Assessing dental students’ empathy through experimental economics: a complementary approach

Assessing dental students’ empathy through experimental economics: a complementary approach | 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 Assessing dental students’ empathy through experimental economics: a complementary approach Mamadou NDOYE, Ismaël RAFAI, Bruno VENTELOU, Laurence LUPI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6735083/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Empathy is crucial in dentistry, influencing both the quality of care and the practitioner-patient relationship. However, traditional methods of assessing empathy, such as self-assessments, are often biased by social desirability and fail to capture observable behaviours. In response to these limitations, behavioural economics proposes using experimental games to measure social preferences such as altruism and cooperation, which are linked to empathy. This study aims to evaluate dental students’ psychosocial competencies by combining traditional tools such as the Jefferson scale and Objective Structured Clinical Examinations (OSCEs), with experimental economic games. The study also explores the impact of the recent French reform of access to health studies on the psychological profiles of recruited students. Methods One hundred and fifty fourth-year students at the Faculty of Dentistry at the Université Côte d’Azur, participated over three academic years (2022–2025). They were assessed using a self-report questionnaire (Jefferson scale), an external evaluation during OSCEs, and participation in economic games simulating social dilemmas. Results There was no correlation between Jefferson scale scores and external assessments (OSCE and CARE grids), but there was a positive correlation between OSCE and CARE scores. The economic games revealed that students from the post-reform pathway exhibited greater cooperation in the Public Good Game than those from the former pathway. However, no significant effect was found on neither Jefferson scores nor on OSCE scores. Conclusion Combining these three tools provides a more comprehensive assessment of students' psychosocial skills. Although the economic games are less correlated with other tools, they reveal stable personality traits and avoid social desirability biases. These findings suggest that educational reforms can lead to subtle yet meaningful changes in students' prosocial behaviours, with implications for training future healthcare professionals. dental students economic games empathy admission pathway Jefferson scale Figures Figure 1 INTRODUCTION Empathy is a fundamental skill in dentistry, directly influencing the quality of care and the practitioner-patient relationship (Stepien 2005). The ADEE recognises "professionalism," particularly the ability to communicate well with patients, as a core element of dental education programmes in the European Union (ADEE website). Empathetic practitioners are better able to understand their patients’ expectations and concerns, thereby fostering effective communication and appropriate care (PMETB 2008, Bellet PS 1991, Hojat 2002). Within the framework of dental education, evaluating and enhancing this competency is essential to promote safe, patient-centered clinical practice (Simpson, 1991; Stewart, 1995; Squiers, 1990). In line with this goal, the 2020 reform of admissions to health education programmes in France aimed to diversify student profiles by selecting the more empathetic candidates through admission processes that consider more than just academic achievement. There are numerous methods of evaluating empathy including self-assessments, patient evaluations, peer assessments, psychometric tests and behavioural observations, each of which has its own advantages and disadvantages (Hemmerdinger 2007). However, measuring empathy among dental students presents a significant methodological challenge. The Jefferson Scale of Physician Empathy (JSPE) is the only scale designed specifically to measure practitioner empathy (Hojat 2001, Hojat 2009). These traditional assessment methods, which rely on self-questionnaires, are widely used but they have several limitations. Social desirability can lead to an overestimation of empathetic skills. Furthermore, these tools struggle to capture the behavioural dimension of empathy, focusing primarily on subjective perceptions. Although clinical observation is closer to real-life practice, it is difficult to implement and is subject to evaluators’ interpretative biases. In response to these limitations, alternative approaches have emerged, particularly from the field of behavioural economics. Behavioural economics assumes that individuals’ decisions can be represented as cost-benefit trade-offs (in a broad sense), in which their psychological orientation is expressed before the choice is made. While empathy is not a concept that economics directly considers, certain economic behaviours allow for the indirect assessment of prosocial aptitudes, such as altruism and cooperation—traits that are intrinsically linked to empathy. In economics, these are referred to as individuals’ “social preferences.” "Social preferences" refers to a set of psychological characteristics that are specific to the individual and operate before decision-making occurs. These traits are thought to structurally and systematically influence all cost-benefit trade-offs an individual must make when faced with social dilemmas, such as whether to act generously or not and whether to engage in collaborative efforts. From a data perspective, experimental economics — the empirical branch of behavioural economics — has developed several tools to measure social preferences through economic games (Murphy 2011). These methods present participants with complex and engaging decision-making dilemmas that reveal their underlying social preferences. Participants are placed in real and controlled decision-making environments where individual interests may conflict with collective interests, and the choices they make in these environments reveal their propensity for altruism or cooperation (Murphy 2011, Hennig-Schmidt 2014). Such tools are already being used in a variety of contexts, particularly by human resources professionals and government agencies, to evaluate individuals' psychosocial abilities during recruitment and throughout their careers. The most commonly used tools include serious games, virtual reality games, and experimental economic games (Galois-Faurie 2014). Two predominant games are particularly relevant for assessing psychosocial skills: the Social Value Orientation (SVO) tool, which measures altruism (Murphy 2011), and the Public Goods Game, which evaluates the capacity for cooperation (Ledyard 1994). The latter is frequently introduced in economics curricula to illustrate the challenges of cooperation (Ventelou 2001). A key feature of these tools is the presence of incentives, whereby participants’ decisions in economic games have real (primarily monetary) consequences for themselves and others. This incentivised design is central to the internal and external validity of experimental economics tools. This must be accompanied by a clear and comprehensive understanding of the link between decisions and outcomes, through precise game instructions. As these decisions have tangible consequences (e.g. a few euros or minutes of effort), they are considered to reflect stakes that extend beyond the game itself (external validity) and are also assumed to reveal participants’ preferences authentically (internal validity), as these decisions are in their best interests. Consequently, the common problem of socially desirable responses in surveys is, in principle, avoided. In this study, to gain a more comprehensive understanding insight into the psychological profiles of dental students in their caregiving roles, we are combining the Jefferson Scale (a self-assessment of empathy) and OSCEs (observer-assessed empathy using the CARE measure) with economic games involving monetary stakes. These games will allow us to explore key dimensions such as prosociality, cooperation, and decision-making under uncertainty. They provide complementary insight into students’ dispositions towards patients, particularly with regards to the economic and financial inherent in healthcare systems, especially fee-for-service models. Therefore, this study examines the relevance of experimental economic games for assessing the psychosocial competencies of dental students, alongside more traditional measures, in order to better understand how empathy develops in a healthcare context that includes economic considerations. A secondary objective is to assess the impact of recent admissions reforms to health studies on the psychological profiles of students admitted to dental school. Materials and Methods Participants and context To compare the results of three methods for assessing dental students’ psychosocial competencies, with a particular focus on empathic behaviour, a three-year study was conducted. All fourth-year students enrolled at the Faculty of Dentistry of the Université Côte d’Azur during the academic years 2022–2023, 2023–2024, and 2024–2025 (n = 150) were randomly assigned a unique identifier, known only to them. