Simulated-patient evaluations during formative interviews predict exam performance better than self-report empathy and emotion recognition abilities

Simulated-patient evaluations during formative interviews predict exam performance better than self-report empathy and emotion recognition abilities | 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 Simulated-patient evaluations during formative interviews predict exam performance better than self-report empathy and emotion recognition abilities Valerie Carrard, Céline Bourquin, Sylvie Félix, Francine Viret, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9377659/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background Communication skills are central to clinical practice and are typically assessed through Objective Structured Clinical Examinations (OSCEs). Prior research indicates that self-report questionnaires, simulated-patient evaluations, and expert assessments each offer valuable yet non-interchangeable perspectives on medical students’ communication skills. However, it remains unclear whether these assessment modalities, when collected during the academic year, can predict subsequent OSCE performance and thereby help identify students struggling to meet communication competency standards. This study examined whether self-report empathy, emotion recognition abilities, and simulated-patient evaluations of communication skills predict later OSCE performance in medical students. Methods Data were drawn from the ETMED‑L longitudinal open-cohort project at the University of Lausanne. The 3rd - and 5th -year medical students who completed the yearly ETMED‑L online questionnaire and consented to retrieval of their communication skills evaluations were eligible for the present study. Self-report empathy was assessed using validated multidimensional instruments and emotion recognition was measured with a performance-based task. Simulated patients used a standardized form to evaluate communication skills during mandatory formative interviews. OSCEs performance was rated by experts using structured communication grids and total exam points. Structural equation modelling was used to examine how self-report empathy, emotion recognition, and simulated-patient evaluations independently and jointly predict OSCEs performance. Results A sample of 468 3rd‑year and 399 5th‑year students was analyzed. In both cohorts, self‑report empathy, emotion recognition, and simulated-patient evaluations independently predicted OSCEs performance. In combined models, only simulated-patient evaluations remained significant predictors of OSCEs performance, with effect sizes approaching large for the 3rd -year cohort and small in the 5th -year cohort. Conclusions Simulated-patient evaluations during formative training are the strongest predictors of later expert-rated OSCEs communication performance, outperforming both self-report and performance-based measures. These findings underscore the practical value of structured simulated‑patient programs within competency‑based medical curricula, as they may help identify students who have difficulty attaining the required communication competencies. Medical student Communication skills Empathy Emotion recognition Simulated patient OSCE Figures Figure 1 Figure 2 1. Introduction Effective communication skills are an essential pillar of medical competence. Since 2018, Switzerland has adopted a national competency-based approach to communication learning outcomes [ 1 ], aligned with the CanMEDS competency framework [ 2 ]. Guided by societal and patient needs, it defines specific competencies that medical students should have developed by the end of their undergraduate education. In this framework, Objective Structured Clinical Examinations (OSCEs) are used to evaluate medical students’ communication skills. OSCEs are standardized, performance-based examinations in which students participate in a sequence of structured clinical scenarios. In these stations, patients are portrayed by trained simulated patients, and students’ performance is assessed using standardized checklists completed by experts. Previous studies suggest that OSCEs performance predicts aspects of future clinical performance, including communication skills in clinical practice [ 3 , 4 ]. Substantial research has examined how self-report questionnaires, simulated-patient evaluations, and expert assessments of communication skills relate to one another, with the aim of testing the inter-rater reliability across these assessment modalities and the possibility of integrating different evaluation sources in the OSCEs [ 5 – 13 ]. While findings generally show good overall agreement, they also consistently reveal discrepancies (see for instance [ 5 – 7 ]) as they capture distinct perspectives and sensitivities regarding different dimensions of communication skills. Building on this conclusion of both agreement and complementarity, a logical extension of this work is to move from a cross-sectional to a longitudinal perspective by examining whether self-report measures and/or simulated-patient ratings collected throughout an academic year can predict subsequent performance in an OSCE. Addressing this question seems essential as identifying such predictive markers would allow for the early detection of students at risk of not reaching the expected level of communication skills. In turn, educators could explore these students’ pedagogical needs to foster improvement, thereby helping ensure that future physicians achieve the required level of competence in communication for high-quality clinical practice. In the context of the ETMED-L project [ 14 ], a cohort study examining the evolution of mental health and empathy throughout medical school via yearly online questionnaires, we additionally collected simulated-patient evaluations during formative interviews as well as OSCEs performance data. The aim of the present study was then to examine whether medical students’ self-report empathy, emotion recognition abilities, and the simulated-patient evaluations of their communication skills during the academic year could predict subsequent OSCEs performance as evaluated by experts. 2. Methods 2.1. Design This study is part of the 4-year ETMED-L project that examines the mental health and empathy of medical students using a longitudinal open-cohort design. 2.2. Procedure Each academic year from 2020–2021 to 2023–2024, an online questionnaire was sent to all medical students registered at the University of Lausanne (Switzerland), except for the external exchange students. The questionnaires were sent at the beginning of March 2021, November 2021, November 2022, and November 2023 (exam-free months). Each time, students had one month to complete the questionnaire, which took approximately one hour to complete. They received 50 CHF (~ 50 USD) upon completion of each yearly questionnaire. The online questionnaire [ 15 ] included several validated self-report instruments, a performance-based test of emotion recognition (see section 2.4.), and a consent form for the retrieval and use for research of the students’ communication skills evaluations produced during their curriculum. If they gave their written consent for it, two types of communication skills evaluations were retrieved: simulated-patient evaluations and OSCEs performance. Within our 6-year medical curriculum, mandatory formative encounters with simulated patients begin in the 2nd year with group sessions and continue annually with individual sessions until the 5th year. The simulated-patient evaluations of students’ communication skills used in the present study were conducted in curriculum year 3 (two formative interviews between September and May on the topic of history taking and diagnosis) and curriculum year 5 (one motivational interview between November and December). OSCEs final grades and communication skills ratings were also retrieved for the present study. These OSCEs are mandatory qualifying exams of students’ clinical competences conducted in May of curriculum year 3 and March of curriculum year 5. During an OSCE, students rotate through several stations in which specific clinical situations are simulated and the performance of the students in each situation is rated in a structured way by experts, including a specific evaluation grid for communication skills. The whole ETMED-L project, including the use of simulated patients’ and experts’ evaluations, was reviewed and approved by the Cantonal Research Ethics Committee - Vaud (Commission cantonale d'éthique de la recherche sur l'être humain – Vaud [CER-VD]) an independent public ethics committee operating at the cantonal level in Switzerland (project number 2020–02474 approved on January 20, 2021). The privacy rights of human subjects have been observed, and all participants provided written informed consent. 