverse participant demographics\u0026mdash;particularly sex, country of training, and level of training (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The positive effects of the level of engagement with moderators during simulated cases (reflected by the number of WhatsApp messages exchanged) and previous participation in SIMBA simulations reinforce the model\u0026rsquo;s effectiveness in enhancing assessed competencies, suggesting that greater interaction with the model not only leads to improved outcomes in the current simulation but may also strengthen future performance by fostering continued familiarity and deeper learning. Participants rated the sessions highly for quality and engagement, expressing a strong preference for SIMBA over traditional lecture-based learning. This study provides key insights into the effectiveness of simulation-based learning in endocrinology, aligning with existing literature that emphasises its role in bridging theoretical knowledge and clinical practice (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy incorporating PPIE, the present study demonstrates a novel approach to SBL that provides participants with a holistic perspective on patient care, an area often overlooked in traditional education models (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The novel inclusion of perspectives from people with lived experiences offered additional benefits, emphasising the alignment of clinical management with patient needs and expectations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Such integration of lived experiences into educational frameworks has been shown to foster empathy, improve communication skills, and enhance the delivery of personalised care, as highlighted in previous studies(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Patient feedback underscored the positive impact of this approach on their confidence in healthcare providers. By highlighting discrepancies between clinical priorities and patient expectations, PPIE also identified areas where clinician training could be improved. These insights underscore the importance of adopting a collaborative approach to medical education, a strategy that has been previously advocated to enhance transparency, accountability, and trust in healthcare delivery (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe relatively small sample size may limit the generalizability of the findings. Nevertheless, the reproducibility of the results one year apart demonstrates the strength of our findings. While participants expressed intentions to adopt session learnings into clinical practice, the study did not objectively assess whether these changes were implemented or resulted in improved patient outcomes, highlighting an area for future research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that integrating PPIE into SBL has a positive impact on both participants and individuals with lived experiences, promoting a more holistic and patient-centred approach to healthcare. Further studies are needed to objectively assess the impact on clinical practice and patient care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Requirement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted as part of a wider SIMBA evaluation study, approved by the University of Birmingham Ethics Committee (Ref: ERN_2023-0544). Clinical trial number: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Participation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. All participants provided informed consent for inclusion in the study. This included consent to complete anonymised surveys and, where applicable, to be interviewed, recorded, and re-consented following transcript review.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the meeting grants by the Society for Endocrinology, UK and Health Education West Midlands and unrestricted educational grants from Recordati, Esteve and Lund Pharmaceuticals. None of these organisations had any input on the design or delivery of the events included in this study. PK receives support from the National Institute for Health and Care Research (NIHR) through his Advanced Clinician Scientist Fellowship (fellowship reference number: NIHR303671). Additionally, PK is supported by the Midlands Patient Safety Research Collaboration (PSRC) and the NIHR-supported Race, Equity, and Diversity in Careers Incubator. Alessandro Prete and Caroline Gillett receive support from the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham (grant reference number NIHR203326). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care UK.