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki for research involving human subjects. Approval was received from the university’s ethics committee (CER #2022-049). For each academic year, the study took place in two phases. The first phase took place during a health economics seminar, and the second phase during two OSCE stations. Students were offered the opportunity to participate in the study after receiving detailed explanations, both orally and in writing, and after providing written informed consent. It was clearly stated that participation was voluntary and could be discontinued at any time without justification. Session 1: Self-Reported Empathy and Economic Games Students were first invited to complete a sociodemographic questionnaire. For the self-assessment of empathy, we used the Jefferson Scale of Physician Empathy – Student Version (JSPE-S), which is the most widely used tool for assessing empathy in health professions students who are not medical doctors. The French version of this scale was validated in 2012 (Zenasni 2012). The JSPE is a standardised 20-item questionnaire that evaluates perceived (self-reported) clinical empathy. A higher score is associated with higher levels of clinical empathy. This questionnaire, developed by Hojat et al., has been validated and is specifically designed for individuals undergoing healthcare training (Hojat 2001, Hojat 2009). Responses are given on a 7-point Likert scale ranging from 1 = "strongly disagree" to 7 = "strongly agree". The three subcomponents of the scale — Perspective Taking (PT), Compassionate Care (CC), and Standing in the Patient’s Shoes (SPS) — are each assessed using seven items on a 5-point Likert scale, ranging from 1 = “does not describe me well” to 5 = “describes me very well”. During the same session, students participated in experimental economic tasks aimed at revealing their social preferences. They were placed in gamified decision-making scenarios (dilemmas) involving payoffs for themselves and for others. They were informed that their choices would have real consequences — in other words, monetary rewards would be associated with their decisions. As such, the decisions made in this exercise are intended to reveal participants’ actual preferences. We drew inspiration from existing behavioural economics literature, which proposes specific frameworks for each dimension of social preferences. Students accessed the O-TREE platform to complete four games. These experimental tasks, conducted in four sequential phases, elicited four key dimensions of economic and social preferences: 1/ risk aversion including a measure of aversion to ambiguity (Charness G & Gneezy 2010), 2/ time preferences (Andreoni J & Sprenger 2012), with the first trade-off being a choice between receiving a payment either now or in one month’s time (T1), and a second trade-off being a choice between receiving a payment either in one month’s time or two months’ time(T2), 3/ cooperation ability (a traditional public goods game, involving four players), and 4/ prosociality - using the SVO tool-. (Murphy 2011). Session 2: A Dual External Perspective — Measuring Observed Empathy and OSCE Stations The external assessment of empathy took place two months later, during the Objective Structured Clinical Examinations (OSCEs), which were conducted solely for formative and research purposes. Empathy was evaluated using the Consultation and Relational Empathy (CARE) measure, which was selected for its proven validity and reliability (Mercer 2004). The OSCE stations simulated two clinical situations that explicitly examined interactional aspects related to communication and patient engagement. One scenario focused on managing a patient's resistance when requesting an antibiotic prescription for irreversible pulpitis that was not medically necessary, while the other addressed managing a patient’s unrealistic expectations when presenting with gingival recession and visible metal margins on maxillary incisors. The stations were carefully designed to enable clear and unambiguous assessment of the targeted skills. Each OSCE station was overseen by a single experienced examiner. Before each OSCE session, the examiners met to align their assessment criteria using a customised evaluation grid specific to each task. Thus, two complementary approaches were used to assess each student: 1) an "objective" evaluation of the student's empathy by the faculty examiner, using the OSCE assessment grid, and 2) a simulated patient’s evaluation, using the CARE scale, developed by Mercer et al. (Mercer 2004), which captures the quality of the clinical relationship from the patient’s perspective. The CARE measure consists of ten items rated on a five-point Likert scale, from 1 = "strongly agree" to 5 = "strongly disagree." Notably, lower scores reflect higher perceived empathy. Data analysis Data analysis was performed using the O-TREE software. Items were recoded after data entry. Categorical variables are reported as counts and expressed as percentages. Continuous variables are presented as means with their corresponding standard deviations. Comparisons were conducted using a Student’s t-test. The distribution properties of each item were checked for the analysis. Pearson’s bivariate correlations (r) were used to compare data from the Jefferson Scale, the CARE measure, and the economic games. The threshold for statistical significance was set at 5% (p < 0.05). Results Sample characteristics: Out of the 167 enrolled students, 150 participated in the study (89.8% participation rate). The majority of respondents were female (71.3%) with a mean age of 21.9 ± 1.73 years. In most cases, only one parent had attained a level of education higher than a bachelor's degree (62.2%). The detailed results are presented in Table 1 . Table 1 Descriptive statistics by 4th-year Promotion (over 3 years) Promo A (2022) Promo B (2023) Promo C (2024) All Mean SD Mean SD Mean SD Mean SD Jefferson Questionnaire Global Score 112.175 9.128 113.267 8.238 108.316 11.801 111.191 10.070 Sub-scale PT 57.575 6.413 58.483 5.583 55.474 7.541 57.159 6.651 Sub-scale CC 46.975 4.417 47.650 3.644 46.070 4.935 46.904 4.372 Sub-scale SPS 7.625 3.019 7.133 2.062 6.772 2.291 7.127 2.425 Economic Games Risk 10.308 5.606 9.150 6.145 10.172 4.860 9.815 5.554 Ambiguity 9.615 5.847 7.600 5.921 8.207 5.991 8.325 5.943 T1 7.923 7.481 9.033 6.322 7.293 6.486 8.115 6.686 T2 7.641 7.617 8.633 6.979 8.483 7.153 8.331 7.170 SVO 23.806 13.720 22.182 16.222 25.502 14.493 23.812 14.974 Cooperation 11.385 5.495 10.383 5.518 11.621 5.782 11.089 5.604 OSCE/CARE OSCE_1 15.304 3.076 17.644 3.204 16.934 3.124 16.735 3.259 OSCE_2 17.913 3.993 16.915 3.013 17.852 2.664 17.536 3.215 CARE_1 35.261 7.561 36.576 8.184 36.902 6.722 36.331 7.485 CARE_2 35.804 8.828 39.102 5.965 40.066 6.539 38.542 7.237 Global OSCE score 16.609 2.925 17.280 2.386 17.393 2.396 17.136 2.556 Global CARE score 35.533 7.238 37.839 5.894 38.484 4.304 37.437 5.891 Demography %Male 0.295 0.462 0.283 0.454 0.281 0.453 0.286 0.295 Age 21.864 1.456 21.883 1.795 21.860 1.695 21.870 21.864 % ”with honors” 0.455 0.504 0.317 0.469 0.482 0.504 0.412 0.455 0 parent > High School diploma 0.051 0.223 0.167 0.376 0.105 0.310 0.115 0.051 ≥ 1 parent ≥ Bachelor degree 0.692 0.468 0.583 0.497 0.614 0.491 0.622 0.692 2 parents ≥ Bachelor degree 0.308 0.468 0.233 0.427 0.404 0.495 0.314 0.308 Factors influencing the choice of practice location Taking over a practice 1.841 1.033 2.050 0.872 1.982 0.744 1.969 1.841 Proximity to family 1.705 0.954 1.850 0.971 1.895 0.976 1.826 1.705 Proximity to place of training 3.045 1.099 3.150 0.988 3.281 0.940 3.168 3.045 Availability of patients 2.909 1.137 2.267 0.861 2.754 0.830 2.615 2.909 Local amenities 1.750 1.123 1.683 0.911 1.667 0.809 1.696 1.750 Financial incentives 2.841 1.256 2.467 1.016 2.667 0.970 2.640 2.841 Caption table 1: This table presents the results of the Jefferson Scale of Empathy (self-reported empathy), economic game outcomes (prosocial behaviors), performance scores in simulated clinical consultations, and socio-demographic characteristics of 4th-year dental students over a three-year period. Regarding the factors that may influence students’ choice of practice location, proximity to the training site, availability of healthcare services and access to financial aid appear to be among the least influential factors overall, although financial aid may be more effective for older students (see Table 1 ). The effect of the admission reform did not impact students’ choices regarding practice location (Table 4 ). Table 4 Linear regressions of each economic game based on the entry pathway and demographic variables. Dependent variable : Risk Ambiguity T1 T2 Cooperation SVO (1) (2) (3) (4) (5) (6) LAS (ref PACES & PASS) 0.075 -0.642 -1.955 * -0.767 1.902 ** 2.580 (0.901) (0.979) (1.090) (1.153) (0.895) (2.466) age 0.393 0.029 -0.228 -0.774 ** -0.045 0.433 (0.272) (0.296) (0.329) (0.348) (0.270) (0.745) Male 1.522 -1.347 -1.710 -1.192 1.585 1.504 (1.027) (1.116) (1.242) (1.314) (1.021) (2.811) With Honors 0.514 -0.311 -0.962 -0.089 -1.267 -2.069 (0.954) (1.037) (1.154) (1.221) (0.948) (2.612) Constant 0.472 8.499 14.955 ** 25.992 *** 11.273 * 13.343 (6.035) (6.561) (7.299) (7.722) (5.998) (16.518) Observations 155 155 155 155 155 155 R 2 0.032 0.013 0.043 0.046 0.058 0.018 Adjusted R 2 0.007 -0.013 0.017 0.021 0.032 -0.008 Residual Std. Error (df = 150) 5.510 5.990 6.665 7.050 5.477 15.082 F Statistic (df = 4; 150) 1.259 0.503 1.679 1.808 2.290 * 0.676 *p<0.1; **p<0.05; ***p<0.01 Caption table 4: This table presents the linear regressions of each economic game based on the entry pathway and demographic variables. The results show that students from the new pathway are more cooperative (p = 0.035) than those from the former pathway. Jefferson Scores (Self-Assessment of Empathy): Overall, the mean empathy score was 111.19 ± 10.07. The subscores for the three dimensions were as follows: Perspective Taking: 57.16 ± 6.65, Compassionate Care: 46.9 ± 4.37, and Standing in the Patient’s Shoes: 7.13 ± 2.43. Female students had non-significantly higher empathy scores than male students, and no correlation was found between empathy scores and age (Table 1 ). While all three distributions peak within the normal range for health professions students (100–115), the relative frequencies and dispersion suggest a shift in empathy profiles, evolving from a uniform level in 2022 to a more dispersed, potentially bimodal pattern in 2023 and 2024 (Fig. 1 ). Double external evaluation of empathy (empathy assessment by OSCE observers and simulated patients – CARE measure) Descriptive Results and Empathy Assessment The descriptive results of the scales used, as well as the students’ performance in the OSCE, from the perspectives of both simulated patients (CARE) and examiners (OSCE scores), are also presented in Table 1 . To what extent is the level of empathy perceived by evaluators correlated with students’ self-assessed attitudes? There was no correlation between the OSCE evaluation form scores (completed by external examiners) and those from the JSPE self-assessment scale scores (Table 3 ). Similarly, there was no correlation between CARE scores (completed by simulated patients) and self-assessed empathy using the Jefferson Scale. However, CARE and OSCE scores were positively correlated (see Table 3 ). Contribution of Economic Games When comparing students from the original “PACES” track (cohort A), comparable to the “PASS” track of cohort B, with those from the new LAS admission pathway in cohorts B and C, it was found that the reform of access to dental studies (PACES vs. LAS) had no impact on Jefferson empathy scores (Table 2 ), or OSCE results, whether assessed by external examiners or simulated patients (Table 2 ). Table 2 Descriptive statistics of students by selection method: former pathway (unique and written results) vs new pathway (multiple-entry and oral integration) PACES LAS Difference (2022–2023) (2023–2024) Mean SD Mean SD t p value Jefferson Questionnaire Global Score 112.487 8.731 109.911 11.146 -1.613 0.109 Sub-scale PT 57.833 5.803 56.494 7.371 -1.266 0.207 Sub-scale CC 47.308 4.122 46.506 4.596 -1.150 0.252 Sub-scale SPS 7.346 2.573 6.911 2.266 -1.123 0.263 Economic Games Risk 9.688 5.820 9.938 5.319 0.280 0.780 Ambiguity 8.662 5.721 8 6.169 -0.698 0.486 T1 9.182 6.926 7.088 6.321 -1.977 0.050 T2 8.649 7.172 8.025 7.201 -0.544 0.587 SVO 22.587 14.746 24.990 15.188 1.006 0.316 Cooperation 10.312 5.322 11.838 5.797 1.719 0.088 OSCE/CARE OSCE_1 16.361 3.402 17.108 3.084 1.492 0.140 OSCE_2 17.361 3.757 17.711 2.573 0.699 0.486 CARE_1 35.880 7.814 36.783 7.160 0.777 0.438 CARE_2 37.470 8.093 39.614 6.128 1.925 0.056 Global OSCE score 16.861 2.801 17.410 2.269 1.386 0.168 Global CARE score 36.675 6.921 38.199 4.556 1.676 0.096 Demography % Male 0.280 0.452 0.291 0.457 0.149 0.882 Age 21.939 1.452 21.797 1.863 -0.536 0.592 With Honor 0.329 0.473 0.500 0.503 2.209 0.029 0 parent > High School diploma 0.104 0.307 0.127 0.335 0.441 0.660 ≥ 1 parent ≥ Bachelor degree 0.610 0.491 0.633 0.485 0.288 0.774 2 parents ≥ Bachelor degree 0.247 0.434 0.380 0.488 1.799 0.074 Factors influencing the choice of practice location Taking over a practice 1.841 1.033 2.050 0.872 1.982 0.744 Proximity to family 1.705 0.954 1.850 0.971 1.895 0.976 Proximity to place of training 3.