2.3. Participants For the present study, two cohorts were extracted from the ETMED-L data: the Year 3 and the Year 5 cohorts. They included all medical students who filled in the ETMED-L questionnaire during their third or fifth curriculum year and gave written consent for the retrieval and use of their communication skills evaluations. Note that the participants of the first ETMED-L data collection wave (2020–2021 academic year) were excluded, because simulated-patient evaluations were only implemented from the second data collection year on. 2.4. Measures 2.4.1. Online questionnaire data The ETMED-L questionnaire [ 15 ] included a section related to sociodemographic information, self-report instruments assessing different dimensions of empathy, and a performance task of emotion recognition abilities. It also presented several measures of mental health and burnout that were not used in the present study. Sociodemographic information. Seven sociodemographic characteristics known to potentially influence empathy and communication skills were considered in the present study: gender identification (male, female, or non-binary), age, number of parents with higher education (college or university degree), social support (mean score of two items measuring self-perceived availability of emotional and practical support from 0="not at all" to 10="a great deal"), having a partner, number of hours of physical activity per week, and having consulted a psychotherapist in the past year. Self-report empathy. The French versions of three well-validated instruments measuring different dimensions of empathy were used. The widely used Jefferson Scale of Physician Empathy Student-Version (JSPE-S [ 16 ]) was developed to assess medical students’ orientations or attitudes toward empathic relationships in the context of patient care and thus measures the cognitive dimension of empathy. The Questionnaire of Cognitive and Affective Empathy (QCAE [ 17 , 18 ]) was validated in a large sample of university students, and both the English and the French versions have been shown to reliably assess the cognitive and affective dimensions of empathy separately. The Ability to Modify Self-Presentation Scale (AMSP [ 19 ]) assesses one’s ability to adapt expressive behaviors in different social situations and was thus chosen as a self-report measure of the behavioral dimension of empathy. Emotion recognition abilities. The Geneva Emotion Recognition Test short version (GERT-S [ 20 ]) is a performance task presenting 42 short videos (less than 30 seconds each) of actors portraying one out of 14 different emotions (e.g., fear, despair, surprise, disgust, anger). The score is then computed as the percent of emotions correctly recognized. 2.4.2. Simulated-patient evaluations All medical students were informed by email that, as part of the ETMED-L project, simulated patients would evaluate their communication skills after each formative interview on a paper-pencil form developed for this study. The form included the 5-item Jefferson Scale of Patient Perception of Physician Empathy (JSPPPE [ 21 ]), a single item regarding general satisfaction with the communication (from 1 = unsatisfactory to 5 = exceptionally satisfactory), and the communication skills grid used during the OSCEs that includes 4 items relating to the quality of (1) answering to the emotions of the patients, (2) structure of the interview, (3) verbal behavior, and (4) nonverbal behavior (rated from 1 = not at all to 5 = totally). 2.4.3. OSCEs performance The total OSCEs points on which the final exam grade is based has been retrieved for the present study. Moreover, the communication skills grid filled in by all experts in every station was averaged across stations. It included a single item regarding general communication competency (from 1 = incompetent to 5 = exceptionally competent) and the same 4 items as in the simulated patients’ communication form: (1) answering to the emotions of the patients, (2) structure of the interview, (3) verbal behavior, and (4) nonverbal behavior. 2.5. Statistical Analyses Most assessment types examined in this study (self-report empathy, simulated-patient evaluation, and OSCEs) were evaluated using multiple indicators. Consequently, structural equation modeling (SEM) with latent variables was employed (see also Figs. 1 and 2 for graphical representation of the models). This approach allows for the effective and efficient modeling of complex interrelationships among multiple variables. The SELF-REPORT EMPATHY latent variable was estimated using scores from the JSPE-S, the cognitive and affective dimensions of the QCAE, and the AMSP. The SIMULATED PATIENT latent variable was estimated using the JSPPPE scores, the satisfaction with communication item, and the communication skills grid scores. For the Year 3 cohort, simulated-patient evaluations occurred during two formative interviews; thus, these indicators first loaded onto two intermediate latent variables (SP3.1 and SP3.2), which then loaded onto the SIMULATED PATIENT latent variable. The latent outcome variable, OSCE, was estimated using the final total OSCEs points, the communication competency item, and the communication skills grid scores. First, to determine whether self-report empathy, emotion recognition abilities, and simulated-patient evaluations of medical students’ communication skills predict expert-rated OSCEs performance, six separate models were tested: 2 cohorts (Year 3 and Year 5) X 3 assessment types (SELF-REPORT EMPATHY, GERT-S [emotion recognition abilities], and SIMULATED PATIENT) independently predicting exam performance (OSCE). Then, to test the conjoint impact of all assessment types, one final model was computed for each cohort separately (Year 3 and Year 5) with SELF-REPORT EMPATHY, GERT-S (emotion recognition abilities), and SIMULATED PATIENT evaluations predicting conjointly OSCE. Moreover, identifying as male, age, parents’ education, social support, having a partner, physical activity, and having consulted a psychotherapist were included in the models as control variables predicting SELF-REPORT EMPATHY, GERT-S, SIMULATED PATIENT, and OSCE. In all models, Maximum Likelihood estimation with Robust standard errors (MLR) was used. Goodness of fit of the models was evaluated with the following indices and rules: the Comparative Fit Index (CFI) should be > .90, the Root Mean Square Error of Approximation (RMSEA) should be < .05, and the Standardized Root Mean Square Residual (SRMR) should be < .08 [ 22 ]. The classic Chi 2 test was reported, but not used as a decisional index, because it is notably sensitive to sample size and thus nearly always rejects the model when large samples are used [ 23 ]. Effect sizes were estimated using the standardized regression coefficient β, with thresholds of 0.1, 0.3, and 0.5 interpreted as small, medium, and large effect sizes, respectively [ 24 ]. The highest missing rate in our data was 4.27% (simulated patients’ satisfaction with communication item in Year 3 cohort). Since research indicates that minimal information gain is achieved from imputing missing data when the missing rate is below 5% [ 25 ], listwise deletion was applied in all analyses. All analyses were run in Stata 18 [ 26 ]. 3. Results 3.1. Sample A detailed flow chart is available as supplementary material (Supplementary Fig. 1). Among the 756 eligible 3rd -year students, 528 participated to the ETMED-L project and 488 gave their consent for the retrieval and use of their communication skills data. Then, nine students who gave a wrong answer to the attention check questions of the questionnaire were excluded. After excluding students for whom communication skills data could not be retrieved (n = 10) or with totally missing self-report empathy and emotion recognition ability scores (n = 1), the final Year 3 cohort sample included 468 students, representing 62% of the eligible students. For the Year 5 cohort, 718 5th -year students were eligible, 526 participated to the ETMED-L project and 474 gave their consent for the retrieval and use of their communication skills data. After exclusion of the students who failed the attention check questions (n = 7) and those for whom communication skills data could not be retrieved (n = 68), the final Year 5 cohort sample included 399 students, representing 56% of the eligible students. 3.2. Descriptive Statistics The sample descriptives are presented in Table 1 . The participants are well distributed across the three participation waves, with between 30% and 36% of the students in each ETMED-L data collection year. The included students were 23 years old on average (range: 19–51) and represent well the gender proportions usually observed in the Lausanne Medical School, with a majority identifying as female (69%), a smaller proportion identifying as male (32%), and a minority identifying as non-binary (1%). Regarding sociodemographic background, around half of the included students have two parents with higher education (56%), and half of the students do not have a paid job in addition to their studies (54%). Detailed statistics for the variables of interest are available in Supplementary Table 1. Table 1 Sample Descriptives Year 3 (N = 468) Year 5 (N = 399) N % N % Participation year 2021–2022 151 32.26 106 26.57 2022–2023 169 36.11 143 35.84 2023–2024 148 31.62 150 37.59 Gender identification Female 321 68.59 259 64.91 Male 144 30.77 135 33.83 Non-binary 3 0.64 5 1.25 Parents with higher education Both 260 55.56 224 56.14 One 109 23.29 82 20.55 None 99 21.15 93 23.31 Having a partner Yes 246 52.56 226 56.64 No 222 47.44 173 43.36 Having consulted a psychotherapist Yes 100 21.37 109 27.32 No 368 78.63 290 27.32 [Table 1 about here] 3.3. Structural Equation Models’ Results Results of the structural equations models testing how self-report empathy, emotion recognition abilities, and simulated-patient evaluations independently predict OSCEs performance are displayed in Fig. 1 (see also Supplementary tables 2 and 3 for detailed results). We observed that in both cohorts, Year 3 and 5, all three assessment types significantly predicted expert-rated