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eME is the first author, having made all-around contributions to the study. ME contributed to the study conception, supervised executive aspects of the project, and wrote the first draft. SR and RS led the delivery of the session in 2024 and they mentored SP to deliver the session in 2025. AS, AM and FC contributed to data curation, quantitative result analysis and fine-tuning of the research methods. CG recruited people with lived experiences and relevant patient groups and contributed to the development of the methodology. AP and CR \u0026nbsp;coordinated project planning and execution, recruiting relevant experts for the session, contributed to\u0026nbsp;fine-tuning the research methods, writing the first draft, and supervised executive aspects of the project. PK is the senior author who conceptualised the study, supervised all aspects of the project, finalised the research methods and critically reviewed and revised the manuscript. All authors contributed substantially to drafting and approving the final draft of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank the healthcare professionals who participated in this study. We thank the University of Birmingham Medical School students who participated as moderators in this study. We also thank the Health Education West Midlands Diabetes and Endocrinology Specialist Trainee Committee, Department of Metabolism and Systems Science, University of Birmingham, and Department of Applied Health Sciences, University of Birmingham, for supporting this study. Thanks to all our patients and patient groups, including Mr Dominic Hargreaves, who represented Addison\u0026apos;s Disease Self Help Group (ADSHG), Ms Jo Grey, who represented ACC Support UK and the Association for Multiple Endocrine Neoplasia Disorders (AMEND), and Ms Caroline Brown, who represented \u0026nbsp;Pituitary Foundation. We thank Helen Simpson, Helena Gleeson, John Newell Price, Yasir S. Elhassan, Martin Fassnacht, Ljiljana Marina, Florian Wernig, Xilin Wu, Lucas Bouys and John Ayuk for their support in delivering the simulated scenarios.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003eThe DEVI Collaboration includes the following members: Kashish Malhorta, Dengyi Zhou, Pavithra Sakithevel, Harshin Balakrishnan, Amynta Arshad, Anisah Ali, Haaziq Shiekh, Hanish Johal, Harshin Balakrishnan, Josh Banerjee, Khushi Kumar, Mukunth Kowshik, Pranav Viswanath Iyer, Shams Ali Baig, Shivam Choudry, Swetha Palanichamy, Syeda Sabba Batul, Vardhan Venkatesh, and Let\u0026iacute;cia Santiago. All members of the DEVI collaboration were involved in delivery of the SBL sessions, critically reviewed and contributed to the revision of the manuscript. Therefore, they all are included as collaborating authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArteaga E, Biesbroek R, Nalau J, Howes M. Across the Great Divide: A Systematic Literature Review to Address the Gap Between Theory and Practice. Sage Open. 2024;14(1).\u003c/li\u003e\n\u003cli\u003eTayade MC, Giri PA, Latti RG. Effectiveness of early clinical exposure in improving attitude and professional skills of medical students in current Indian medical education set up. Journal of Family Medicine and Primary Care. 2021;10(2):681-5.\u003c/li\u003e\n\u003cli\u003eSo HY, Chen PP, Wong GKC, Chan TTN. Simulation in medical education. Journal of the Royal College of Physicians of Edinburgh. 2019;49(1):52-7.\u003c/li\u003e\n\u003cli\u003eZhao H, Xiong J, Zhang Z, Qi C. Growth Mindset and College Students\u0026rsquo; Learning Engagement During the COVID-19 Pandemic: A Serial Mediation Model. Frontiers in Psychology. 2021;12:621094-.\u003c/li\u003e\n\u003cli\u003eBahattab A, Caviglia M, Martini D, Hubloue I, Corte FD, Ragazzoni L. Scenario-Based e-Simulation Design for Global Health Education: Theoretical Foundation and Practical Recommendations. Journal of Medical Internet Research. 2023;25(1):e46639-e.\u003c/li\u003e\n\u003cli\u003eMelson E, Davitadze M, Aftab M, Ng CY, Ooi E, Blaggan P, et al. Simulation via instant messaging-Birmingham advance (SIMBA) model helped improve clinicians\u0026apos; confidence to manage cases in diabetes and endocrinology. BMC Medical Education. 2020;20(1):1-10.\u003c/li\u003e\n\u003cli\u003eDavitadze M, Ooi E, Ng CY, Zhou D, Thomas L, Hanania T, et al. SIMBA: using Kolb\u0026rsquo;s learning theory in simulation-based learning to improve participants\u0026rsquo; confidence. BMC Medical Education. 2022;22(1):1-11.\u003c/li\u003e\n\u003cli\u003eLi YY, Au ML, Tong LK, Ng WI, Wang SC. High-fidelity simulation in undergraduate nursing education: A meta-analysis. Nurse Education Today. 2022;111:105291-.\u003c/li\u003e\n\u003cli\u003ePublic Involvement - Health Research Authority.\u003c/li\u003e\n\u003cli\u003eRegan de Bere S, Nunn S. Towards a pedagogy for patient and public involvement in medical education. Medical Education. 2016;50(1):79-92.