à45 1.099 3.150 0.988 3.281 0.940 Availability of patients 2.909 1.137 2.267 0.861 2.754 0.830 Local amenities 1.750 1.123 1.683 0.911 1.667 0.809 Financial incentives 2.841 1.256 2.467 1.016 2.667 0.970 Caption Table 2 : This table presents the descriptive statistics of students, grouped by selection method: the former pathway, where admission was solely based on written results, and the new pathway, which incorporates multiple-entry and an oral assessment. The data allows for a comparison of the performance and characteristics of students from these two selection systems. Following the reform of health studies admissions, a single, unified entry pathway was replaced by multiple selection routes. This table displays the distribution of empathy-related measures across different student groups, according to their entry route into health studies following the national reform of the selection process. However, the economic games revealed that students from the new LAS pathway were more cooperative in the Public Good Game (Cooperation variable) than those from the PACES pathway (Table 2 , p = 0.5; further supported by Table IV, showing a linear regression analysis, p = 0.035). DISCUSSION Convergence and Divergence of the Tools The results show that the self-assessed empathy score using the Jefferson Scale was equivalent to the score found at the University of Lorraine (Haddad 2023), suggesting a good self-awareness of empathy. Findings from several studies support the integration of virtual patient-based placement models into allied health training programmes for communication skills training (Quail 2016). However, the use of economic games in this context has been rare and has not previously been applied to dental students (Hennig-Schmidt 2014). The correlation matrix clearly indicates that the various tools measuring students’ prosociality are consistent within a given range, but complementary outside of it (Table 3 ). Nevertheless, it is the economic games that most effectively revealed the impact of the reforms on students’ psychological profiles when admitted to dental studies. The divergence between the different results obtained using the various methods can be explained by the nature of the competencies being measured. Specifically, self-assessment captures awareness of competencies, while external assessment captures observable manifestations in a clinical setting, and economic games capture stable behavioural preferences, which are often linked to personality and are less influenced by the educational environment. Accordingly, it seems that economic games seem particularly relevant for: objectifying behaviours: Unlike self-assessments, they are less susceptible to social desirability biases (Bardey et al. 2021). identifying individual profiles: They allow atypical students to be identified, who may require tailored pedagogical support (Hennig-Schmidt, 2014). predicting professional behaviours: Previous studies have shown that the preferences revealed through these games can predict the quality of professional practices (Massin et al., 2018). This study aimed to test the feasibility of using a combination of self-reported scales (Jefferson), simulated clinical examinations (OSCE) and economic games to assess the prosociality of dental surgery students. Results from three successive cohorts show that this combination of tools is both operational and reveals complementary dimensions of prosociality. The Jefferson questionnaire scores demonstrate good internal consistency between subdimensions (r > 0.7 between PT and CC), confirming the robustness of the tool as a self-reported measure of empathy. Similarly, the OSCEs and CARE grids scores are strongly correlated (r = 0.79 for both OSCE exercises and r = 0.69 between OSCE and CARE), confirming the stability of simulated clinical observation as a measure of behavioural empathy. The correlation between Jefferson scores and OSCE (r = 0.637; p < 0.001) indicates that students who self-report as empathetic are also perceived as such in simulated situations. This validates the complementarity of the two evaluation methods in capturing empathy within an initial training curriculum. However, the economic games used (time preferences, risk aversion, cooperation, and prosocial orientation) did not show any significant correlations with empathy scores (Jefferson or OSCE). However, this dissociation should not be interpreted as a failure of the tool; rather, it demonstrates the ability of economic games to capture deeper and more reliable (or less manipulable?) dimensions of prosociality. They specifically allow behaviours to be objectified more effectively, freeing them from the social desirability bias inherent in traditional questionnaires (Bardey et al. 2021). Thus, their low correlation with the other tools strengthens their value in a comprehensive evaluation approach. It is also important to note that the economic dimension (here captured by the explicit use of monetary incentives) is an extremely present element in any case in the caregiver-patient relationship, particularly in France, where the private sector accounts for almost 90% of primary care provision. One of the major contributions of this study is the experimentation with a combined method that integrates three complementary types of tools. This triangulation allows us to overcome the limitations inherent in each method when used in isolation. The Jefferson questionnaire, although widely validated, relies on introspective responses. The OSCEs simulate relevant clinical situations but they are structured and codified. The economic games test individual choices in an experimentally controlled context, revealing preferences that are assumed to be structurally upstream of the cost-benefit trade-offs in social action. Using these methods together allows us to create of a more comprehensive map of the prosocial skills of future healthcare providers. It also helps to document the economic behaviours of healthcare professionals, particularly those of a commercial and financial nature. The impact of these behaviours on medical choices is now widely acknowledged by the medical community itself (Hemenway 1990). Finally, this work takes place in a the context of a reform in to entry to health studies (REES). In France, for example, two main admission pathways have replaced the former common first year (PACES) since the 2020 reform of access to health studies. The Specific Health Access Pathway (Parcours d’Accès Spécifique Santé, PASS) and the Health Access Bachelor’s Program (Licence Accès Santé, LAS). The PASS is a first-year programme primarily focused on health-related subjects with a minor in another discipline, while the LAS allows students to pursue a regular bachelor’s degree in a non-health discipline (e.g. law, biology, or humanities) alongside an additional health-related module. Both pathways offer the opportunity to apply for entry into medical, dental, pharmacy, or midwifery schools, depending on academic performance and success in additional soft-skills assessments. The reform explicitly aims to transform the profiles of those recruited to health studies, particularly through the creation of the LAS pathway. However, the current results do not indicate any significant changes in sociodemographic profiles (gender, social background and academic performance) or professional aspirations. Nevertheless, some initial indications emerge: students on the LAS pathway demonstrate greater cooperation and slightly increased prosociality in games (as measured by the SVO tool), as well as establishing a more authentic clinical relationship in the OSCEs. These modest but consistent effects suggest a gradual rather than dramatic transformation. They align with analyses in educational policy evaluation which state that reform does not immediately produce significant effects, but may generate subtle shifts in collective trajectories. Abbreviations ADEE: Association for Dental Education in Europe JSPE: Jefferson Scale of Physician Empathy (PT: Perspective Taking; CC: Compassionate Care; SPS: Standing in the Patient’s Shoes) OSCEs: Objective Structured Clinical Examinations CARE scale: Consultation and Relational Empathy scale SVO: Social Value Orientation tool CER: Comité d’Ethique de la Recherche PACES: Première Année Commune aux Etudes de Santé (Common First Year for Health Studies) PASS: Parcours d’Accès Spécifique Santé (Specific Health Access Pathway) LAS: Licence Accès Santé (Health Access Bachelor’s Programme) Declarations Ethics approval and consent to participate This study received approval from the Ethics Committee of Université Côte d’Azur. All participating students signed an informed consent form, which is kept on file at the Department of Oral Public Health.” Consent for publication All participants were fully informed about the objectives, procedures, and potential uses of the study results, including the possibility of publication in scientific journals. Each participant voluntarily agreed to take part in the