OSCEs performance. For the Year 3 cohort, simulated-patient evaluations demonstrated a strong significant effect (β = .54, p<.001), while self-reported empathy (β = .17, p=.013) and emotion recognition (β = .15, p=.002) showed smaller but significant effects. In the Year 5 cohort, all predictors yielded small significant effect sizes (empathy: β = .15, p=.039; emotion recognition: β = .13, p=.013; simulated-patient evaluations: β = .21, p<.001). All models showed a good fit to the data on all indices considered. [Figure 1 about here] Figure 2 presents the results of the structural equation models examining how self-report empathy, emotion recognition abilities, and simulated-patient evaluations conjointly predict OSCEs performance (see Supplementary Tables 4 and 5 for detailed results). These analyses indicate that simulated-patient evaluations are the strongest predictor of OSCEs performance. When considered alongside simulated-patient evaluations, neither self-report empathy nor emotion recognition abilities significantly predict OSCEs performance. In contrast, simulated-patient evaluations show a significant association, with an effect size approaching large for the Year 3 cohort (β = .49, p<.001) and small for the Year 2 cohort (β = .20, p<.001). All indices indicated a good fit to the data. [Figure 2 about here] 4. Discussion The present study examined whether self-report empathy, emotion recognition abilities, and simulated-patient evaluations during the academic year predict future exam performance. Our findings indicate that, among these three assessment modalities, simulated-patient evaluations obtained during formative training are the strongest predictor of future exam results. Indeed, when self-report empathy and emotion recognition were included in the same model as simulated-patient evaluations, only the latter emerged as a significant predictor of future OSCEs performance. This result may stem not only from the conceptual alignment between their rating schemes and those used in the exams, but also from the high ecological validity of interpersonal interactions. Communication skills are inherently relational and context-dependent; thus, assessments embedded in real-time interactions capture nuances that static measures like questionnaires and performance tasks cannot. The results of the present study thus aligns with pas literature underscoring the value of simulated-patient evaluations as a reliable measure of students’ communication skills that is consistent with experts’ ratings [ 5 – 11 ]. This convergence between simulated patients and experts may be attributed to the comprehensive training that simulated patients receive at our institution. At the University of Lausanne, the simulated patient training program includes theoretical introductions, role-plays, feedback giving skills, debriefing, and regular evaluations [ 27 ], all of which help ensure alignment with the communication competencies expected by experts during exams. The specificity of the simulated patient evaluation additionally resides in the combination of a trained and structured perspective with a subjective patient experience that unfolds in the interaction with the student. The importance of simulated patients’ feedback – and of their training to provide it – is well established. Consistent with experiential learning theory [ 28 ], which posits that interpersonal competencies are best developed through authentic practice, structured feedback, and opportunities for reflection, research has shown that simulation-based medical education is most effective when it incorporates accurate and meaningful feedback [ 29 ]. The present study further emphasizes the importance of high-quality training for simulated patients by indicating that, beyond the pedagogical value of accurate feedback in itself, appropriate training could additionally enable simulated patients to provide evaluations that are congruent with expert assessments during OSCEs when using a comparable scoring grid. Although resource-intensive, high-quality simulated patient training programs are justified by their educational benefits and the reliability of simulated patients’ assessments. Indeed, the other important practical implication stemming from the predictive power of simulated-patient evaluations is that these assessments could help proactively identify students demonstrating early signs of difficulty in achieving required communication skills standards. This early identification would enable targeted pedagogical interventions. However, translating these insights into action remains complex, as the underlying causes of underperformance can vary substantially from one student to another. Consequently, any interventions would need to be carefully designed and tailored to address each learner’s specific needs. They would also need to be embedded in a process of reflective integration by the student in order to be effective [ 30 ] and, ultimately, to support the development of the nuanced communication skills required for quality clinical practice. Although simulated-patient evaluation is the strongest predictor of exam performance, our results also show that self-report empathy is a significant, though modest, predictor of exam performance when considered on its own. Past findings regarding the link between self-report empathy and expert-rated performance are mixed, with some studies showing moderate but significant associations [ 31 , 32 ] and others finding no significant link [ 6 ]. The significant link found in the present study may be due to the multidimensional approach adopted to measure empathy. Empathy is widely recognized as a multidimensional construct, encompassing at least cognitive (i.e., perspective-taking) and affective processes (i.e., emotional contagion). By incorporating multiple instruments assessing three dimensions of empathy, our study may have provided a more comprehensive self-report measure of empathic ability that better aligns with expert ratings during OSCEs. Emotion recognition, measured through a performance-based task that does not rely on self-report was also a significant predictor of OSCEs performance when considered on its own. Aligning with the very scarce existing literature [ 33 ], our findings show that emotion recognition abilities are indeed significantly associated with exam performance, with a small effect size. This result is encouraging, as it suggests that the capacity to accurately identify others’ emotions translates into observable behaviors during OSCEs that experts seem able to capture in their evaluations. 4.1. Limitations and strengths The present study’s strength lies in the variety of communication skills evaluations included, the multidimensional approach to empathy, the use of validated instruments, and the collection of assessments grounded in practical teaching (simulated-patient interviews and OSCEs). However, the design of this study comes with some limitations that should be acknowledged. Although the response rate achieved was at the higher end of what is typically observed in this population [ 34 ], participation bias may have compromised representativeness and, consequently, the generalizability of the findings. To address this, we compared our sample to the usual gender and curriculum year distribution observed at our university and included control variables in the final model to mitigate potential bias. Nevertheless, the results may not be fully applicable to populations known to have lower survey response rates. This study was conducted at a single institution, which may limit the generalizability of the findings to other medical schools or cultural contexts. Comparative studies across institutions are warranted to confirm the results and would also clarify the role of simulated patient training protocols in shaping their evaluation accuracy. Conclusion This study demonstrates that among different assessments of communication skills collected throughout the medical curriculum, simulated-patient evaluations are the strongest and most consistent predictors of subsequent expert-rated OSCEs performance. While self-report empathy and emotion recognition abilities show modest associations with exam outcomes, only simulated-patient evaluations retain predictive value when considered alongside other measures, highlighting their ecological validity and their capacity to capture relational, context-dependent aspects of communication. These findings support the integration of structured simulated patient programs not only as formative training tools but also as valuable contributors to competency-based assessment strategies, enabling earlier identification of students who may require pedagogical support. By reinforcing the role of experiential and multisource assessments, this study underscores the importance of maintaining high-quality simulated patient training and rating to foster the development of communication skills essential for future clinical practice. Declarations Ethics approval and consent to participate The whole ETMED-L project, including the use of simulated patients’ and experts’ evaluations, was reviewed and approved by the Cantonal Research Ethics Committee - Vaud (Commission cantonale d'éthique de la recherche sur l'être humain – Vaud [CER-VD]) an independent public ethics committee operating at the cantonal level in Switzerland (project number 2020-02474 approved on January 20, 2021). The privacy rights of human subjects have been observed, and all participants provided written informed consent. Consent for publication Not applicable Availability of data and materials The dataset supporting the conclusions of this article is available in the Zenodo repository, https://doi.org/10.5281/zenodo.19250423 Competing interests The authors declare that they have no conflicts of interests. Funding This work was supported by the Swiss National Science Foundation (grant number 10001C_197442). