Trajectories of medical students’ empathy nowadays: A longitudinal study using a comprehensive framework of empathy

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For more than a decade, the literature has been dominated by the notion that medical students may paradoxically lose their empathy during medical school. However, medical curricula have significantly evolved, and the question is whether this is still the case. The present study aimed to describe the trajectories of different dimensions of empathy from the beginning to the end of a six-year medical curriculum and explore the influence of different psychosocial and health-related factors. Methods . In an open cohort design, all medical students at the University of Lausanne (Switzerland) were invited to complete four waves of yearly online questionnaires. Cognitive, affective, and behavioral empathy were measured with three validated instruments, and emotion recognition was assessed with a performance test. For each measure, linear mixed models including an array of psychosocial and health-related potential covariates were modelled. Different temporal variance-covariance structures and nonlinear trajectories were tested. Results . The final sample included 3224 questionnaires completed by 1667 medical students. The cognitive and affective dimensions of empathy significantly increased in the first half of medical school, followed by a plateau, whereas behavioral empathy remained stable. For emotion recognition, a significant linear increase was observed. The only covariate with substantial influence was gender: students identifying as male presented similar trajectories of empathy and emotion recognition but with overall lower scores than students identifying as female or nonbinary. Conclusions . This study revealed significant increases in cognitive empathy, affective empathy, and emotion recognition. Developments in today’s medical curricula may have contributed to the observed increase in empathy. Future multisite studies are warranted to identify the features of the educational environment that impact the trajectories of empathy during medical school. Medical students empathy emotion recognition longitudinal study Figures Figure 1 Introduction Since the seminal work of Hojat and colleagues [ 1 ] in 2009, the decrease in empathy during medical school has been widely broadcasted and the belief that medical students “lose their empathy” during medical school has become well rooted in both the medical and medical education communities. However, after more than a decade of medical curriculum improvement worldwide regarding the relational and communicational aspects of patient encounters [ 2 , 3 ], the time might have come to reexamine the trajectories of empathy during medical school. In 2020, a systematic review reported that, among the 24 cross-sectional and 6 longitudinal studies identified, 14 reported a significant decrease in empathy during medical school, whereas the remaining 16 reported an increase, stability, or mixed results [ 4 ]. Some authors have attributed these mixed findings to geo-sociocultural factors. A review reported that US studies mostly reported small but significant decreases in empathy, whereas Far Eastern studies mostly reported small but significant increases in empathy [ 5 ]. Another explanation could be the instruments used to measure empathy. A meta-analysis indeed revealed that changes in empathy were significant only when they were measured with the Jefferson Scale of Physician Empathy (JSPE [ 6 ]) [ 7 ]. One study even reported opposing trajectories when different instruments were used. Smith et al. [ 8 ] indeed reported a decline in empathy previously described when the JSPE was used but reported a significant increase in empathy when measured with the Questionnaire of Cognitive and Affective Empathy (QCAE [ 9 ]). Also, using task-based measures in addition to self-report questionnaires, their results revealed that medical students’ ability to recognize others’ emotional states and their sensitivity to facial expressions increased during medical school [ 8 ]. It is important to consider the well-recognized multidimensionality of empathy. Most authors agree that it encompasses at least a cognitive and an affective component, which are, respectively, the ability to recognize and understand others’ emotions by taking their perspective for cognitive empathy and a resonance with or microcontagion of others’ feelings for affective empathy [ 10 ]. Other authors add to these two dimensions a behavioral component of empathy, which is the ability to act accurately on the basis of one’s understanding of others’ emotions [ 11 ]. Using the Interpersonal Reactivity Index (IRI [ 12 ]), a multidimensional measure of empathy, Quince et al. [ 13 ] reported that different dimensions of empathy can follow different trajectories during medical school. They indeed reported a statistically significant decline in the affective dimension of empathy, whereas the cognitive dimension of empathy remained stable [ 13 ]. They also observed that this decline in affective empathy was only significant for male students, thus highlighting the importance of accounting for both the multidimensionality of empathy and the impact of potentially influencing factors. Several factors have been shown to influence empathy and its trajectories, such as gender [ 1 , 4 , 14 ], specialty choice [ 1 , 4 , 14 ], social support [ 14 ], and psychological well-being [ 14 – 19 ]. To obtain a better understanding of empathy trajectories during medical school, these factors need to be controlled for. Moreover, identifying influencing factors would provide a better understanding of individual differences in empathy trajectories and may offer avenues for interventions to prevent empathy decline during medical school. The aim of the present study was to describe the trajectories of different dimensions of empathy over the full period of medical school and explore the influence of different psychosocial and health factors on these trajectories. This study sought to build on the same strengths as previous work [ 8 , 13 ] by adopting a comprehensive framework of empathy with validated self-report questionnaires of the cognitive, affective, and behavioral components of empathy as well as a test of emotion recognition to include a measure that does not rely on self-reports. Moreover, it complements existing literature by (1) covering an entire six-year curriculum, (2) including a large sample, (3) formally testing possible nonlinear trajectories of empathy, and (4) investigating the influence of psychosocial and health-related factors. Such studies revisiting empathy trajectories during medical school are currently needed to maybe start deconstructing the notion of medical students’ empathy loss. Indeed, medical curricula constantly evolve, and the efforts made during the last two decades to improve communication skills training [ 2 , 3 ] might hopefully have impacted the development of empathy during medical school. Methods Design This longitudinal study used data collected in an open cohort design for the ETMED-L project [ 20 ]. In each of the four data collection waves (March 2021, November 2021, November 2022, and November 2023), medical students from all curriculum years (1 to 6) were included (except for the last data collection wave, in which first-year students were excluded to