research and signed an informed consent form prior to participation. By signing the consent form, participants also gave their explicit permission for the anonymized data collected during the study to be published. No personally identifiable information is included in the published material. Availability of data and materials The datasets generated and analysed during the current study are not publicly available due to institutional data protection policies and the presence of potentially identifiable information. However, anonymised data may be made available from the corresponding author upon reasonable request and with prior approval from the Department of Oral Public Health at Côte d’Azur University. Competing interests The authors declare that they have no financial or non-financial competing interests that could have influenced the conduct or conclusions of this study. Funding This study was funded by the ROSAM project (agreement number: IReSP-LI-VENTELOU-AAP-18-HSR-008), which provided financial support for the students to participate in the economic games. Authors’ contributions LL and BV initiated the project team and designed and managed the research project. MN drafted the manuscript for submission. IR oversaw the setup of the experience, and provided the specific support, completing the data entry and analysis. BV and LL ensured the scientific rigour of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank Professor Mohammadreza Hojat for generously granting them permission to use the Jefferson Scale of Empathy free of charge. His contribution enabled us to assess self-reported empathy in our study, adding significant value to our research. Authors’ information MN is a dental student who completed a dual degree in Dentistry and Political Science. This study formed the basis of his doctoral thesis. IR is a research fellow at the CNRS in Toulouse. BV is a research professor at the CNRS at the Aix-Marseille School of Economics. He specialises in health economics and public health and is a member of the High Council for Public Health. BV’s research laboratory received funding as part of the ‘France 2030’ investment plan, which is managed by the French National Research Agency (reference: ANR-17-EURE-0020), as well as from the Aix-Marseille University Initiative of Excellence – A*MIDEX. LL is a professor of oral public health and the dean of the Faculty of Dental Surgery at Université Côte d’Azur. 1 Cote d’Azur University, UFR Odontologie, Pôle universitaire Saint Jean d’Angély, 5 avenue du 22ème BCA, 06300 Nice, France 2 AMU-AMSE 5-9 Boulevard Maurice Bourdet, CS 50498 13205 Marseille Cedex 1 – [email protected] 3 Université Toulouse Capitole, CNRS, Toulouse School of Management, Toulouse School of Economics 1 Esp. de l’Université 31000 Toulouse. References Stepien KA, Baernstein A: Educating for empathy. J Gen Intern Med 2005, 21:524–530. https://adee.org/graduating-european-dentist/graduating-european-dentist-curriculum/professionalism Postgraduate Medical Education and Training Board: Educating Tomorrow’s Doctors – Future Models of Medical Training; Medical Workforce Shape and Training Expectations. London: PMETB; 2008 Bellet PS, Maloney MJ: The importance of empathy as an interviewing skill in medicine. JAMA 1991, 266:1831–1832. 10. Hojat M, Gonella JS, Mangione S, Nasca TJ, Veloski JJ, Erdmann JB, Callahan CA, Magee M: Empathy in medical students as compared to academic performance, clinical competence and gender. Med Educ 2002, 36:522–527 Simpson M, Buckman R, Stewart M, et al. Doctor-patient communication: the Toronto statement. Br Med J 1991: 303: 1385–1387. Stewart M. Effective physician-patient communication and health outcomes: a review. CMAJ 1995: 152: 1423–1433. Squiers RW. A model of empathetic understanding and adherence to treatment regimens in practitioner-patient relationships. Soc Sci Med 1990: 30: 325–339. Hemmerdinger J, Stoddart S, Lilford R. A systematic review of test of empathy in medicine. BMC Med Educ 2007: 7: 24–29. Hojat M, Mangione S, Nasca TJ, et al. The Jefferson Scale of Physician Empathy: development and preliminary psychometric data. Educ Psychol Meas 2001: 61: 349–365. Hojat M, Vergare M, Maxwell K, et al. The devil is in the third year: a longitudinal study of erosion of empathy in medical school. Acad Med 2009: 84: 1182–1191. Murphy R O, Ackermann K A, Handgraaf MJJ. Measuring Social Value Orientation (SVO). JDM, 2011; 6, 771-78. Hennig-Schmidt H, Wiesen D. Other-regarding behavior and motivation in health care provision: an experiment with medical and non-medical students. Soc Sci Med. 2014 May; 108:156-65. doi: 10.1016/j.socscimed.2014.03.001. Epub 2014 Mar 3. PMID: 24641880. Galois-Faurie I, Lacroux A. « Serious games» et recrutement : quels enjeux de recherche en gestion des ressources humaines ? @ GRH , 2014 ;10(1), 11-35. Ledyard, J. O. (1994). Public goods: A survey of experimental research. Division of the Humanities and Social Sciences, California Inst. of Technology . Ventelou B. (2001). Au-delà de la rareté : la croissance économique comme construction sociale. Albin Michel. Zenasni F, Boujut E, Bluffel du Vaure C, Catu-Pinault A, Tavani JL, Rigal L, Jaury P, Sultan S. Development of a French-language version of the Jefferson Scale of Physician Empathy and association with practice characteristics and burnout in a sample of General Practitioners. Int J Pers Cent Med. 2012; 2 (4): 759-766. Charness G, Gneezy U. Portfolio choice and Risk Attitudes: an experiment. Econ Inq 2010 January; Vol. 48, No. 1, 133–146. Andreoni J, Sprenger C. Risk Preferences Are Not Time Preferences. American Econ Rev 2012; 102(7): 3357–76. Mercer SW, Maxwell M, Heaney D, Watt GC. The consultation and relational empathy (CARE) measure: Development and preliminary validation and reliability of an empathy-based consultation process measure. Fam Pract. 2004 Dec;21(6):699-705. Haddad YG, Sturzu L, Bisch M, Yasukawa K, Baudet A. Empathy of dental students and educators in French hospitals: A cross-sectional study. Nurs Health Sci. 2023 Jun;25(2):209-215. doi: 10.1111/nhs.13017. Epub 2023 Apr 12. PMID: 37045795. Quail M, Brundage SB, Spitalnick J, Allen PJ, Beilby J. Student self-reported communication skills, knowledge and confidence across standardized patient, virtual and traditional clinical learning environments. BMC Med Educ. 2016 Feb 27; 16:73. doi: 10.1186/s12909-016-0577-5. PMID: 26919838; PMCID: PMC4769506 Bardey D, Kembou S, Ventelou B. Physicians’ incentives to adopt personalized medicine: experimental evidence. J Econ Behav Organ. 2021; 191, 686-713. Massin S, Nebout A, Ventelou B. Predicting medical practices using various risk attitude measures. Eur J Health Econ. 2018 Jul;19(6):843-860. doi: 10.1007/s10198-017-0925-3. Epub 2017 Aug 31. PMID: 28861629. Hemenway D, Killen A, Cashman SB, Parks CL, Bicknell WJ. Physicians' responses to financial incentives. Evidence from a for-profit ambulatory care center. N Engl J Med. 1990 Apr 12;322(15):1059-63. doi: 10.1056/NEJM199004123221507. PMID: 2320066. Table Table 3 is available in the Supplementary Files section Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6735083","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479867852,"identity":"fa61855c-c764-4fcc-a4f0-aa1d66ff8483","order_by":0,"name":"Mamadou NDOYE","email":"","orcid":"","institution":"Université Côte d'Azur","correspondingAuthor":false,"prefix":"","firstName":"Mamadou","middleName":"","lastName":"NDOYE","suffix":""},{"id":479867853,"identity":"61762055-60cf-48e1-8f69-91fdc860b8b3","order_by":1,"name":"Ismaël RAFAI","email":"","orcid":"","institution":"Toulouse 1 Capitole University","correspondingAuthor":false,"prefix":"","firstName":"Ismaël","middleName":"","lastName":"RAFAI","suffix":""},{"id":479867855,"identity":"6c673321-4267-4913-bf40-8546dda62291","order_by":2,"name":"Bruno VENTELOU","email":"","orcid":"","institution":"Aix-Marseille University","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"","lastName":"VENTELOU","suffix":""},{"id":479867856,"identity":"f829f57e-5a00-4842-8bde-22bbaf24363d","order_by":3,"name":"Laurence LUPI","email":"data:image/png;base64,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","orcid":"","institution":"Université Côte d'Azur","correspondingAuthor":true,"prefix":"","firstName":"Laurence","middleName":"","lastName":"LUPI","suffix":""}],"badges":[],"createdAt":"2025-05-23 18:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6735083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6735083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86132261,"identity":"20aac3ac-5b74-4069-bc93-b65283b55c16","added_by":"auto","created_at":"2025-07-07 06:57:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of\u003c/strong\u003e \u003cstrong\u003eJefferson Empathy scores\u003c/strong\u003e \u003cstrong\u003eby 4th-year Promotion (2022 in blue, 2023 in red, 2024 in green)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCaption figure 1: This histogram illustrates the distribution of self-reported empathy scores (Jefferson Scale of Empathy) among 4th-year dental students across three academic cohorts. Each color represents a different promotion year, highlighting variations in score distributions over time. The 2023 cohort (in red) appears to have slightly higher empathy scores overall compared to 2022 (blue) and 2024 (green), as reflected in the distribution’s shift toward the right. The 2024 cohort (green) displays a wider distribution, suggesting more variability in students’ empathy levels. This could be due to increased diversity in admission pathways (post-reform), leading to a more heterogeneous student population in terms of interpersonal skills and background.