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions AB and CB conceptualized the ETMED‑L project and secured funding. VC and CB collected the questionnaire data. VC, FV, and SF collected the simulated patient and OSCE data. VC performed the data analysis. VC and SF drafted the manuscript. AB, CB, FV, SF, and YT critically reviewed and revised the manuscript. All authors approved the final version and agree to be accountable for all aspects of the work. Acknowledgements The authors want to thank all simulated patients of the Lausanne Medical School and their trainers for their collaboration in this project. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process. During the preparation of this work, the authors used Microsoft Copilot 365, an AI language model, to assist with language editing and improve readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. References Michaud PA, Jucker-Kupper P, Group TP working. The “Profiles” document: a modern revision of the objectives of undergraduate medical studies in Switzerland. Swiss Medical Weekly. 2016;146:w14270–w14270. doi:10.4414/smw.2016.14270 Frank JR, Danoff D. The CanMEDS initiative: implementing an outcomes-based framework of physician competencies. Medical Teacher. 2007;29:642–7. doi:10.1080/01421590701746983 PubMed PMID: 18236250. 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Int J Soc Res Methodol. 2016;19:575–91. doi:10.1080/13645579.2015.1126486 StataCorp. Stata Statistical Software: Release 18. StataCorp LLC. College Station, TX; 2023. Viret F, Christen A, Boegli J, Félix S. Développement d’un programme de patients simulés : partage de quinze ans d’expérience à l’Université de Lausanne. Pédagogie médicale. 2023;24:115–27. doi:10.1051/pmed/2023003 Kolb DA, Boyatzis RE, Mainemelis C. Experiential Learning Theory: Previous Research and New Directions. In: Perspectives on Thinking, Learning, and Cognitive Styles. Routledge; 2001. Barry Issenberg S, Mcgaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Medical Teacher. 2005;27:10–28. doi:10.1080/01421590500046924 PubMed PMID: 16147767. Carless D, Boud D. The development of student feedback literacy: enabling uptake of feedback. Assessment & Evaluation in Higher Education. 2018;43:1315–25. doi:10.1080/02602938.2018.1463354 Chen DCR, Pahilan ME, Orlander JD. Comparing a Self-Administered Measure of Empathy with Observed Behavior Among Medical Students. J GEN INTERN MED. 2010;25:200–2. doi:10.1007/s11606-009-1193-4 Casas RS, Xuan Z, Jackson AH, Stanfield LE, Harvey NC, Chen DC. Associations of medical student empathy with clinical competence. Patient Education and Counseling. 2017;100:742–7. doi:10.1016/j.pec.2016.11.006 Schreckenbach T, Ochsendorf F, Sterz J, Rüsseler M, Bechstein WO, Bender B, et al. Emotion recognition and extraversion of medical students interact to predict their empathic communication perceived by simulated patients. BMC Med Educ. 2018;18:237. doi:10.1186/s12909-018-1342-8 Cho YI, Johnson TP, VanGeest JB. Enhancing Surveys of Health Care Professionals: A Meta-Analysis of Techniques to Improve Response. Eval Health Prof. 2013;36:382–407. doi:10.1177/0163278713496425 Additional Declarations No competing interests reported. Supplementary Files EMPACSSupMat20260306.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 18 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-9377659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631005468,"identity":"dbdceb94-40e6-4fc2-9473-324dd409653a","order_by":0,"name":"Valerie Carrard","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Valerie","middleName":"","lastName":"Carrard","suffix":""},{"id":631005469,"identity":"a4047cdc-6bef-418c-b5f3-0ebe3275c637","order_by":1,"name":"Céline Bourquin","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Céline","middleName":"","lastName":"Bourquin","suffix":""},{"id":631005470,"identity":"e092b771-d9b6-4d44-948b-41cf68470e1d","order_by":2,"name":"Sylvie Félix","email":"","orcid":"","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Sylvie","middleName":"","lastName":"Félix","suffix":""},{"id":631005471,"identity":"ebed84e6-7fdb-451d-a567-ec93354300b2","order_by":3,"name":"Francine Viret","email":"","orcid":"","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Francine","middleName":"","lastName":"Viret","suffix":""},{"id":631005472,"identity":"d5fb6cc4-a9af-4d21-99bf-ec4a16f9883f","order_by":4,"name":"Yusuke Takeuchi","email":"","orcid":"","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Takeuchi","suffix":""},{"id":631005473,"identity":"c2abbedd-63cf-4549-9ad6-f06f45d7c876","order_by":5,"name":"Alexandre Berney","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACdjDJDMQ8EAF+KCMBpxZmdC2SDSRrMThAQAt/M/MxiZ87rPP5GXgPPq7cYZO4+fjZY9I8DHZ5uLRIHGZLNuw9k245s4Ev2fDsmbTEbWfy0oBakotxOuwwj+ED3rbDBkD3mEk2th1O3HYDyJjBcCCxAYcO+cP8Hw7+BWqxP8Bj/rOx7X/i5hkEtBgc5mF8DLaFgceMsbHtQOIGCR4ziQ94tBgeZjM2lm1LN5A4zJcMdFiy8YwzOcYWHwyScWqRO978TPJtm7UBf3vvwY+NbXay/e1nDG8kVNjh1IIAkAhicISoNCCoHgHsSVA7CkbBKBgFIwQAAJQUVEnukxu+AAAAAElFTkSuQmCC","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":true,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Berney","suffix":""}],"badges":[],"createdAt":"2026-04-10 09:40:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9377659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9377659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108397430,"identity":"5d3b9250-a3f3-46d1-a04a-cc680b4d00fd","added_by":"auto","created_at":"2026-05-04 08:22:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural equation models with self-report empathy, emotion recognition, and simulated-patient evaluations independently predicting exam performance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoefficients are standardized b with thresholds of 0.1, 0.3, and 0.5 interpreted respectively as small, medium, and large effect sizes. SP = simulated-patient evaluations; OSCE = Objective Structured Clinical Examination; JSPE-S = Jefferson Scale of Physician Empathy - Student version; QCAE = Questionnaire of Cognitive and Affective Empathy; AMSP = Ability to Modify Self-Presentation; GERT-S = Geneva Emotion Recognition Test - Short Version; JSPPPE = Jefferson Scale of Patient Perception of Physician Empathy.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9377659/v1/c73723d22589f62000331af9.jpg"},{"id":108493258,"identity":"eb9fda13-e5f5-410b-a8e9-a762d1ea62a5","added_by":"auto","created_at":"2026-05-05 09:59:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural equation models with self-report empathy, emotion recognition, and simulated-patient evaluations conjointly predicting exam performance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentifying as male, age, parents’ education, social support, having a partner, physical activity, and having consulted a psychotherapist are included in all models as control variables predicting SELF-REPORT EMPATHY, GERT-S, SIMULATED PATIENT, and OSCE. Coefficients are standardized b with thresholds of 0.1, 0.3, and 0.5 interpreted respectively as small, medium, and large effect sizes. SP = simulated-patient evaluations; OSCE = Objective Structured Clinical Examination; JSPE-S = Jefferson Scale of Physician Empathy - Student version; QCAE = Questionnaire of Cognitive and Affective Empathy; AMSP = Ability to Modify Self-Presentation; GERT-S = Geneva Emotion Recognition Test - Short Version; JSPPPE = Jefferson Scale of Patient Perception of Physician Empathy.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9377659/v1/4f12b5c0bea94a3910c83de9.jpg"},{"id":108494845,"identity":"518ec015-210a-48c2-9f4e-be35a9d20e4d","added_by":"auto","created_at":"2026-05-05 10:07:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":515241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9377659/v1/5f241b1a-0325-4475-b764-1f1b44509de9.pdf"},{"id":108397432,"identity":"d86bd62f-04c0-4f29-ae60-5be9e7eafa77","added_by":"auto","created_at":"2026-05-04 08:22:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":105654,"visible":true,"origin":"","legend":"","description":"","filename":"EMPACSSupMat20260306.docx","url":"https://assets-eu.researchsquare.com/files/rs-9377659/v1/5e24262890907a2cec14dbab.