prioritize multiple participation). Linear Mixed Models (LMMs) were applied because they effectively accommodate limited and discontinuous data, providing a reliable way to estimate the longitudinal trajectories of empathy across the complete six years of medical education, even if assessments were conducted at a maximum of four time points for each participant. Data Collection At each of the four data collection waves, an online questionnaire investigating mental health and interpersonal competence was sent to all matriculated medical students at the University of Lausanne (Switzerland), except for the students who were at the university as part of an academic exchange. It took approximately one hour to complete the online questionnaire fully, and the students received 50 CHF (~ 50 USD) for each completed questionnaire. The Research Ethics Committee of the canton of Vaud approved the project (project number 2020–02474), and all participants provided written informed consent. At the end of the questionnaire, contact information for mental health help services was provided if students felt the need to seek support. Measures Empathy and emotion recognition To adopt a more comprehensive framework of empathy, we used five measures from three validated instruments as well as an emotion recognition test described in more detail in the published study protocol [ 20 ]. First, we assessed empathy with the widely used Jefferson Scale of Physician Empathy - Student Version (JSPE-S [ 6 ]), which was developed to measure medical students’ orientations or attitudes toward empathic relationships in the context of patient care [ 6 ]. Second, the validated French version of the Questionnaire of Cognitive and Affective Empathy (QCAE [ 9 , 21 ]) was used as a more multidimensional measure of empathy assessing separately cognitive and affective empathy. For assessing behavioural empathy, the observation of interactions with patients would have been ideal, but these kinds of data would have been difficult to obtain. After careful examination of existing self-reported instruments, we found that the validated French version of the Ability to Modify Self-Presentation Scale (AMSP [ 22 , 23 ]) could be a proxy to measure a behavioral dimension of empathy, as it assesses one’s ability to modify their behaviours according to the social situation at hand. Finally, we used the Geneva Emotion Recognition Test – Short Form (GERT-S [ 24 ]) to measure emotion recognition accuracy. Indeed, empathy has traditionally been assessed through self-report questionnaires, but there are also well-validated performance-based tasks that evaluate the ability to recognize emotions in individuals depicted in pictures or short videos [ 25 ]. The GERT-S presents 42 video clips of actors expressing one out of 14 emotions while saying pseudolinguistic sentences. The final score of the test is the number of emotions correctly recognized by the participants. These types of emotion recognition tasks have been found to have a significant, though modest, correlation with self-reported measures of both cognitive and affective empathy (Murphy and Lilienfeld, 2019), suggesting that while the ability to recognize others' emotions is linked to both understanding and resonating with others' emotions, they also capture aspects of emotional processing that go beyond these dimensions. Psychosocial and health-related covariates Four sociological covariates were analysed: identifying as male (gender identity recoded into 1 = male and 2 = female or nonbinary), parents’ education (number of parents with a college or university degree), having a partner (1 = yes, 0 = no), and social support. Social support was measured as the average score of two questions adapted from the Swiss Household Panel survey [ 26 ]: “If necessary, in your opinion, to what extent can someone provide you with practical help, this means concrete help or useful advice, if 0 means "not at all" and 10 "a great deal"?” for practical support and “To what extent can someone be available in case of need and show understanding, by talking with you for example, if 0 means "not at all" and 10 "a great deal"?” for emotional support. Regarding psychological covariates , having consulted a psychotherapist during the past year (1 = yes, 0 = no) and coping strategies were assessed. The French version of the coping section of the Euronet questionnaire [ 27 , 28 ] was used to measure three types of coping strategies [ 29 ]: emotion-focused , problem-focused , and help-seeking . Moreover, mental health and burnout indicators were included. For mental health, the Center for Epidemiological Studies-Depression [ 30 ] was used to measure depression symptoms , and the State-Trait Anxiety Inventory [ 31 ] was used for anxiety symptoms . Additionally, the emotional exhaustion , cynicism , and academic efficacy (reversed) dimensions of burnout were assessed with the Maslach Burnout Inventory Student-Survey [ 32 ]. Finally, two health-related covariates were included: physical activity (number of hours per week) and satisfaction with one’s own health (“Are you satisfied with your health?” rated on a scale from 1 = very unsatisfied to 5 = very satisfied). Statistical Analysis To investigate the longitudinal trajectories of empathy during medical school, separate LMMs were modelled for each empathy and emotion recognition measure, with curriculum year and covariates as fixed effects. Specifically, the fixed effects account for the systematic variation associated with changes across the curriculum years and control for potential confounding factors. Additionally, random intercepts were incorporated at the student level to account for the correlation between repeated measures within the same student while considering the inherent variability between students. The restricted maximum likelihood method was used to produce unbiased estimates of variance and covariance parameters [ 33 , 34 ]. Furthermore, two different temporal variance-covariance structures (Autoregressive Covariance Structure of AR1 and Autoregressive/Moving Average Covariance Structure of ARMA1.1) were tested to potentially account for the temporal spillover of empathy and emotion recognition, with the best fitting model being selected via likelihood ratio tests. The nonlinear trajectories of empathy and emotion recognition were subsequently tested by including the time as quadratic or cubic. The best-fitting model was selected via likelihood ratio tests and presented as the final model. For all the final models, we report marginal R 2 as the percentage of variation explained by the fixed part of the model, conditional R 2 as the percentage of variation explained by both the fixed and random parts of the model, and the intraclass correlation coefficient (ICC) as the proportion of total between-student variance. Moreover, standardized β values are presented for effect size estimation. Standardized βs between 0.10–0.29 are considered small, those between 0.30–0.49 are considered medium, and those 0.50 or greater are considered large effect sizes [ 35 ]. To explore significant differences between specific time points, pairwise comparisons to each preceding year were additionally conducted with Holm-Bonferroni corrections to account for multiple comparisons. However, unlike LMMs, these comparisons do not account for the correlation between repeated measures within the same student and are