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6735083/v1/2cb13d6b2b6a968619e29c99.png"},{"id":86132469,"identity":"e33059d6-2c14-4bc4-a0b5-8687c2c24a42","added_by":"auto","created_at":"2025-07-07 07:05:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1327105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6735083/v1/486a4dca-c53e-4b43-aec7-449266a93f30.pdf"},{"id":86131316,"identity":"616fb828-d50d-43be-b62d-6d77897294e0","added_by":"auto","created_at":"2025-07-07 06:49:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21021,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6735083/v1/1731f567a78db0b588b3deba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing dental students’ empathy through experimental economics: a complementary approach","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEmpathy is a fundamental skill in dentistry, directly influencing the quality of care and the practitioner-patient relationship (Stepien 2005). The ADEE recognises \"professionalism,\" particularly the ability to communicate well with patients, as a core element of dental education programmes in the European Union (ADEE website). Empathetic practitioners are better able to understand their patients\u0026rsquo; expectations and concerns, thereby fostering effective communication and appropriate care (PMETB 2008, Bellet PS 1991, Hojat 2002). Within the framework of dental education, evaluating and enhancing this competency is essential to promote safe, patient-centered clinical practice (Simpson, 1991; Stewart, 1995; Squiers, 1990). In line with this goal, the 2020 reform of admissions to health education programmes in France aimed to diversify student profiles by selecting the more empathetic candidates through admission processes that consider more than just academic achievement.\u003c/p\u003e \u003cp\u003eThere are numerous methods of evaluating empathy including self-assessments, patient evaluations, peer assessments, psychometric tests and behavioural observations, each of which has its own advantages and disadvantages (Hemmerdinger 2007). However, measuring empathy among dental students presents a significant methodological challenge. The Jefferson Scale of Physician Empathy (JSPE) is the only scale designed specifically to measure practitioner empathy (Hojat 2001, Hojat 2009). These traditional assessment methods, which rely on self-questionnaires, are widely used but they have several limitations. Social desirability can lead to an overestimation of empathetic skills. Furthermore, these tools struggle to capture the behavioural dimension of empathy, focusing primarily on subjective perceptions. Although clinical observation is closer to real-life practice, it is difficult to implement and is subject to evaluators\u0026rsquo; interpretative biases.\u003c/p\u003e \u003cp\u003eIn response to these limitations, alternative approaches have emerged, particularly from the field of behavioural economics. Behavioural economics assumes that individuals\u0026rsquo; decisions can be represented as cost-benefit trade-offs (in a broad sense), in which their psychological orientation is expressed before the choice is made. While empathy is not a concept that economics directly considers, certain economic behaviours allow for the indirect assessment of prosocial aptitudes, such as altruism and cooperation\u0026mdash;traits that are intrinsically linked to empathy. In economics, these are referred to as individuals\u0026rsquo; \u0026ldquo;social preferences.\u0026rdquo; \"Social preferences\" refers to a set of psychological characteristics that are specific to the individual and operate before decision-making occurs. These traits are thought to structurally and systematically influence all cost-benefit trade-offs an individual must make when faced with social dilemmas, such as whether to act generously or not and whether to engage in collaborative efforts. From a data perspective, experimental economics \u0026mdash; the empirical branch of behavioural economics \u0026mdash; has developed several tools to measure social preferences through economic games (Murphy 2011). These methods present participants with complex and engaging decision-making dilemmas that reveal their underlying social preferences. Participants are placed in real and controlled decision-making environments where individual interests may conflict with collective interests, and the choices they make in these environments reveal their propensity for altruism or cooperation (Murphy 2011, Hennig-Schmidt 2014).\u003c/p\u003e \u003cp\u003eSuch tools are already being used in a variety of contexts, particularly by human resources professionals and government agencies, to evaluate individuals' psychosocial abilities during recruitment and throughout their careers. The most commonly used tools include serious games, virtual reality games, and experimental economic games (Galois-Faurie 2014). Two predominant games are particularly relevant for assessing psychosocial skills: the Social Value Orientation (SVO) tool, which measures altruism (Murphy 2011), and the Public Goods Game, which evaluates the capacity for cooperation (Ledyard 1994). The latter is frequently introduced in economics curricula to illustrate the challenges of cooperation (Ventelou 2001). A key feature of these tools is the presence of incentives, whereby participants\u0026rsquo; decisions in economic games have real (primarily monetary) consequences for themselves and others. This incentivised design is central to the internal and external validity of experimental economics tools. This must be accompanied by a clear and comprehensive understanding of the link between decisions and outcomes, through precise game instructions. As these decisions have tangible consequences (e.g. a few euros or minutes of effort), they are considered to reflect stakes that extend beyond the game itself (external validity) and are also assumed to reveal participants\u0026rsquo; preferences authentically (internal validity), as these decisions are in their best interests. Consequently, the common problem of socially desirable responses in surveys is, in principle, avoided.\u003c/p\u003e \u003cp\u003e In this study, to gain a more comprehensive understanding insight into the psychological profiles of dental students in their caregiving roles, we are combining the Jefferson Scale (a self-assessment of empathy) and OSCEs (observer-assessed empathy using the CARE measure) with economic games involving monetary stakes. These games will allow us to explore key dimensions such as prosociality, cooperation, and decision-making under uncertainty. They provide complementary insight into students\u0026rsquo; dispositions towards patients, particularly with regards to the economic and financial inherent in healthcare systems, especially fee-for-service models. Therefore, this study examines the relevance of experimental economic games for assessing the psychosocial competencies of dental students, alongside more traditional measures, in order to better understand how empathy develops in a healthcare context that includes economic considerations. A secondary objective is to assess the impact of recent admissions reforms to health studies on the psychological profiles of students admitted to dental school.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and context\u003c/h2\u003e \u003cp\u003eTo compare the results of three methods for assessing dental students\u0026rsquo; psychosocial competencies, with a particular focus on empathic behaviour, a three-year study was conducted. All fourth-year students enrolled at the Faculty of Dentistry of the Universit\u0026eacute; C\u0026ocirc;te d\u0026rsquo;Azur during the academic years 2022\u0026ndash;2023, 2023\u0026ndash;2024, and 2024\u0026ndash;2025 (n\u0026thinsp;=\u0026thinsp;150) were randomly assigned a unique identifier, known only to them.\u003c/p\u003e \u003cp\u003e The study was conducted in accordance with the ethical principles of the Declaration of Helsinki for research involving human subjects. Approval was received from the university\u0026rsquo;s ethics committee (CER #2022-049). For each academic year, the study took place in two phases. The first phase took place during a health economics seminar, and the second phase during two OSCE stations. Students were offered the opportunity to participate in the study after receiving detailed explanations, both orally and in writing, and after providing written informed consent. It was clearly stated that participation was voluntary and could be discontinued at any time without justification.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSession 1: Self-Reported Empathy and Economic Games\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStudents were first invited to complete a sociodemographic questionnaire.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor the self-assessment of empathy, we used the Jefferson Scale of Physician Empathy \u0026ndash; Student Version (JSPE-S), which is the most widely used tool for assessing empathy in health professions students who are not medical doctors. The French version of this scale was validated in 2012 (Zenasni 2012). The JSPE is a standardised 20-item questionnaire that evaluates perceived (self-reported) clinical empathy. A higher score is associated with higher levels of clinical empathy. This questionnaire, developed by Hojat et al., has been validated and is specifically designed for individuals undergoing healthcare training (Hojat 2001, Hojat 2009). Responses are given on a 7-point Likert scale ranging from 1 = \"strongly disagree\" to 7 = \"strongly agree\". The three subcomponents of the scale \u0026mdash; Perspective Taking (PT), Compassionate Care (CC), and Standing in the Patient\u0026rsquo;s Shoes (SPS) \u0026mdash; are each assessed using seven items on a 5-point Likert scale, ranging from 1 = \u0026ldquo;does not describe me well\u0026rdquo; to 5 = \u0026ldquo;describes me very well\u0026rdquo;.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDuring the same session, students participated in experimental economic tasks aimed at revealing their social preferences. They were placed in gamified decision-making scenarios (dilemmas) involving payoffs for themselves and for others. They were informed that their choices would have real consequences \u0026mdash; in other words, monetary rewards would be associated with their decisions. As such, the decisions made in this exercise are intended to reveal participants\u0026rsquo; actual preferences. We drew inspiration from existing behavioural economics literature, which proposes specific frameworks for each dimension of social preferences. Students accessed the O-TREE platform to complete four games. These experimental tasks, conducted in four sequential phases, elicited four key dimensions of economic and social preferences: 1/ risk aversion including a measure of aversion to ambiguity (Charness G \u0026amp; Gneezy 2010), 2/ time preferences (Andreoni J \u0026amp; Sprenger 2012), with the first trade-off being a choice between receiving a payment either now or in one month\u0026rsquo;s time (T1), and a second trade-off being a choice between receiving a payment either in one month\u0026rsquo;s time or two months\u0026rsquo; time(T2), 3/ cooperation ability (a traditional public goods game, involving four players), and 4/ prosociality - using the SVO tool-. (Murphy 2011).