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simulated-patient evaluations during formative interviews predict exam performance better than self-report empathy and emotion recognition abilities","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEffective communication skills are an essential pillar of medical competence. Since 2018, Switzerland has adopted a national competency-based approach to communication learning outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], aligned with the CanMEDS competency framework [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Guided by societal and patient needs, it defines specific competencies that medical students should have developed by the end of their undergraduate education. In this framework, Objective Structured Clinical Examinations (OSCEs) are used to evaluate medical students\u0026rsquo; communication skills. OSCEs are standardized, performance-based examinations in which students participate in a sequence of structured clinical scenarios. In these stations, patients are portrayed by trained simulated patients, and students\u0026rsquo; performance is assessed using standardized checklists completed by experts. Previous studies suggest that OSCEs performance predicts aspects of future clinical performance, including communication skills in clinical practice [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubstantial research has examined how self-report questionnaires, simulated-patient evaluations, and expert assessments of communication skills relate to one another, with the aim of testing the inter-rater reliability across these assessment modalities and the possibility of integrating different evaluation sources in the OSCEs [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While findings generally show good overall agreement, they also consistently reveal discrepancies (see for instance [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]) as they capture distinct perspectives and sensitivities regarding different dimensions of communication skills.\u003c/p\u003e \u003cp\u003eBuilding on this conclusion of both agreement and complementarity, a logical extension of this work is to move from a cross-sectional to a longitudinal perspective by examining whether self-report measures and/or simulated-patient ratings collected throughout an academic year can predict subsequent performance in an OSCE. Addressing this question seems essential as identifying such predictive markers would allow for the early detection of students at risk of not reaching the expected level of communication skills. In turn, educators could explore these students\u0026rsquo; pedagogical needs to foster improvement, thereby helping ensure that future physicians achieve the required level of competence in communication for high-quality clinical practice.\u003c/p\u003e \u003cp\u003eIn the context of the ETMED-L project [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], a cohort study examining the evolution of mental health and empathy throughout medical school via yearly online questionnaires, we additionally collected simulated-patient evaluations during formative interviews as well as OSCEs performance data. The aim of the present study was then to examine whether medical students\u0026rsquo; self-report empathy, emotion recognition abilities, and the simulated-patient evaluations of their communication skills during the academic year could predict subsequent OSCEs performance as evaluated by experts.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Design\u003c/h2\u003e \u003cp\u003eThis study is part of the 4-year ETMED-L project that examines the mental health and empathy of medical students using a longitudinal open-cohort design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Procedure\u003c/h2\u003e \u003cp\u003eEach academic year from 2020\u0026ndash;2021 to 2023\u0026ndash;2024, an online questionnaire was sent to all medical students registered at the University of Lausanne (Switzerland), except for the external exchange students. The questionnaires were sent at the beginning of March 2021, November 2021, November 2022, and November 2023 (exam-free months). Each time, students had one month to complete the questionnaire, which took approximately one hour to complete. They received 50 CHF (~\u0026thinsp;50 USD) upon completion of each yearly questionnaire. The online questionnaire [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] included several validated self-report instruments, a performance-based test of emotion recognition (see section 2.4.), and a consent form for the retrieval and use for research of the students\u0026rsquo; communication skills evaluations produced during their curriculum. If they gave their written consent for it, two types of communication skills evaluations were retrieved: simulated-patient evaluations and OSCEs performance.\u003c/p\u003e \u003cp\u003eWithin our 6-year medical curriculum, mandatory formative encounters with simulated patients begin in the 2nd year with group sessions and continue annually with individual sessions until the 5th year. The simulated-patient evaluations of students\u0026rsquo; communication skills used in the present study were conducted in curriculum year 3 (two formative interviews between September and May on the topic of history taking and diagnosis) and curriculum year 5 (one motivational interview between November and December).\u003c/p\u003e \u003cp\u003eOSCEs final grades and communication skills ratings were also retrieved for the present study. These OSCEs are mandatory qualifying exams of students\u0026rsquo; clinical competences conducted in May of curriculum year 3 and March of curriculum year 5. During an OSCE, students rotate through several stations in which specific clinical situations are simulated and the performance of the students in each situation is rated in a structured way by experts, including a specific evaluation grid for communication skills.\u003c/p\u003e \u003cp\u003e The whole ETMED-L project, including the use of simulated patients\u0026rsquo; and experts\u0026rsquo; evaluations, was reviewed and approved by the Cantonal Research Ethics Committee - Vaud (Commission cantonale d'\u0026eacute;thique de la recherche sur l'\u0026ecirc;tre humain \u0026ndash; Vaud [CER-VD]) an independent public ethics committee operating at the cantonal level in Switzerland (project number 2020\u0026ndash;02474 approved on January 20, 2021). The privacy rights of human subjects have been observed, and all participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Participants\u003c/h2\u003e \u003cp\u003eFor the present study, two cohorts were extracted from the ETMED-L data: the Year 3 and the Year 5 cohorts. They included all medical students who filled in the ETMED-L questionnaire during their third or fifth curriculum year and gave written consent for the retrieval and use of their communication skills evaluations. Note that the participants of the first ETMED-L data collection wave (2020\u0026ndash;2021 academic year) were excluded, because simulated-patient evaluations were only implemented from the second data collection year on.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Measures\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Online questionnaire data\u003c/h2\u003e \u003cp\u003eThe ETMED-L questionnaire [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] included a section related to sociodemographic information, self-report instruments assessing different dimensions of empathy, and a performance task of emotion recognition abilities. It also presented several measures of mental health and burnout that were not used in the present study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSociodemographic information.\u003c/b\u003e Seven sociodemographic characteristics known to potentially influence empathy and communication skills were considered in the present study: gender identification (male, female, or non-binary), age, number of parents with higher education (college or university degree), social support (mean score of two items measuring self-perceived availability of emotional and practical support from 0=\"not at all\" to 10=\"a great deal\"), having a partner, number of hours of physical activity per week, and having consulted a psychotherapist in the past year.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSelf-report empathy.\u003c/b\u003e The French versions of three well-validated instruments measuring different dimensions of empathy were used. The widely used Jefferson Scale of Physician Empathy Student-Version (JSPE-S [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) was developed to assess medical students\u0026rsquo; orientations or attitudes toward empathic relationships in the context of patient care and thus measures the cognitive dimension of empathy. The Questionnaire of Cognitive and Affective Empathy (QCAE [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]) was validated in a large sample of university students, and both the English and the French versions have been shown to reliably assess the cognitive and affective dimensions of empathy separately. The Ability to Modify Self-Presentation Scale (AMSP [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) assesses one\u0026rsquo;s ability to adapt expressive behaviors in different social situations and was thus chosen as a self-report measure of the behavioral dimension of empathy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEmotion recognition abilities.\u003c/b\u003e The Geneva Emotion Recognition Test short version (GERT-S [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]) is a performance task presenting 42 short videos (less than 30 seconds each) of actors portraying one out of 14 different emotions (e.g., fear, despair, surprise, disgust, anger). The score is then computed as the percent of emotions correctly recognized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Simulated-patient evaluations\u003c/h2\u003e \u003cp\u003eAll medical students were informed by email that, as part of the ETMED-L project, simulated patients would evaluate their communication skills after each formative interview on a paper-pencil form developed for this study. The form included the 5-item Jefferson Scale of Patient Perception of Physician Empathy (JSPPPE [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]), a single item regarding general satisfaction with the communication (from 1\u0026thinsp;=\u0026thinsp;unsatisfactory to 5\u0026thinsp;=\u0026thinsp;exceptionally satisfactory), and the communication skills grid used during the OSCEs that includes 4 items relating to the quality of (1) answering to the emotions of the patients, (2) structure of the interview, (3) verbal behavior, and (4) nonverbal behavior (rated from 1\u0026thinsp;=\u0026thinsp;not at all to 5\u0026thinsp;=\u0026thinsp;totally).