thus a less accurate representation of longitudinal trajectories. The highest missing rate at the item level was 0.59% (AMSP items). It has been shown that much gain from multiple imputation is unlikely when missing rates are lower than 5% [ 36 ]. Thus, missing data at the item level were replaced by mean scores if less than 20% of the items were missing. If 20% or more of the items were missing, the total score was considered missing, and missing data at the score level were then handled with full information maximum likelihood in the LMMs. Stata version 17 [ 37 ] and R version 4.2.2 [ 38 ] were used for the analyses, and p values < .05 were considered significant. Results Sample Figure 1 presents the flow chart of the study participation. After exclusion of the students who gave a wrong answer to at least one of the two attention questions (e.g., “In order to check your attention, please answer ‘Slightly agree’ to this question.”), scored implausibly low on the GERT-S, or encountered technical issues, each data collection wave included respectively 885, 1032, 1049, and 812 answered questionnaires, which represented 49.36%, 57.56%, 58.51%, and 45.29% of each wave’s eligible students. From those, we excluded 169 repeat students to have only linear curriculum trajectories as well as 15 students who had missing data on all the variables of interest across all the data collection waves. Consequently, the final sample included 1667 medical students who filled in a total of 3224 questionnaires. [Figure 1 about here] The medical students in our final sample were between 17 and 49 years old, with a mean age of 21.80 years (standard deviation = 3.09) at first participation. Among them, 67.19% self-identified as female, 0.84% self-identified as nonbinary, and 31.97% as male, which corresponds to the gender proportions generally observed in the Lausanne Medical School. Half of the medical students had a partner (52.37%), and the majority had one (21.66%) or two (53.57%) parents with higher education (college or university diploma). An important proportion of the students, 23.88%, indicated having consulted a psychotherapist in the last 12 months, which is more than twice as much as the corresponding rates in the general population of similar ages (9% for the 15–24 years old and 11% for the 25–34 years old [ 39 ]). Trajectories of Empathy and Emotion Recognition The results of the LMMs modelling empathy trajectories from the first to the sixth year of medical school are displayed in Table 1 . For the JSPE-S, the cognitive dimension of the QCAE, and the affective dimension of the QCAE, we observed a significant general increase over the curriculum year ( standardized b [ std. b ] = .21, .05, and .06, respectively), taking a nonlinear concave shape ( std. b = − .16, − .04, and − .03, respectively). However, the pairwise comparisons to each preceding year displayed in Table 2 indicate that the shape of the empathy trajectories might be one of significant increase followed by a plateau. Indeed, we observed significant increases in JSPE-S scores from year 1 up to year 4, followed by a plateau, with no significant changes from year 4 to 6. For both the cognitive and affective dimensions of the QCAE, we observe a single significant increase from year 2 to year 3, followed also by a plateau with no significant changes between year 3 and 6. For the behavioral dimension of empathy measured with the AMSP, the curriculum years had no significant impact, indicating a stable trajectory. Finally, the emotion recognition test (GERT-S) results followed a linear trajectory of significant increase ( std. b = .20), and pairwise comparisons to each previous year revealed significant increases from year 2 up to year 5. The trajectories’ effect sizes are small for the JSPE-S and the GERT-S and even smaller for the cognitive and affective dimensions of the QCAE. Table 1 Linear mixed models of empathy trajectories including psychosocial and health-related covariates JSPE-S QCAE-Cognitive QCAE-Affective AMSP GERT-S Predictors std. b B p std. b B p std. b B p std. b B p std. b B p (Intercept) .17 87.11 < .001 .06 50.16 < .001 .02 23.61 < .001 .03 18.73 < .001 − .04 28.35 < .001 Curriculum year .21 4.72 < .001 .05 0.95 < .001 .06 0.64 .001 − .03 -0.09 .110 .20 0.51 < .001 Curriculum year^2 − .16 -0.52 < .001 − .04 -0.11 .002 − .03 -0.06 .019 Identifying as male − .08 -1.50 .001 − .11 -1.55 < .001 − .23 -2.77 < .001 .06 0.68 .008 − .18 -1.61 < .001 Parents Education − .01 -0.10 .658 .01 0.11 .555 .02 0.14 .309 .03 0.15 .250 .07 0.38 .001 Having a partner .04 0.77 .011 .01 0.09 .684 .02 0.18 .259 .05 0.49 .004 .03 0.23 .132 Social support .08 0.38 < .001 .04 0.15 .012 .06 0.18 < .001 .02 0.05 .265 .08 0.17 < .001 Psychotherapist consultation .04 0.80 .024 .05 0.76 .002 .02 0.26 .170 .03 0.41 .042 − .01 -0.12 .501 Emotion-focused coping .03 0.07 .148 − .02 -0.03 .434 .17 0.24 < .001 .00 0.00 .990 .02 0.02 .351 Problem-focused coping .05 0.27 .001 .13 0.49 < .001 .02 0.07 .111 .07 0.21 < .001 .01 0.03 .443 Help-seeking coping .04 0.13 .027 .05 0.12 .005 .11 0.21 < .001 − .01 -0.01 .763 − .04 -0.06 .050 Depressive symptoms .03 0.02 .287 .06 0.03 .014 .08 0.04 < .001 .00 0.00 .931 .00 0.00 .943 Anxiety symptoms − .05 -0.04 .069 − .07 -0.04 .008 .11 0.05 < .001 − .16 -0.07 < .001 − .02 -0.01 .445 Burnout-Emotional exhaustion .06 0.10 .009 .01 0.02 .516 .01 0.01 .482 .06 0.06 .012 − .02 -0.01 .449 Burnout-Cynicism .00 0.01 .829 .00 -0.01 .817 − .03 -0.03 .156 .04 0.05 .037 .00 0.00 .924 Burnout-Academic efficacy .08 0.16 < .001 .09 0.13 < .001 .02 0.03 .214 .15 0.16 < .001 − .04 -0.03 .113 Physical activity .01 0.04 .464 − .02 -0.05 .283 − .02 -0.03 .311 .06 0.12 < .001 .00 0.00 .880 Satisfaction with health .00 -0.04 .769 − .03 -0.19 .070 .04 0.25 .002 − .03 -0.15 .071 − .01 -0.05 .519 N individuals 1658 1664 1664 1658 1662 N observations 3205 3213 3213 3204 3207 σ 2 33.28 14.87 8.56 10.79 8.05 τ 00 individuals 36.69 26.34 14.26 12.66 8.79 ICC .52 .64 .62 .54 .52 Marginal R 2 .12 .07 .23 .08 .09 Conditional R 2 .58 .66 .71 .58 .56 AICc 22066.31 20015.28 18203.28 18422.02 17664.37 Significant p-values are indicated in bold. Std = Standardized, JSPE-S = Jefferson Scale of Physician Empathy Student version, QCAE = Questionnaire of Cognitive and Affective Empathy, AMSP = Ability to Monitor Self-Presentation, GERT-S = Geneva Emotion Recognition Test Short. Standardized β s between 0.10–0.29 are considered small, those between 0.30–0.49 are considered medium, and those 0.50 or greater are considered large effect sizes [ 35 ] Table 2 Descriptive statistics of students’ empathy per curriculum year and comparisons to previous years N %Missing Mean SD Min Max Comparison to previous year: z (SE) p -value JSPE-S Year 1 574 0.69 101.71 9.98 65 126 Year 2 448 0.22 105.26 8.40 74 125 7.67 (0.43) < .001 Year 3 579 0.69 107.76 8.28 79 127 5.85 (0.39) < .001 Year 4 545 0.55 108.85 7.92 67 130 2.44 (0.37) .044 Year 5 602 0.17 108.48 7.92 77 126 -0.70 (0.36) .499 Year 6 458 1.08 108.55 8.58 76 127 -1.15 (0.39) .499 QCAE-Cognitive Year 1 578 0.00 57.36 6.69 35 76 Year 2 449 0.00 57.56 7.00 36 76 1.30 (0.30) .585 Year 3 583 0.00 58.79 6.57 34 76 3.14 (0.26) .008 Year 4 548 0.00 58.38 7.19 33 76 -0.79 (0.25) .585 Year 5 603 0.00 58.85 6.63 35 76 1.21 (0.24) .585 Year 6 463 0.00 58.74 6.52 36 76 -1.48 (0.26) .553 QCAE-Affective Year 1 578 0.00 33.86 5.82 15 47 Year 2 449 0.00 33.90 5.54 17 48 