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eSession 2: A Dual External Perspective — Measuring Observed Empathy and OSCE Stations\u003c/h3\u003e\n\u003cp\u003eThe external assessment of empathy took place two months later, during the Objective Structured Clinical Examinations (OSCEs), which were conducted solely for formative and research purposes. Empathy was evaluated using the Consultation and Relational Empathy (CARE) measure, which was selected for its proven validity and reliability (Mercer 2004). The OSCE stations simulated two clinical situations that explicitly examined interactional aspects related to communication and patient engagement. One scenario focused on managing a patient's resistance when requesting an antibiotic prescription for irreversible pulpitis that was not medically necessary, while the other addressed managing a patient\u0026rsquo;s unrealistic expectations when presenting with gingival recession and visible metal margins on maxillary incisors. The stations were carefully designed to enable clear and unambiguous assessment of the targeted skills. Each OSCE station was overseen by a single experienced examiner.\u003c/p\u003e \u003cp\u003eBefore each OSCE session, the examiners met to align their assessment criteria using a customised evaluation grid specific to each task. Thus, two complementary approaches were used to assess each student: 1) an \"objective\" evaluation of the student's empathy by the faculty examiner, using the OSCE assessment grid, and 2) a simulated patient\u0026rsquo;s evaluation, using the CARE scale, developed by Mercer et al. (Mercer 2004), which captures the quality of the clinical relationship from the patient\u0026rsquo;s perspective. The CARE measure consists of ten items rated on a five-point Likert scale, from 1 = \"strongly agree\" to 5 = \"strongly disagree.\" Notably, lower scores reflect higher perceived empathy.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using the O-TREE software. Items were recoded after data entry. Categorical variables are reported as counts and expressed as percentages. Continuous variables are presented as means with their corresponding standard deviations. Comparisons were conducted using a Student\u0026rsquo;s t-test. The distribution properties of each item were checked for the analysis. Pearson\u0026rsquo;s bivariate correlations (r) were used to compare data from the Jefferson Scale, the CARE measure, and the economic games. The threshold for statistical significance was set at 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSample characteristics:\u003c/h2\u003e\n \u003cp\u003eOut of the 167 enrolled students, 150 participated in the study (89.8% participation rate).\u003c/p\u003e\n \u003cp\u003eThe majority of respondents were female (71.3%) with a mean age of 21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73 years.\u003c/p\u003e\n \u003cp\u003eIn most cases, only one parent had attained a level of education higher than a bachelor\u0026apos;s degree (62.2%). The detailed results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive statistics by 4th-year Promotion (over 3 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePromo A (2022)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePromo B (2023)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePromo C (2024)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eJefferson Questionnaire\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-scale PT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-scale CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-scale SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic Games\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbiguity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCooperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSCE/CARE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSCE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSCE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal OSCE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal CARE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% \u0026rdquo;with honors\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 parent\u0026thinsp;\u0026gt;\u0026thinsp;High School diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1 parent\u0026thinsp;\u0026ge;\u0026thinsp;Bachelor degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 parents\u0026thinsp;\u0026ge;\u0026thinsp;Bachelor degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors influencing the choice of practice location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaking over a practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximity to family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximity to place of training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvailability of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal amenities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial incentives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eCaption table 1:\u003c/strong\u003e This table presents the results of the Jefferson Scale of Empathy (self-reported empathy), economic game outcomes (prosocial behaviors), performance scores in simulated clinical consultations, and socio-demographic characteristics of 4th-year dental students over a three-year period.\u003c/p\u003e\n \u003cp\u003eRegarding the factors that may influence students\u0026rsquo; choice of practice location, proximity to the training site, availability of healthcare services and access to financial aid appear to be among the least influential factors overall, although financial aid may be more effective for older students (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The effect of the admission reform did not impact students\u0026rsquo; choices regarding practice location (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regressions of each economic game based on the entry pathway and demographic variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e:\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbiguity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCooperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAS (ref PACES \u0026amp; PASS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.955\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.902\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.774\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.270)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.811)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith Honors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.955\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.992\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.273\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.299)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(16.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual Std. Error (df\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF Statistic (df\u0026thinsp;=\u0026thinsp;4; 150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.290\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e*p\u0026lt;0.1; **p\u0026lt;0.05; ***p\u0026lt;0.01\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eCaption table 4:\u003c/strong\u003e This table presents the linear regressions of each economic game based on the entry pathway and demographic variables. The results show that students from the new pathway are more cooperative (p = 0.035) than those from the former pathway.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eJefferson Scores (Self-Assessment of Empathy):\u003c/h3\u003e\n\u003cp\u003eOverall, the mean empathy score was 111.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07. The subscores for the three dimensions were as follows: Perspective Taking: 57.16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65, Compassionate Care: 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37, and Standing in the Patient\u0026rsquo;s Shoes: 7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43.\u003c/p\u003e\n\u003cp\u003eFemale students had non-significantly higher empathy scores than male students, and no correlation was found between empathy scores and age (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). While all three distributions peak within the normal range for health professions students (100\u0026ndash;115), the relative frequencies and dispersion suggest a shift in empathy profiles, evolving from a uniform level in 2022 to a more dispersed, potentially bimodal pattern in 2023 and 2024 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDouble external evaluation of empathy (empathy assessment by OSCE observers and simulated patients \u0026ndash; CARE measure)\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eDescriptive Results and Empathy Assessment\u003c/h3\u003e\n\u003cp\u003eThe descriptive results of the scales used, as well as the students\u0026rsquo; performance in the OSCE, from the perspectives of both simulated patients (CARE) and examiners (OSCE scores), are also presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTo what extent is the level of empathy perceived by evaluators correlated with students\u0026rsquo; self-assessed attitudes?