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. OSCEs performance\u003c/h2\u003e \u003cp\u003eThe total OSCEs points on which the final exam grade is based has been retrieved for the present study. Moreover, the communication skills grid filled in by all experts in every station was averaged across stations. It included a single item regarding general communication competency (from 1\u0026thinsp;=\u0026thinsp;incompetent to 5\u0026thinsp;=\u0026thinsp;exceptionally competent) and the same 4 items as in the simulated patients\u0026rsquo; communication form: (1) answering to the emotions of the patients, (2) structure of the interview, (3) verbal behavior, and (4) nonverbal behavior.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analyses\u003c/h2\u003e \u003cp\u003eMost assessment types examined in this study (self-report empathy, simulated-patient evaluation, and OSCEs) were evaluated using multiple indicators. Consequently, structural equation modeling (SEM) with latent variables was employed (see also Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for graphical representation of the models). This approach allows for the effective and efficient modeling of complex interrelationships among multiple variables. The SELF-REPORT EMPATHY latent variable was estimated using scores from the JSPE-S, the cognitive and affective dimensions of the QCAE, and the AMSP. The SIMULATED PATIENT latent variable was estimated using the JSPPPE scores, the satisfaction with communication item, and the communication skills grid scores. For the Year 3 cohort, simulated-patient evaluations occurred during two formative interviews; thus, these indicators first loaded onto two intermediate latent variables (SP3.1 and SP3.2), which then loaded onto the SIMULATED PATIENT latent variable. The latent outcome variable, OSCE, was estimated using the final total OSCEs points, the communication competency item, and the communication skills grid scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFirst, to determine whether self-report empathy, emotion recognition abilities, and simulated-patient evaluations of medical students\u0026rsquo; communication skills predict expert-rated OSCEs performance, six separate models were tested: 2 cohorts (Year 3 and Year 5) X 3 assessment types (SELF-REPORT EMPATHY, GERT-S [emotion recognition abilities], and SIMULATED PATIENT) independently predicting exam performance (OSCE). Then, to test the conjoint impact of all assessment types, one final model was computed for each cohort separately (Year 3 and Year 5) with SELF-REPORT EMPATHY, GERT-S (emotion recognition abilities), and SIMULATED PATIENT evaluations predicting conjointly OSCE. Moreover, identifying as male, age, parents\u0026rsquo; education, social support, having a partner, physical activity, and having consulted a psychotherapist were included in the models as control variables predicting SELF-REPORT EMPATHY, GERT-S, SIMULATED PATIENT, and OSCE.\u003c/p\u003e \u003cp\u003eIn all models, Maximum Likelihood estimation with Robust standard errors (MLR) was used. Goodness of fit of the models was evaluated with the following indices and rules: the Comparative Fit Index (CFI) should be \u0026gt; .90, the Root Mean Square Error of Approximation (RMSEA) should be \u0026lt; .05, and the Standardized Root Mean Square Residual (SRMR) should be \u0026lt; .08 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The classic Chi\u003csup\u003e2\u003c/sup\u003e test was reported, but not used as a decisional index, because it is notably sensitive to sample size and thus nearly always rejects the model when large samples are used [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Effect sizes were estimated using the standardized regression coefficient β, with thresholds of 0.1, 0.3, and 0.5 interpreted as small, medium, and large effect sizes, respectively [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe highest missing rate in our data was 4.27% (simulated patients\u0026rsquo; satisfaction with communication item in Year 3 cohort). Since research indicates that minimal information gain is achieved from imputing missing data when the missing rate is below 5% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], listwise deletion was applied in all analyses. All analyses were run in Stata 18 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sample\u003c/h2\u003e \u003cp\u003eA detailed flow chart is available as supplementary material (Supplementary Fig.\u0026nbsp;1). Among the 756 eligible 3rd -year students, 528 participated to the ETMED-L project and 488 gave their consent for the retrieval and use of their communication skills data. Then, nine students who gave a wrong answer to the attention check questions of the questionnaire were excluded. After excluding students for whom communication skills data could not be retrieved (n\u0026thinsp;=\u0026thinsp;10) or with totally missing self-report empathy and emotion recognition ability scores (n\u0026thinsp;=\u0026thinsp;1), the final Year 3 cohort sample included 468 students, representing 62% of the eligible students. For the Year 5 cohort, 718 5th -year students were eligible, 526 participated to the ETMED-L project and 474 gave their consent for the retrieval and use of their communication skills data. After exclusion of the students who failed the attention check questions (n\u0026thinsp;=\u0026thinsp;7) and those for whom communication skills data could not be retrieved (n\u0026thinsp;=\u0026thinsp;68), the final Year 5 cohort sample included 399 students, representing 56% of the eligible students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe sample descriptives are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The participants are well distributed across the three participation waves, with between 30% and 36% of the students in each ETMED-L data collection year. The included students were 23 years old on average (range: 19\u0026ndash;51) and represent well the gender proportions usually observed in the Lausanne Medical School, with a majority identifying as female (69%), a smaller proportion identifying as male (32%), and a minority identifying as non-binary (1%). Regarding sociodemographic background, around half of the included students have two parents with higher education (56%), and half of the students do not have a paid job in addition to their studies (54%). Detailed statistics for the variables of interest are available in Supplementary Table\u0026nbsp;1.\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\u003eSample Descriptives\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYear 3 (N\u0026thinsp;=\u0026thinsp;468)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eYear 5 (N\u0026thinsp;=\u0026thinsp;399)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2021\u0026ndash;2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2022\u0026ndash;2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2023\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender identification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-binary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParents with higher education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOne\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving a partner\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving consulted a psychotherapist\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Structural Equation Models\u0026rsquo; Results\u003c/h2\u003e \u003cp\u003eResults of the structural equations models testing how self-report empathy, emotion recognition abilities, and simulated-patient evaluations independently predict OSCEs performance are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (see also Supplementary tables 2 and 3 for detailed results). We observed that in both cohorts, Year 3 and 5, all three assessment types significantly predicted expert-rated OSCEs performance. For the Year 3 cohort, simulated-patient evaluations demonstrated a strong significant effect (β\u0026thinsp;=\u0026thinsp;.54, p\u0026lt;.001), while self-reported empathy (β\u0026thinsp;=\u0026thinsp;.17, p=.013) and emotion recognition (β\u0026thinsp;=\u0026thinsp;.15, p=.002) showed smaller but significant effects. In the Year 5 cohort, all predictors yielded small significant effect sizes (empathy: β\u0026thinsp;=\u0026thinsp;.15, p=.039; emotion recognition: β\u0026thinsp;=\u0026thinsp;.13, p=.013; simulated-patient evaluations: β\u0026thinsp;=\u0026thinsp;.21, p\u0026lt;.001). All models showed a good fit to the data on all indices considered.