1.37 (0.23) .679 Year 3 583 0.00 34.58 5.57 16 48 2.66 (0.20) .039 Year 4 548 0.00 34.67 5.49 20 48 0.35 (0.19) 1.000 Year 5 603 0.00 34.92 5.44 15 48 0.10 (0.19) 1.000 Year 6 463 0.00 34.87 5.34 18 48 0.17 (0.20) 1.000 AMSP Year 1 574 0.69 23.51 5.27 10 35 Year 2 448 0.22 22.80 5.29 5 35 -3.68 (0.24) .001 Year 3 579 0.69 23.03 5.06 5 35 0.90 (0.22) .989 Year 4 544 0.73 22.79 4.95 9 35 -0.98 (0.20) .989 Year 5 602 0.17 23.05 4.93 9 35 1.78 (0.20) .303 Year 6 458 1.08 23.25 4.89 0 35 -0.30 (0.22) .989 GERT-S Year 1 575 0.52 29.14 4.18 18 38 Year 2 446 0.67 29.49 4.20 17 38 1.44 (0.22) .299 Year 3 581 0.34 30.24 4.04 18 40 3.43 (0.20) .002 Year 4 545 0.55 30.81 4.29 17 40 2.89 (0.19) .012 Year 5 603 0.00 31.45 3.93 17 41 3.54 (0.18) .002 Year 6 460 0.65 31.34 4.37 17 42 1.05 (0.20) .299 Significant p-values are indicated in bold. Comparisons to previous years were adjusted for multiple comparisons using the Holm-Bonferroni method. JSPE-S = Jefferson Scale of Physician Empathy Student version, QCAE = Questionnaire of Cognitive and Affective Empathy, AMSP = Ability to Monitor Self-Presentation, GERT-S = Geneva Emotion Recognition Test Short form. [Table 1 about here] [Table 2 about here] Influence of Psychosocial and Health-Related Covariates As shown in Table 1 , several covariates significantly influenced different measures of empathy and emotion recognition. Nevertheless, identifying as male was the only covariate with an absolute average effect size large enough to be considered small (absolute average β = 0.13). Compared with the students identifying as female or nonbinary, the students identifying as male had lower scores on the JSPE, QCAE (both cognitive and affective), and GERT-S, whereas the reverse was observed for the AMSP, with students identifying as male having higher scores than students identifying as female or nonbinary. Note that the interaction effect between gender identification and curriculum year was also investigated and was never significant, indicating that even if students’ level of empathy varied according to their gender identification, the slopes of the trajectories were similar. All other covariates had absolute average effect sizes that were too low to even be considered small. Discussion This study aimed to offer an up-to-date insight into empathy trajectories in today’s undergraduate medical education. Its results do not confirm the widely broadcasted decline in empathy during medical school. Among the five included instruments measuring different dimensions of empathy and emotion recognition, none followed a decreasing trajectory. On the contrary, this study shows a general enhancement in cognitive and affective empathy, especially in the first curriculum years, and a steady improvement in emotion recognition abilities. Behavioral empathy, on the other hand, remained relatively stable during medical school. The present study is not the first to show an improvement in empathy during medical school [ 4 , 8 ], but its results contradict those reported in previous studies using the JSPE [ 7 ]. These differences might be due to geo-sociocultural factors such as differences in the conceptualization of empathy, different ways to promote empathy, and differences in the educational environment, resources and logistics within a given medical school. Nevertheless, in the present study, an increase was observed across different measures and different dimensions of empathy. Importantly, the measure that shows the most consistent increase across time is the emotion recognition test (GERT-S). This test does not rely on self-reports and could be considered a more reliable estimate of the students’ actual abilities, which strengthens our findings that the students tended to become more empathic during their medical school. Medical curricula have significantly changed in recent decades, and communication skills training has become a central part of medical education. Our results indicate that improvement in empathy mainly occurs in the first three years of the six-year curriculum, suggesting that these first years are pivotal times for the development of interactional skills. At our university, several courses that aim to sensitize medical students to empathy and to the importance of interaction skills have been implemented at the beginning of medical school. The improvement in empathy in the first years of medical school observed in our study might indicate the effect of such teaching. The results of the present study show a plateau during the last years of the curriculum, which corresponds to medical students’ entry into clinical work [ 1 ]. Encountering the complexities of clinical practice has previously been incriminated as the source of a drop in empathy. However, the present study’s results indicate that medical students’ empathy withstood these difficulties and remained stable in the second half of the curriculum. Additionally, the plateau observed in the present study may reflect a standard learning curve, where improvement in empathy is observed up to a maximum that cannot easily be surpassed for the time being. Behavioral empathy was the only measure that showed stability over time. In the present study, the ability to modify one’s behavior according to the social situation at hand (measured via the AMSP) was used as a proxy of behavioral empathy, which is usually defined as the ability to act accurately on the basis of one’s understanding of others’ emotions [ 11 ]. Given that it does not follow the same trajectory observed in the other empathy measures, this proxy might not totally measure the behavioral pendant of empathy and rather something related to self-monitoring abilities (as the AMSP instrument was originally created for). Any conclusion regarding behavioural empathy trajectories during medical school should be withheld until further studies explore other measures of this concept, the most pertinent being the observation of empathic behaviours during actual interactions. The present study explored different psychosocial and health-related factors that could influence empathy trajectories, such as gender, parents’ education, social support, coping strategies, mental health, or physical activity. The results show that the influence of most of these factors on empathy trajectories was not substantial, which is surprising given that past studies have attested of the influence of several factors on empathy [ 14 ]. Gender identification was the only factor that had a substantial influence on the trajectories of empathy, with small effect sizes. We indeed observed that students identifying as male generally had lower trajectories of empathy (cognitive and affective) and emotion recognition than did students identifying as female. This gender difference in levels of empathy has been extensively reported in past studies and has been attributed to biological predispositions or sociocultural influences [ 40 ]. By accounting for a large array of potentially influential factors, the present study ensured a less biased estimation of empathy trajectories, but the most influential factors might be those not included: the factors pertaining to the educational environment. As a single-site study, the specific influence of the educational environment could not be analysed and is possibly the most determinant factor of medical students’ empathy trajectories. Future multisite studies are needed to confirm