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no correlation between the OSCE evaluation form scores (completed by external examiners) and those from the JSPE self-assessment scale scores (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, there was no correlation between CARE scores (completed by simulated patients) and self-assessed empathy using the Jefferson Scale. However, CARE and OSCE scores were positively correlated (see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eContribution of Economic Games\u003c/h2\u003e\n \u003cp\u003eWhen comparing students from the original \u0026ldquo;PACES\u0026rdquo; track (cohort A), comparable to the \u0026ldquo;PASS\u0026rdquo; track of cohort B, with those from the new LAS admission pathway in cohorts B and C, it was found that the reform of access to dental studies (PACES vs. LAS) had no impact on Jefferson empathy scores (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), or OSCE results, whether assessed by external examiners or simulated patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of students by selection method: former pathway (unique and written results) vs new pathway (multiple-entry and oral integration)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePACES\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLAS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2022\u0026ndash;2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(2023\u0026ndash;2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eJefferson Questionnaire\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-scale PT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-scale CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSub-scale SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic Games\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbiguity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSVO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCooperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSCE/CARE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSCE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOSCE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARE_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARE_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal OSCE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal CARE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemography\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith Honor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 parent\u0026thinsp;\u0026gt;\u0026thinsp;High School diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;1 parent\u0026thinsp;\u0026ge;\u0026thinsp;Bachelor degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 parents\u0026thinsp;\u0026ge;\u0026thinsp;Bachelor degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors influencing the choice of practice location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaking over a practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximity to family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProximity to place of training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.\u0026agrave;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAvailability of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal amenities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial incentives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eCaption\u003c/strong\u003e Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e: This table presents the descriptive statistics of students, grouped by selection method: the former pathway, where admission was solely based on written results, and the new pathway, which incorporates multiple-entry and an oral assessment. The data allows for a comparison of the performance and characteristics of students from these two selection systems. Following the reform of health studies admissions, a single, unified entry pathway was replaced by multiple selection routes. This table displays the distribution of empathy-related measures across different student groups, according to their entry route into health studies following the national reform of the selection process.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eHowever, the economic games revealed that students from the new LAS pathway were more cooperative in the Public Good Game (Cooperation variable) than those from the PACES pathway (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.5; further supported by Table IV, showing a linear regression analysis, p\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConvergence and Divergence of the Tools\u003c/h2\u003e \u003cp\u003eThe results show that the self-assessed empathy score using the Jefferson Scale was equivalent to the score found at the University of Lorraine (Haddad 2023), suggesting a good self-awareness of empathy. Findings from several studies support the integration of virtual patient-based placement models into allied health training programmes for communication skills training (Quail 2016). However, the use of economic games in this context has been rare and has not previously been applied to dental students (Hennig-Schmidt 2014). The correlation matrix clearly indicates that the various tools measuring students\u0026rsquo; prosociality are consistent within a given range, but complementary outside of it (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Nevertheless, it is the economic games that most effectively revealed the impact of the reforms on students\u0026rsquo; psychological profiles when admitted to dental studies. The divergence between the different results obtained using the various methods can be explained by the nature of the competencies being measured. Specifically, self-assessment captures awareness of competencies, while external assessment captures observable manifestations in a clinical setting, and economic games capture stable behavioural preferences, which are often linked to personality and are less influenced by the educational environment. Accordingly, it seems that economic games seem particularly relevant for:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eobjectifying behaviours: Unlike self-assessments, they are less susceptible to social desirability biases (Bardey et al. 2021).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eidentifying individual profiles: They allow atypical students to be identified, who may require tailored pedagogical support (Hennig-Schmidt, 2014).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003epredicting professional behaviours: Previous studies have shown that the preferences revealed through these games can predict the quality of professional practices (Massin et al., 2018).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis study aimed to test the feasibility of using a combination of self-reported scales (Jefferson), simulated clinical examinations (OSCE) and economic games to assess the prosociality of dental surgery students. Results from three successive cohorts show that this combination of tools is both operational and reveals complementary dimensions of prosociality. The Jefferson questionnaire scores demonstrate good internal consistency between subdimensions (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7 between PT and CC), confirming the robustness of the tool as a self-reported measure of empathy. Similarly, the OSCEs and CARE grids scores are strongly correlated (r\u0026thinsp;=\u0026thinsp;0.79 for both OSCE exercises and r\u0026thinsp;=\u0026thinsp;0.69 between OSCE and CARE), confirming the stability of simulated clinical observation as a measure of behavioural empathy.\u003c/p\u003e \u003cp\u003eThe correlation between Jefferson scores and OSCE (r\u0026thinsp;=\u0026thinsp;0.637; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) indicates that students who self-report as empathetic are also perceived as such in simulated situations. This validates the complementarity of the two evaluation methods in capturing empathy within an initial training curriculum.\u003c/p\u003e \u003cp\u003eHowever, the economic games used (time preferences, risk aversion, cooperation, and prosocial orientation) did not show any significant correlations with empathy scores (Jefferson or OSCE). However, this dissociation should not be interpreted as a failure of the tool; rather, it demonstrates the ability of economic games to capture deeper and more reliable (or less manipulable?) dimensions of prosociality. They specifically allow behaviours to be objectified more effectively, freeing them from the social desirability bias inherent in traditional questionnaires (Bardey et al. 2021). Thus, their low correlation with the other tools strengthens their value in a comprehensive evaluation approach. It is also important to note that the economic dimension (here captured by the explicit use of monetary incentives) is an extremely present element in any case in the caregiver-patient relationship, particularly in France, where the private sector accounts for almost 90% of primary care provision.\u003c/p\u003e \u003cp\u003eOne of the major contributions of this study is the experimentation with a combined method that integrates three complementary types of tools. This triangulation allows us to overcome the limitations inherent in each method when used in isolation.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe Jefferson questionnaire, although widely validated, relies on introspective responses.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe OSCEs simulate relevant clinical situations but they are structured and codified.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe economic games test individual choices in an experimentally controlled context, revealing preferences that are assumed to be structurally upstream of the cost-benefit trade-offs in social action.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUsing these methods together allows us to create of a more comprehensive map of the prosocial skills of future healthcare providers. It also helps to document the economic behaviours of healthcare professionals, particularly those of a commercial and financial nature. The impact of these behaviours on medical choices is now widely acknowledged by the medical community itself (Hemenway 1990).\u003c/p\u003e \u003cp\u003eFinally, this work takes place in a the context of a reform in to entry to health studies (REES). In France, for example, two main admission pathways have replaced the former common first year (PACES) since the 2020 reform of access to health studies. The Specific Health Access Pathway (Parcours d\u0026rsquo;Acc\u0026egrave;s Sp\u0026eacute;cifique Sant\u0026eacute;, PASS) and the Health Access Bachelor\u0026rsquo;s Program (Licence Acc\u0026egrave;s Sant\u0026eacute;, LAS). The PASS is a first-year programme primarily focused on health-related subjects with a minor in another discipline, while the LAS allows students to pursue a regular bachelor\u0026rsquo;s degree in a non-health discipline (e.g. law, biology, or humanities) alongside an additional health-related module. Both pathways offer the opportunity to apply for entry into medical, dental, pharmacy, or midwifery schools, depending on academic performance and success in additional soft-skills assessments. The reform explicitly aims to transform the profiles of those recruited to health studies, particularly through the creation of the LAS pathway. However, the current results do not indicate any significant changes in sociodemographic profiles (gender, social background and academic performance) or professional aspirations. Nevertheless, some initial indications emerge: students on the LAS pathway demonstrate greater cooperation and slightly increased prosociality in games (as measured by the SVO tool), as well as establishing a more authentic clinical relationship in the OSCEs.