\u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the structural equation models examining how self-report empathy, emotion recognition abilities, and simulated-patient evaluations conjointly predict OSCEs performance (see Supplementary Tables\u0026nbsp;4 and 5 for detailed results). These analyses indicate that simulated-patient evaluations are the strongest predictor of OSCEs performance. When considered alongside simulated-patient evaluations, neither self-report empathy nor emotion recognition abilities significantly predict OSCEs performance. In contrast, simulated-patient evaluations show a significant association, with an effect size approaching large for the Year 3 cohort (β\u0026thinsp;=\u0026thinsp;.49, p\u0026lt;.001) and small for the Year 2 cohort (β\u0026thinsp;=\u0026thinsp;.20, p\u0026lt;.001). All indices indicated a good fit to the data.\u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study examined whether self-report empathy, emotion recognition abilities, and simulated-patient evaluations during the academic year predict future exam performance. Our findings indicate that, among these three assessment modalities, simulated-patient evaluations obtained during formative training are the strongest predictor of future exam results.\u003c/p\u003e \u003cp\u003eIndeed, when self-report empathy and emotion recognition were included in the same model as simulated-patient evaluations, only the latter emerged as a significant predictor of future OSCEs performance. This result may stem not only from the conceptual alignment between their rating schemes and those used in the exams, but also from the high ecological validity of interpersonal interactions. Communication skills are inherently relational and context-dependent; thus, assessments embedded in real-time interactions capture nuances that static measures like questionnaires and performance tasks cannot. The results of the present study thus aligns with pas literature underscoring the value of simulated-patient evaluations as a reliable measure of students’ communication skills that is consistent with experts’ ratings [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis convergence between simulated patients and experts may be attributed to the comprehensive training that simulated patients receive at our institution. At the University of Lausanne, the simulated patient training program includes theoretical introductions, role-plays, feedback giving skills, debriefing, and regular evaluations [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], all of which help ensure alignment with the communication competencies expected by experts during exams. The specificity of the simulated patient evaluation additionally resides in the combination of a trained and structured perspective with a subjective patient experience that unfolds in the interaction with the student. The importance of simulated patients’ feedback – and of their training to provide it – is well established. Consistent with experiential learning theory [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], which posits that interpersonal competencies are best developed through authentic practice, structured feedback, and opportunities for reflection, research has shown that simulation-based medical education is most effective when it incorporates accurate and meaningful feedback [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. The present study further emphasizes the importance of high-quality training for simulated patients by indicating that, beyond the pedagogical value of accurate feedback in itself, appropriate training could additionally enable simulated patients to provide evaluations that are congruent with expert assessments during OSCEs when using a comparable scoring grid. Although resource-intensive, high-quality simulated patient training programs are justified by their educational benefits and the reliability of simulated patients’ assessments.\u003c/p\u003e \u003cp\u003eIndeed, the other important practical implication stemming from the predictive power of simulated-patient evaluations is that these assessments could help proactively identify students demonstrating early signs of difficulty in achieving required communication skills standards. This early identification would enable targeted pedagogical interventions. However, translating these insights into action remains complex, as the underlying causes of underperformance can vary substantially from one student to another. Consequently, any interventions would need to be carefully designed and tailored to address each learner’s specific needs. They would also need to be embedded in a process of reflective integration by the student in order to be effective [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] and, ultimately, to support the development of the nuanced communication skills required for quality clinical practice.\u003c/p\u003e \u003cp\u003eAlthough simulated-patient evaluation is the strongest predictor of exam performance, our results also show that self-report empathy is a significant, though modest, predictor of exam performance when considered on its own. Past findings regarding the link between self-report empathy and expert-rated performance are mixed, with some studies showing moderate but significant associations [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] and others finding no significant link [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. The significant link found in the present study may be due to the multidimensional approach adopted to measure empathy. Empathy is widely recognized as a multidimensional construct, encompassing at least cognitive (i.e., perspective-taking) and affective processes (i.e., emotional contagion). By incorporating multiple instruments assessing three dimensions of empathy, our study may have provided a more comprehensive self-report measure of empathic ability that better aligns with expert ratings during OSCEs.\u003c/p\u003e \u003cp\u003eEmotion recognition, measured through a performance-based task that does not rely on self-report was also a significant predictor of OSCEs performance when considered on its own. Aligning with the very scarce existing literature [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], our findings show that emotion recognition abilities are indeed significantly associated with exam performance, with a small effect size. This result is encouraging, as it suggests that the capacity to accurately identify others’ emotions translates into observable behaviors during OSCEs that experts seem able to capture in their evaluations.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Limitations and strengths\u003c/h2\u003e \u003cp\u003eThe present study’s strength lies in the variety of communication skills evaluations included, the multidimensional approach to empathy, the use of validated instruments, and the collection of assessments grounded in practical teaching (simulated-patient interviews and OSCEs). However, the design of this study comes with some limitations that should be acknowledged. Although the response rate achieved was at the higher end of what is typically observed in this population [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e], participation bias may have compromised representativeness and, consequently, the generalizability of the findings. To address this, we compared our sample to the usual gender and curriculum year distribution observed at our university and included control variables in the final model to mitigate potential bias. Nevertheless, the results may not be fully applicable to populations known to have lower survey response rates. This study was conducted at a single institution, which may limit the generalizability of the findings to other medical schools or cultural contexts. Comparative studies across institutions are warranted to confirm the results and would also clarify the role of simulated patient training protocols in shaping their evaluation accuracy.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that among different assessments of communication skills collected throughout the medical curriculum, simulated-patient evaluations are the strongest and most consistent predictors of subsequent expert-rated OSCEs performance. While self-report empathy and emotion recognition abilities show modest associations with exam outcomes, only simulated-patient evaluations retain predictive value when considered alongside other measures, highlighting their ecological validity and their capacity to capture relational, context-dependent aspects of communication. These findings support the integration of structured simulated patient programs not only as formative training tools but also as valuable contributors to competency-based assessment strategies, enabling earlier identification of students who may require pedagogical support. By reinforcing the role of experiential and multisource assessments, this study underscores the importance of maintaining high-quality simulated patient training and rating to foster the development of communication skills essential for future clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe whole ETMED-L project, including the use of simulated patients\u0026rsquo; and experts\u0026rsquo; evaluations, was reviewed and approved by the Cantonal Research Ethics Committee - Vaud (Commission cantonale d\u0026apos;\u0026eacute;thique de la recherche sur l\u0026apos;\u0026ecirc;tre humain \u0026ndash; Vaud [CER-VD]) an independent public ethics committee operating at the cantonal level in Switzerland (project number 2020-02474 approved on January 20, 2021). The privacy rights of human subjects have been observed, and all participants provided written informed consent.