this hypothesis. Limitations This study has several limitations that need to be mentioned. Although a more comprehensive approach to empathy is proposed by encompassing its cognitive, affective, and behavioral dimensions, as well as emotion recognition, the present study cannot claim to cover all aspects of this multifaceted and complex concept. Individuals with more socioeconomic and health issues are known to be less likely to participate in cohort studies [ 41 ] and might be underrepresented in our sample. Thus, despite a good response rate, empathy trajectories could differ among students who did not volunteer for the study. The presence of a learning effect for the GERT-S cannot be completely dismissed. Nevertheless, learning effects for such a test from one year to the next are likely insignificant, and when looking solely at the students who participated once (and who thus could not have a learning effect), we could observe that students in more advanced curriculum years still had higher GERT-S scores than those in earlier curriculum years did. Conclusion The potential decrease in empathy during medical school has been largely broadcasted and raised awareness on the need for more interpersonal skills training. Since then, medical curricula have significantly evolved in that regard and might now be an environment more favourable to the development of interactional skills. Updated insights into the current status of medical students’ empathy trajectories are thus needed. The present study indicates that empathy improves during medical school even when multiple dimensions of empathy are considered with different instruments, including an emotion recognition test that is not self-reported. Our results further warrant additional studies on the educational environment to identify the factors that most strongly influence medical students’ empathy trajectories. Abbreviations AMSP Ability to Modify Self-Presentation Scale GERT-S Geneva Emotion Recognition Test – Short Form ICC Intraclass correlation coefficient IRI Interpersonal Reactivity Index JSPE Jefferson Scale of Physician Empathy JSPE-S Jefferson Scale of Physician Empathy - Student Version LMM Linear Mixed Model QCAE Questionnaire of Cognitive and Affective Empathy Std. Standardized Declarations Ethics approval and consent to participate The Research Ethics Committee of the Canton de Vaud approved the ETMED-L project (project number 2020-02474). All participants provided written informed consent. Consent for publication Not applicable Availability of data and materials The dataset supporting the conclusions of this article is available in the zenodo repository, http://doi.org/10.5281/zenodo.11277114 Competing interests The authors declare that they have no competing interests. Funding This work is supported by the Swiss National Science Foundation (grant number 10001C_197442). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions VC : data collection, data management, statistical analyses, and manuscript drafting; AB : project’s principal investigator, project design, and manuscript drafting; CB : project design, data collection, and manuscript drafting; SR : statistical analyses and manuscript drafting; MB : establishment and refinement of project procedures, critical review of the manuscript; MSM : project design and critical review of the manuscript; SB, KS, JG, and PAB : establishment and refinement of project procedures and critical review of the manuscript. All the authors have read and approved the final manuscript. Acknowledgements The authors want to thank Sylvie Felix and Fabienne Thévenaz for their help in the ETMED-L project’s data collection. References Hojat M, Vergare MJ, Maxwell K, Brainard G, Herrine SK, Isenberg GA, et al. 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The Jefferson Scale of Physician Empathy: Development and preliminary psychometric data. Educ Psychol Meas. 2001;61:349–65. Spatoula V, Panagopoulou E, Montgomery A. Does empathy change during undergraduate medical education? – A meta-analysis. Med Teach. 2019;41:895–904. Smith KE, Norman GJ, Decety J. The complexity of empathy during medical school training: Evidence for positive changes. Med Educ. 2017;51:1146–59. Reniers RLEP, Corcoran R, Drake R, Shryane NM, Völlm BA. The QCAE: A Questionnaire of Cognitive and Affective Empathy. J Pers Assess. 2011;93:84–95. Cuff BMP, Brown SJ, Taylor L, Howat DJ. Empathy: A review of the concept. Emot Rev. 2016;8:144–53. Mercer SW, Reynolds WJ. Empathy and quality of care. Br J Gen Pract. 2002;52:S9–12. Davis MH. A multidimensional approach to individual differences in empathy. Cat Sel Doc Psychol. 1980;10. Quince TA, Parker R, Wood DF, Benson JA. Stability of empathy among undergraduate medical students: A longitudinal study at one UK medical school. BMC Med Educ. 2011;11:90–8. Neumann M, Edelhäuser F, Tauschel D, Fischer MR, Wirtz M, Woopen C, et al. Empathy decline and its reasons: A systematic review of studies with medical students and residents. Acad Med. 2011;86:996–1009. Quince TA, Thiemann P, Benson J, Hyde S. Undergraduate medical students’ empathy: Current perspectives. Adv Med Educ Pract. 2016;7:443–55. Dyrbye L, Shanafelt T. A narrative review on burnout experienced by medical students and residents. Med Educ. 2016;50:132–49. Puthran R, Zhang MWB, Tam WW, Ho RC. Prevalence of depression amongst medical students: A meta-analysis. Med Educ. 2016;50:456–68. Hope V, Henderson M. Medical student depression, anxiety and distress outside North America: A systematic review. Med Educ. 2014;48:963–79. Carrard V, Bourquin C, Berney S, Schlegel K, Gaume J, Bart PA, et al. The relationship between medical students’ empathy, mental health, and burnout: A cross-sectional study. Med Teach. 2022;44:1392–9. Berney A, Carrard V, Berney S, Schlegel K, Gaume J, Gholam M, et al. Study protocol for the ETMED-L project: Longitudinal study of mental health and interpersonal competence of medical students in a Swiss university using a comprehensive framework of empathy. BMJ Open. 2021;11:e053070. Myszkowski N, Brunet-Gouet E, Roux P, Robieux L, Malézieux A, Boujut E, et al. Is the Questionnaire of Cognitive and Affective Empathy measuring two or five dimensions? Evidence in a French sample. Psychiatry Res. 2017;255:292–6. Myszkowski N, Storme M, Zenasni F, Lubart T. Appraising the duality of self-monitoring: Psychometric qualities of the Revised Self-Monitoring Scale and the Concern for Appropriateness Scale in French. Can J Behav Sci. 2014;46:387–96. Lennox RD, Wolfe RN. Revision of the self-monitoring scale. J Pers Soc Psychol. 1984;46:1349–64. Schlegel K, Grandjean D, Scherer KR. Introducing the Geneva Emotion Recognition Test: An example of Rasch-based test development. Psychol Assess. 2014;26:666–72. Schlegel K, Boone RT, Hall JA. Individual differences in interpersonal accuracy: A Multi-level meta-analysis to assess whether judging other people is one skill or many. J Nonverbal Behav. 2017;41:103–37. Voorpostel M, Tillmann R, Lebert F, Kuhn U, Lipps O, Ryser VA, et al. Swiss Household Panel Userguide (1999–2020), Wave 22. Lausanne: FORS; 2022. Grob A, Bodmer NM, Flammer A. Living conditions in Europe: The case of Switzerland. Bern: University of Bern, Institute of Psychology; 1993. Bodmer NM, Grob A. Well-being and constraints of adolescents: A comparison of adolescents from single-parent and two-parent families [Bien-être et contraintes d’adolescents: Une comparaison entre adolescents de familles monoparentales et de familles biparentales]. Int J Psychol. 