\u003c/li\u003e\n\u003cli\u003eLu V, Kumar K. The hidden curriculum of peer teaching in developing a professional identity: Perspectives of medical students and junior doctors. The Clinical Teacher. 2024;21(2):e13680-e.\u003c/li\u003e\n\u003cli\u003eElendu C, Amaechi DC, Okatta AU, Amaechi EC, Elendu TC, Ezeh CP, et al. The impact of simulation-based training in medical education: A review. Medicine (United States). 2024;103(27):e38813-e.\u003c/li\u003e\n\u003cli\u003eMcGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. A critical review of simulation-based medical education research: 2003-2009. Medical Education. 2010;44(1):50-63.\u003c/li\u003e\n\u003cli\u003eMalhotra K, Ali A, Soran V, Ogiliev T, Zhou D, Melson E, et al. Levelling the learning ground for healthcare professionals across the world through SIMBA: a mixed-methods study. BMJ Open. 2023;13(7):e069109-e.\u003c/li\u003e\n\u003cli\u003eJha V, Quinton ND, Bekker HL, Roberts TE. Strategies and interventions for the involvement of real patients in medical education: a systematic review. Medical Education. 2009;43(1):10-20.\u003c/li\u003e\n\u003cli\u003eTowle A, Bainbridge L, Godolphin W, Katz A, Kline C, Lown B, et al. Active patient involvement in the education of health professionals. Medical Education. 2010;44(1):64-74.\u003c/li\u003e\n\u003cli\u003eOcloo J, Garfield S, Franklin BD, Dawson S. Exploring the theory, barriers and enablers for patient and public involvement across health, social care and patient safety: a systematic review of reviews. Health Research Policy and Systems. 2021;19(1):1-21.\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":"Simulation Training, Medical Education, Professional Competence, Patient-Centred Care, SBL, PPI.","lastPublishedDoi":"10.21203/rs.3.rs-6968791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6968791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003ePatient and Public Involvement and Engagement (PPIE) enhances patient-centred care but is inconsistently integrated into educational frameworks. Simulation-based learning (SBL) bridges the gap between theory and practice, promoting active learning. This study evaluated the impact of incorporating PPIE into SBL on healthcare professionals\u0026rsquo; (HCPs) confidence, performance, and clinical practice in managing adrenal disorders.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThis study, conducted between October 2023 and March 2025, included two hybrid events of SBL with PPIE integration on adrenal conditions designed using Kern\u0026rsquo;s framework. Patients provided input by contributing lived experiences to enrich the realism of the SBL scenarios. The change between pre-and post-session confidence was analysed using the Wilcoxon rank-sum test. Qualitative feedback from healthcare professionals and patients was thematically analysed to explore the intervention\u0026rsquo;s impact. Participant performance in the simulation was assessed using the Global Rating Scale.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eIn total, 99 participants attended SIMBA sessions in 2024 and 2025. Confidence in managing adrenal disorders significantly improved post-session in both years (44.3% improvement in 2024 and 49.2% in 2025). Factors influencing performance included training level and WhatsApp engagement, although their impacts varied from year to year. Participants praised the model\u0026rsquo;s relevance, interactivity, and patient involvement, with feedback indicating intent to improve clinical practice and holistic care. 92.0% overall expressed a preference for this SBL-PPIE structure over traditional lecture-based methods. Patient feedback valued the empathy-driven insights shared.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eIntegrating PPIE into SBL significantly improved HCPs\u0026rsquo; confidence and supported a patient-centred approach. Patients also reported increased confidence in the quality of their care.\u003c/p\u003e","manuscriptTitle":"Integrating Patient and Public Involvement into Simulation- Based Learning for Adrenal Conditions: A Mixed-Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 10:48:47","doi":"10.21203/rs.3.rs-6968791/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-08-06T04:35:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T12:33:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T04:53:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T19:17:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-07-08T19:14:53+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":"412cb6ce-a737-489a-9adc-4301eb31c058","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T10:48:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 10:48:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6968791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6968791","identity":"rs-6968791","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}