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is available in the Zenodo repository, https://doi.org/10.5281/zenodo.19250423\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Swiss National Science Foundation (grant number 10001C_197442). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAB and CB conceptualized the ETMED‑L project and secured funding. VC and CB collected the questionnaire data. VC, FV, and SF collected the simulated patient and OSCE data. VC performed the data analysis. VC and SF drafted the manuscript. AB, CB, FV, SF, and YT critically reviewed and revised the manuscript. All authors approved the final version and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors want to thank all simulated patients of the Lausanne Medical School and their trainers for their collaboration in this project.\u003c/p\u003e\n\u003ch2\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process.\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used Microsoft Copilot 365, an AI language model, to assist with language editing and improve readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMichaud PA, Jucker-Kupper P, Group TP working. 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BMC Med Educ. 2021;21:107. doi:10.1186/s12909-021-02540-y\u003c/li\u003e\n\u003cli\u003eBerg K, Majdan JF, Berg D, Veloski J, Hojat M. A comparison of medical students\u0026rsquo; self-reported empathy with simulated patients\u0026rsquo; assessments of the students\u0026rsquo; empathy. Medical Teacher. 2011;33:388\u0026ndash;91. doi:10.3109/0142159X.2010.530319 PubMed PMID: 21517687.\u003c/li\u003e\n\u003cli\u003eBerney A, Carrard V, Berney S, Schlegel K, Gaume J, Gholam M, et al. Study protocol for the ETMED-L project: Longitudinal study of mental health and interpersonal competence of medical students in a Swiss university using a comprehensive framework of empathy. BMJ Open. 2021;11:e053070. doi:10.1136/bmjopen-2021-053070 PubMed PMID: 34862292.\u003c/li\u003e\n\u003cli\u003eCarrard V, Bourquin C, Berney A. Questionnaires for the ETMED-L project [Internet]. 2024. 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Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988.\u003c/li\u003e\n\u003cli\u003eLee KJ, Roberts G, Doyle LW, Anderson PJ, Carlin JB. Multiple imputation for missing data in a longitudinal cohort study: A tutorial based on a detailed case study involving imputation of missing outcome data. Int J Soc Res Methodol. 2016;19:575\u0026ndash;91. doi:10.1080/13645579.2015.1126486\u003c/li\u003e\n\u003cli\u003eStataCorp. Stata Statistical Software: Release 18. StataCorp LLC. College Station, TX; 2023.\u003c/li\u003e\n\u003cli\u003eViret F, Christen A, Boegli J, F\u0026eacute;lix S. D\u0026eacute;veloppement d\u0026rsquo;un programme de patients simul\u0026eacute;s : partage de quinze ans d\u0026rsquo;exp\u0026eacute;rience \u0026agrave; l\u0026rsquo;Universit\u0026eacute; de Lausanne. P\u0026eacute;dagogie m\u0026eacute;dicale. 2023;24:115\u0026ndash;27. doi:10.1051/pmed/2023003\u003c/li\u003e\n\u003cli\u003eKolb DA, Boyatzis RE, Mainemelis C. Experiential Learning Theory: Previous Research and New Directions. In: Perspectives on Thinking, Learning, and Cognitive Styles. Routledge; 2001.\u003c/li\u003e\n\u003cli\u003eBarry Issenberg S, Mcgaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Medical Teacher. 2005;27:10\u0026ndash;28. doi:10.1080/01421590500046924 PubMed PMID: 16147767.\u003c/li\u003e\n\u003cli\u003eCarless D, Boud D. The development of student feedback literacy: enabling uptake of feedback. Assessment \u0026amp; Evaluation in Higher Education. 2018;43:1315\u0026ndash;25. doi:10.1080/02602938.2018.1463354\u003c/li\u003e\n\u003cli\u003eChen DCR, Pahilan ME, Orlander JD. Comparing a Self-Administered Measure of Empathy with Observed Behavior Among Medical Students. J GEN INTERN MED. 2010;25:200\u0026ndash;2. doi:10.1007/s11606-009-1193-4\u003c/li\u003e\n\u003cli\u003eCasas RS, Xuan Z, Jackson AH, Stanfield LE, Harvey NC, Chen DC. Associations of medical student empathy with clinical competence. Patient Education and Counseling. 2017;100:742\u0026ndash;7. doi:10.1016/j.pec.2016.11.006\u003c/li\u003e\n\u003cli\u003eSchreckenbach T, Ochsendorf F, Sterz J, R\u0026uuml;sseler M, Bechstein WO, Bender B, et al. Emotion recognition and extraversion of medical students interact to predict their empathic communication perceived by simulated patients. BMC Med Educ. 2018;18:237. doi:10.1186/s12909-018-1342-8\u003c/li\u003e\n\u003cli\u003eCho YI, Johnson TP, VanGeest JB. Enhancing Surveys of Health Care Professionals: A Meta-Analysis of Techniques to Improve Response. Eval Health Prof. 2013;36:382\u0026ndash;407. doi:10.1177/0163278713496425\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":"Medical student, Communication skills, Empathy, Emotion recognition, Simulated patient, OSCE","lastPublishedDoi":"10.21203/rs.3.rs-9377659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9377659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCommunication skills are central to clinical practice and are typically assessed through Objective Structured Clinical Examinations (OSCEs). Prior research indicates that self-report questionnaires, simulated-patient evaluations, and expert assessments each offer valuable yet non-interchangeable perspectives on medical students\u0026rsquo; communication skills. However, it remains unclear whether these assessment modalities, when collected during the academic year, can predict subsequent OSCE performance and thereby help identify students struggling to meet communication competency standards. This study examined whether self-report empathy, emotion recognition abilities, and simulated-patient evaluations of communication skills predict later OSCE performance in medical students.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were drawn from the ETMED‑L longitudinal open-cohort project at the University of Lausanne. The 3rd - and 5th -year medical students who completed the yearly ETMED‑L online questionnaire and consented to retrieval of their communication skills evaluations were eligible for the present study. Self-report empathy was assessed using validated multidimensional instruments and emotion recognition was measured with a performance-based task. Simulated patients used a standardized form to evaluate communication skills during mandatory formative interviews. OSCEs performance was rated by experts using structured communication grids and total exam points. Structural equation modelling was used to examine how self-report empathy, emotion recognition, and simulated-patient evaluations independently and jointly predict OSCEs performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA sample of 468 3rd‑year and 399 5th‑year students was analyzed. In both cohorts, self‑report empathy, emotion recognition, and simulated-patient evaluations independently predicted OSCEs performance. In combined models, only simulated-patient evaluations remained significant predictors of OSCEs performance, with effect sizes approaching large for the 3rd -year cohort and small in the 5th -year cohort.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSimulated-patient evaluations during formative training are the strongest predictors of later expert-rated OSCEs communication performance, outperforming both self-report and performance-based measures. These findings underscore the practical value of structured simulated‑patient programs within competency‑based medical curricula, as they may help identify students who have difficulty attaining the required communication competencies.\u003c/p\u003e","manuscriptTitle":"Simulated-patient evaluations during formative interviews predict exam performance better than self-report empathy and emotion recognition abilities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:22:07","doi":"10.21203/rs.3.rs-9377659/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T11:16:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T23:55:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T21:18:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224345915743470504511116562854593991137","date":"2026-05-04T15:53:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"840560904916633269240765675951313224","date":"2026-05-04T13:40:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294256566388482173864607207043203729590","date":"2026-04-23T13:38:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T18:12:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T17:44:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-18T09:08:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T08:47:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-04-17T07:48:43+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":"3742248d-890d-4c1d-91de-dbc8ef0345ba","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T11:16:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T23:55:19+00:00","index":63,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T21:18:31+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"224345915743470504511116562854593991137","date":"2026-05-04T15:53:43+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"840560904916633269240765675951313224","date":"2026-05-04T13:40:42+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T14:48:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:22:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9377659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9377659","identity":"rs-9377659","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}