1996;31:39–48. Perrin M, Vandeleur CL, Castelao E, Rothen S, Glaus J, Vollenweider P, et al. Determinants of the development of post-traumatic stress disorder, in the general population. Soc Psychiatry Psychiatr Epidemiol. 2014;49:447–57. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401. Spielberger CD. Manual for the State-trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists; 1983. Schaufeli WB, Martínez IM, Pinto AM, Salanova M, Bakker AB. Burnout and engagement in university students: a cross-national study. J Cross-Cult Psychol. 2002;464–81. Pinheiro J, Bates D. Mixed-Effects Models in S and S-PLUS. Springer Science & Business Media; 2006. Gałecki A, Burzykowski T. Linear Mixed-Effects Model. In: Gałecki A, Burzykowski T, editors. Linear Mixed-Effects Models Using R: A Step-by-Step Approach. New York, NY: Springer; 2013. pp. 245–73. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988. Lee KJ, Roberts G, Doyle LW, Anderson PJ, Carlin JB. Multiple imputation for missing data in a longitudinal cohort study: A tutorial based on a detailed case study involving imputation of missing outcome data. Int J Soc Res Methodol. 2016;19:575–91. StataCorp. Stata Statistical Software: Release 17. StataCorp LLC. College Station, TX: 2021. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria: 2022. Office fédéral de la statistique. Traitement pour problèmes psychiques en 2022 [Internet]. Off. Fédéral Stat.2023 [cited 2023 Dec 5]; https://www.bfs.admin.ch/asset/fr/28465393 Christov-Moore L, Simpson EA, Coudé G, Grigaityte K, Iacoboni M, Ferrari PF. Empathy: Gender effects in brain and behavior. Neurosci Biobehav Rev. 2014;46:604–27. Rothenbühler M, Voorpostel M. Attrition in the Swiss Household Panel: Are Vulnerable Groups more Affected than Others? [Internet]. In: Oris M, Roberts C, Joye D, Ernst Stähli M, editors. Surveying Human Vulnerabilities across the Life Course. Cham: Springer International Publishing; 2016 [cited 2024 Jan 9]. page 223–44. https://doi.org/10.1007/978-3-319-24157-9_10 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 20 Aug, 2024 Editor assigned by journal 19 Aug, 2024 Submission checks completed at journal 19 Aug, 2024 First submitted to journal 14 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4913406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342666158,"identity":"0e4c8a1c-14fa-4999-ba8d-d9cb7adf299c","order_by":0,"name":"Valerie Carrard","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Valerie","middleName":"","lastName":"Carrard","suffix":""},{"id":342666159,"identity":"5b18cad1-fe98-4184-95be-51510e655443","order_by":1,"name":"Céline Bourquin","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Céline","middleName":"","lastName":"Bourquin","suffix":""},{"id":342666160,"identity":"d615c559-f854-4611-8db7-6a40fd07cf16","order_by":2,"name":"Sylvie Berney","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Sylvie","middleName":"","lastName":"Berney","suffix":""},{"id":342666161,"identity":"7bf807b7-1466-4ad1-bae2-b87b1c1d61f4","order_by":3,"name":"Setareh Ranjbar","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Setareh","middleName":"","lastName":"Ranjbar","suffix":""},{"id":342666162,"identity":"04981b5d-aaac-4755-b6c0-ec4380ddbb78","order_by":4,"name":"Katja Schlegel","email":"","orcid":"","institution":"University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Schlegel","suffix":""},{"id":342666163,"identity":"2fdf1c10-aa37-4026-a730-71648e08a720","order_by":5,"name":"Jacques Gaume","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Jacques","middleName":"","lastName":"Gaume","suffix":""},{"id":342666164,"identity":"acdfc04f-2534-43dc-8023-868f5b47c63a","order_by":6,"name":"Pierre-Alexandre Bart","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Pierre-Alexandre","middleName":"","lastName":"Bart","suffix":""},{"id":342666165,"identity":"6e1b30d7-5b46-4a4c-a754-017bc93962a1","order_by":7,"name":"Marianne Schmid Mast","email":"","orcid":"","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"Schmid","lastName":"Mast","suffix":""},{"id":342666166,"identity":"48680396-273a-419c-be5c-733cd193c4d1","order_by":8,"name":"Martin Preisig","email":"","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Preisig","suffix":""},{"id":342666167,"identity":"dcdaf3c7-9b92-43db-82f4-962466b2fdd3","order_by":9,"name":"Alexandre Berney","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACdjDJDMQ8EAF+KCMBpxZmdC2SDSRrMThAQAt/M/MxiZ87rPP5GXgPPq7cYZO4+fjZY9I8DHZ5uLRIHGZLNuw9k245s4Ev2fDsmbTEbWfy0oBakotxOuwwj+ED3rbDBkD3mEk2th1O3HYDyJjBcCCxAYcO+cP8Hw7+BWqxP8Bj/rOx7X/i5hkEtBgc5mF8DLaFgceMsbHtQOIGCR4ziQ94tBgeZjM2lm1LN5A4zJcMdFiy8YwzOcYWHwyScWqRO978TPJtm7UBf3vvwY+NbXay/e1nDG8kVNjh1IIAkAhicISoNCCoHgHsSVA7CkbBKBgFIwQAAJQUVEnukxu+AAAAAElFTkSuQmCC","orcid":"","institution":"Lausanne University Hospital (CHUV) and University of Lausanne","correspondingAuthor":true,"prefix":"","firstName":"Alexandre","middleName":"","lastName":"Berney","suffix":""}],"badges":[],"createdAt":"2024-08-14 11:51:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4913406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4913406/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-025-07051-8","type":"published","date":"2025-04-15T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66633020,"identity":"7c6fd6ae-caef-48d1-9341-68d8a603c070","added_by":"auto","created_at":"2024-10-15 05:04:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":745932,"visible":true,"origin":"","legend":"\u003ch2\u003eParticipation flow chart for each data collection wave\u003c/h2\u003e\n\u003cp\u003eNote that in wave 4, first-year students were not eligible for participation. GERT-S = Geneva Emotion Recognition Test – Short form.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4913406/v1/5525a817ff9b13d1e3f6eaa9.png"},{"id":81050791,"identity":"92824243-f572-49d7-b6fb-622b338f6efd","added_by":"auto","created_at":"2025-04-21 16:05:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2080970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4913406/v1/a232976c-f0b5-49ad-9513-2404fe5306da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trajectories of medical students’ empathy nowadays: A longitudinal study using a comprehensive framework of empathy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the seminal work of Hojat and colleagues [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] in 2009, the decrease in empathy during medical school has been widely broadcasted and the belief that medical students “lose their empathy” during medical school has become well rooted in both the medical and medical education communities. However, after more than a decade of medical curriculum improvement worldwide regarding the relational and communicational aspects of patient encounters [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the time might have come to reexamine the trajectories of empathy during medical school. In 2020, a systematic review reported that, among the 24 cross-sectional and 6 longitudinal studies identified, 14 reported a significant decrease in empathy during medical school, whereas the remaining 16 reported an increase, stability, or mixed results [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Some authors have attributed these mixed findings to geo-sociocultural