\u003c/p\u003e \u003cp\u003eThese modest but consistent effects suggest a gradual rather than dramatic transformation. They align with analyses in educational policy evaluation which state that reform does not immediately produce significant effects, but may generate subtle shifts in collective trajectories.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADEE: Association for Dental Education in Europe\u003c/p\u003e\n\u003cp\u003eJSPE: Jefferson Scale of Physician Empathy (PT: Perspective Taking; CC: Compassionate Care; SPS: Standing in the Patient’s Shoes)\u003c/p\u003e\n\u003cp\u003eOSCEs: Objective Structured Clinical Examinations\u003c/p\u003e\n\u003cp\u003eCARE scale: Consultation and Relational Empathy scale\u003c/p\u003e\n\u003cp\u003eSVO: Social Value Orientation tool\u003c/p\u003e\n\u003cp\u003eCER: Comité d’Ethique de la Recherche\u003c/p\u003e\n\u003cp\u003ePACES: Première Année Commune aux Etudes de Santé (Common First Year for Health Studies)\u003c/p\u003e\n\u003cp\u003ePASS: Parcours d’Accès Spécifique Santé (Specific Health Access Pathway)\u003c/p\u003e\n\u003cp\u003eLAS: Licence Accès Santé (Health Access Bachelor’s Programme)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Ethics Committee of Universit\u0026eacute; C\u0026ocirc;te d\u0026rsquo;Azur. All participating students signed an informed consent form, which is kept on file at the Department of Oral Public Health.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were fully informed about the objectives, procedures, and potential uses of the study results, including the possibility of publication in scientific journals. Each participant voluntarily agreed to take part in the research and signed an informed consent form prior to participation. By signing the consent form, participants also gave their explicit permission for the anonymized data collected during the study to be published. No personally identifiable information is included in the published material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to institutional data protection policies and the presence of potentially identifiable information. However, anonymised data may be made available from the corresponding author upon reasonable request and with prior approval from the Department of Oral Public Health at C\u0026ocirc;te d\u0026rsquo;Azur University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or non-financial competing interests that could have influenced the conduct or conclusions of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the ROSAM project (agreement number: IReSP-LI-VENTELOU-AAP-18-HSR-008), which provided financial support for the students to participate in the economic games.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLL and BV initiated the project team and designed and managed the research project. MN drafted the manuscript for submission. IR oversaw the setup of the experience, and provided the specific support, completing the data entry and analysis. BV and LL ensured the scientific rigour of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Professor Mohammadreza Hojat for generously granting them permission to use the Jefferson Scale of Empathy free of charge. His contribution enabled us to assess self-reported empathy in our study, adding significant value to our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMN\u0026nbsp;\u003c/strong\u003eis a dental student who completed a dual degree in Dentistry and Political Science. This study formed the basis of his doctoral thesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIR\u0026nbsp;\u003c/strong\u003eis a research fellow at the CNRS in Toulouse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBV\u0026nbsp;\u003c/strong\u003eis a research professor at the CNRS at the Aix-Marseille School of Economics. He specialises in health economics and public health and is a member of the High Council for Public Health.\u003c/p\u003e\n\u003cp\u003eBV\u0026rsquo;s research laboratory received funding as part of the \u0026lsquo;France 2030\u0026rsquo; investment plan, which is managed by the French National Research Agency (reference: ANR-17-EURE-0020), as well as from the Aix-Marseille University Initiative of Excellence \u0026ndash; A*MIDEX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLL\u0026nbsp;\u003c/strong\u003eis a professor of oral public health and the dean of the Faculty of Dental Surgery at Universit\u0026eacute; C\u0026ocirc;te d\u0026rsquo;Azur.\u003c/p\u003e\n\u003cp\u003e1 Cote d\u0026rsquo;Azur University, UFR Odontologie, P\u0026ocirc;le universitaire Saint Jean d\u0026rsquo;Ang\u0026eacute;ly, 5 avenue du 22\u0026egrave;me BCA, 06300 Nice, France\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2 AMU-AMSE 5-9 Boulevard Maurice Bourdet, CS 50498 13205 Marseille Cedex 1 \u0026ndash; [email protected]\u003c/p\u003e\n\u003cp\u003e3 Universit\u0026eacute; Toulouse Capitole, CNRS, Toulouse School of Management, Toulouse School of Economics 1 Esp. de l\u0026rsquo;Universit\u0026eacute; 31000 Toulouse.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eStepien KA, Baernstein A: Educating for empathy. 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The consultation and relational empathy (CARE) measure: Development and preliminary validation and reliability of an empathy-based consultation process measure. Fam Pract. 2004 Dec;21(6):699-705.\u003c/li\u003e\n \u003cli\u003eHaddad YG, Sturzu L, Bisch M, Yasukawa K, Baudet A. Empathy of dental students and educators in French hospitals: A cross-sectional study. Nurs Health Sci. 2023 Jun;25(2):209-215. doi: 10.1111/nhs.13017. Epub 2023 Apr 12. PMID: 37045795.\u003c/li\u003e\n \u003cli\u003eQuail M, Brundage SB, Spitalnick J, Allen PJ, Beilby J. Student self-reported communication skills, knowledge and confidence across standardized patient, virtual and traditional clinical learning environments. BMC Med Educ. 2016 Feb 27; 16:73. doi: 10.1186/s12909-016-0577-5. PMID: 26919838; PMCID: PMC4769506\u003c/li\u003e\n \u003cli\u003eBardey D, Kembou S, Ventelou B. Physicians\u0026rsquo; incentives to adopt personalized medicine: experimental evidence. J Econ Behav Organ. 2021; 191, 686-713.\u003c/li\u003e\n \u003cli\u003eMassin S, Nebout A, Ventelou B. Predicting medical practices using various risk attitude measures. Eur J Health Econ. 2018 Jul;19(6):843-860. doi: 10.1007/s10198-017-0925-3. Epub 2017 Aug 31. PMID: 28861629.\u003c/li\u003e\n \u003cli\u003eHemenway D, Killen A, Cashman SB, Parks CL, Bicknell WJ. Physicians\u0026apos; responses to financial incentives. Evidence from a for-profit ambulatory care center. N Engl J Med. 1990 Apr 12;322(15):1059-63. doi: 10.1056/NEJM199004123221507. PMID: 2320066.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section\u003c/p\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":"dental students, economic games, empathy, admission pathway, Jefferson scale","lastPublishedDoi":"10.21203/rs.3.rs-6735083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6735083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEmpathy is crucial in dentistry, influencing both the quality of care and the practitioner-patient relationship. However, traditional methods of assessing empathy, such as self-assessments, are often biased by social desirability and fail to capture observable behaviours. In response to these limitations, behavioural economics proposes using experimental games to measure social preferences such as altruism and cooperation, which are linked to empathy. This study aims to evaluate dental students\u0026rsquo; psychosocial competencies by combining traditional tools such as the Jefferson scale and Objective Structured Clinical Examinations (OSCEs), with experimental economic games. The study also explores the impact of the recent French reform of access to health studies on the psychological profiles of recruited students.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e One hundred and fifty fourth-year students at the Faculty of Dentistry at the Universit\u0026eacute; C\u0026ocirc;te d\u0026rsquo;Azur, participated over three academic years (2022\u0026ndash;2025). They were assessed using a self-report questionnaire (Jefferson scale), an external evaluation during OSCEs, and participation in economic games simulating social dilemmas.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThere was no correlation between Jefferson scale scores and external assessments (OSCE and CARE grids), but there was a positive correlation between OSCE and CARE scores. The economic games revealed that students from the post-reform pathway exhibited greater cooperation in the Public Good Game than those from the former pathway. However, no significant effect was found on neither Jefferson scores nor on OSCE scores.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCombining these three tools provides a more comprehensive assessment of students' psychosocial skills. Although the economic games are less correlated with other tools, they reveal stable personality traits and avoid social desirability biases. These findings suggest that educational reforms can lead to subtle yet meaningful changes in students' prosocial behaviours, with implications for training future healthcare professionals.\u003c/p\u003e","manuscriptTitle":"Assessing dental students’ empathy through experimental economics: a complementary approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 06:48:58","doi":"10.21203/rs.3.rs-6735083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"44246822842667348437217318627044853666","date":"2026-05-05T11:44:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T09:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43718064219439489471169245194763364323","date":"2025-07-04T14:35:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-02T12:38:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-27T04:48:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-03T08:20:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-31T18:06:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-05-31T18:03:16+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":"6a05249d-4640-4527-a470-30334ce46094","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T06:48:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-07 06:48:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6735083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6735083","identity":"rs-6735083","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}