factors. A review reported that US studies mostly reported small but significant decreases in empathy, whereas Far Eastern studies mostly reported small but significant increases in empathy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Another explanation could be the instruments used to measure empathy. A meta-analysis indeed revealed that changes in empathy were significant only when they were measured with the Jefferson Scale of Physician Empathy (JSPE [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. One study even reported opposing trajectories when different instruments were used. Smith et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] indeed reported a decline in empathy previously described when the JSPE was used but reported a significant increase in empathy when measured with the Questionnaire of Cognitive and Affective Empathy (QCAE [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]). Also, using task-based measures in addition to self-report questionnaires, their results revealed that medical students’ ability to recognize others’ emotional states and their sensitivity to facial expressions increased during medical school [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to consider the well-recognized multidimensionality of empathy. Most authors agree that it encompasses at least a cognitive and an affective component, which are, respectively, the ability to recognize and understand others’ emotions by taking their perspective for cognitive empathy and a resonance with or microcontagion of others’ feelings for affective empathy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Other authors add to these two dimensions a behavioral component of empathy, which is the ability to act accurately on the basis of one’s understanding of others’ emotions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Using the Interpersonal Reactivity Index (IRI [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]), a multidimensional measure of empathy, Quince et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that different dimensions of empathy can follow different trajectories during medical school. They indeed reported a statistically significant decline in the affective dimension of empathy, whereas the cognitive dimension of empathy remained stable [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They also observed that this decline in affective empathy was only significant for male students, thus highlighting the importance of accounting for both the multidimensionality of empathy and the impact of potentially influencing factors.\u003c/p\u003e \u003cp\u003eSeveral factors have been shown to influence empathy and its trajectories, such as gender [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], specialty choice [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], social support [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and psychological well-being [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To obtain a better understanding of empathy trajectories during medical school, these factors need to be controlled for. Moreover, identifying influencing factors would provide a better understanding of individual differences in empathy trajectories and may offer avenues for interventions to prevent empathy decline during medical school.\u003c/p\u003e \u003cp\u003eThe aim of the present study was to describe the trajectories of different dimensions of empathy over the full period of medical school and explore the influence of different psychosocial and health factors on these trajectories. This study sought to build on the same strengths as previous work [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] by adopting a comprehensive framework of empathy with validated self-report questionnaires of the cognitive, affective, and behavioral components of empathy as well as a test of emotion recognition to include a measure that does not rely on self-reports. Moreover, it complements existing literature by (1) covering an entire six-year curriculum, (2) including a large sample, (3) formally testing possible nonlinear trajectories of empathy, and (4) investigating the influence of psychosocial and health-related factors. Such studies revisiting empathy trajectories during medical school are currently needed to maybe start deconstructing the notion of medical students’ empathy loss. Indeed, medical curricula constantly evolve, and the efforts made during the last two decades to improve communication skills training [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] might hopefully have impacted the development of empathy during medical school.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eDesign\u003c/p\u003e\u003cp\u003eThis longitudinal study used data collected in an open cohort design for the ETMED-L project [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In each of the four data collection waves (March 2021, November 2021, November 2022, and November 2023), medical students from all curriculum years (1 to 6) were included (except for the last data collection wave, in which first-year students were excluded to prioritize multiple participation). Linear Mixed Models (LMMs) were applied because they effectively accommodate limited and discontinuous data, providing a reliable way to estimate the longitudinal trajectories of empathy across the complete six years of medical education, even if assessments were conducted at a maximum of four time points for each participant.\u003c/p\u003e\u003cp\u003eData Collection\u003c/p\u003e\u003cp\u003eAt each of the four data collection waves, an online questionnaire investigating mental health and interpersonal competence was sent to all matriculated medical students at the University of Lausanne (Switzerland), except for the students who were at the university as part of an academic exchange. It took approximately one hour to complete the online questionnaire fully, and the students received 50 CHF (~ 50 USD) for each completed questionnaire. The Research Ethics Committee of the canton of Vaud approved the project (project number 2020–02474), and all participants provided written informed consent. At the end of the questionnaire, contact information for mental health help services was provided if students felt the need to seek support.\u003c/p\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003cp\u003eEmpathy and emotion recognition\u003c/p\u003e\u003cp\u003eTo adopt a more comprehensive framework of empathy, we used five measures from three validated instruments as well as an emotion recognition test described in more detail in the published study protocol [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. First, we assessed empathy with the widely used Jefferson Scale of Physician Empathy - Student Version (JSPE-S [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]), which was developed to measure medical students’ orientations or attitudes toward empathic relationships in the context of patient care [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Second, the validated French version of the Questionnaire of Cognitive and Affective Empathy (QCAE [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]) was used as a more multidimensional measure of empathy assessing separately cognitive and affective empathy. For assessing behavioural empathy, the observation of interactions with patients would have been ideal, but these kinds of data would have been difficult to obtain. After careful examination of existing self-reported instruments, we found that the validated French version of the Ability to Modify Self-Presentation Scale (AMSP [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]) could be a proxy to measure a behavioral dimension of empathy, as it assesses one’s ability to modify their behaviours according to the social situation at hand. Finally, we used the Geneva Emotion Recognition Test – Short Form (GERT-S [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]) to measure emotion recognition accuracy. Indeed, empathy has traditionally been assessed through self-report questionnaires, but there are also well-validated performance-based tasks that evaluate the ability to recognize emotions in individuals depicted in pictures or short videos [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The GERT-S presents 42 video clips of actors expressing one out of 14 emotions while saying pseudolinguistic sentences. The final score of the test is the number of emotions correctly recognized by the participants. These types of emotion recognition tasks have been found to have a significant, though modest, correlation with self-reported measures of both cognitive and affective empathy (Murphy and Lilienfeld, 2019), suggesting that while the ability to recognize others' emotions is linked to both understanding and resonating with others' emotions, they also capture aspects of emotional processing that go beyond these dimensions.\u003c/p\u003e\u003cp\u003ePsychosocial and health-related covariates\u003c/p\u003e\u003cp\u003eFour \u003cb\u003esociological covariates\u003c/b\u003e were analysed: identifying as male (gender identity recoded into 1 = male and 2 = female or nonbinary), parents’ education (number of parents with a college or university degree), having a partner (1 = yes, 0 = no), and social support. Social support was measured as the average score of two questions adapted from the Swiss Household Panel survey [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]: “If necessary, in your opinion, to what extent can someone provide you with practical help, this means concrete help or useful advice, if 0 means \"not at all\" and 10 \"a great deal\"?” for practical support and “To what extent can someone be available in case of need and show understanding, by talking with you for example, if 0 means \"not at all\" and 10 \"a great deal\"?” for emotional support.\u003c/p\u003e\u003cp\u003eRegarding \u003cb\u003epsychological covariates\u003c/b\u003e, \u003cem\u003ehaving consulted a psychotherapist\u003c/em\u003e during the past year (1 = yes, 0 = no) and coping strategies were assessed. The French version of the coping section of the Euronet questionnaire [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was used to measure three types of coping strategies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]: \u003cem\u003eemotion-focused\u003c/em\u003e, \u003cem\u003eproblem-focused\u003c/em\u003e, and \u003cem\u003ehelp-seeking\u003c/em\u003e. Moreover, mental health and burnout indicators were included. For mental health, the Center for Epidemiological Studies-Depression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] was used to measure \u003cem\u003edepression symptoms\u003c/em\u003e, and the State-Trait Anxiety Inventory [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was used for \u003cem\u003eanxiety symptoms\u003c/em\u003e. Additionally, the \u003cem\u003eemotional exhaustion\u003c/em\u003e, \u003cem\u003ecynicism\u003c/em\u003e, and \u003cem\u003eacademic efficacy\u003c/em\u003e (reversed) dimensions of burnout were assessed with the Maslach Burnout Inventory Student-Survey [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, two \u003cb\u003ehealth-related covariates\u003c/b\u003e were included: \u003cem\u003ephysical activity\u003c/em\u003e (number of hours per week) and \u003cem\u003esatisfaction with one’s own health\u003c/em\u003e (“Are you satisfied with your health?” rated on a scale from 1 = very unsatisfied to 5 = very satisfied).\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo investigate the longitudinal trajectories of empathy during medical school, separate LMMs were modelled for each empathy and emotion recognition measure, with curriculum year and covariates as fixed effects. Specifically, the fixed effects account for the systematic variation associated with changes across the curriculum years and control for potential confounding factors. Additionally, random intercepts were incorporated at the student level to account for the correlation between repeated measures within the same student while considering the inherent variability between students. The restricted maximum likelihood method was used to produce unbiased estimates of variance and covariance parameters [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, two different temporal variance-covariance structures (Autoregressive Covariance Structure of AR1 and Autoregressive/Moving Average Covariance Structure of ARMA1.1) were tested to potentially account for the temporal spillover of empathy and emotion recognition, with the best fitting model being selected via likelihood ratio tests. The nonlinear trajectories of empathy and emotion recognition were subsequently tested by including the time as quadratic or cubic. The best-fitting model was selected via likelihood ratio tests and presented as the final model. For all the final models, we report marginal R\u003csup\u003e2\u003c/sup\u003e as the percentage of variation explained by the fixed part of the model, conditional R\u003csup\u003e2\u003c/sup\u003e as the percentage of variation explained by both the fixed and random parts of the model, and the intraclass correlation coefficient (ICC) as the proportion of total between-student variance. Moreover, standardized β values are presented for effect size estimation. Standardized βs between 0.10–0.29 are considered small, those between 0.30–0.49 are considered medium, and those 0.50 or greater are considered large effect sizes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo explore significant differences between specific time points, pairwise comparisons to each preceding year were additionally conducted with Holm-Bonferroni corrections to account for multiple comparisons. However, unlike LMMs, these comparisons do not account for the correlation between repeated measures within the same student and are thus a less accurate representation of longitudinal trajectories.\u003c/p\u003e\u003cp\u003eThe highest missing rate at the item level was 0.59% (AMSP items). It has been shown that much gain from multiple imputation is unlikely when missing rates are lower than 5% [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, missing data at the item level were replaced by mean scores if less than 20% of the items were missing. If 20% or more of the items were missing, the total score was considered missing, and missing data at the score level were then handled with full information maximum likelihood in the LMMs. Stata version 17 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and R version 4.2.2 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] were used for the analyses, and p values \u0026lt; .05 were considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSample\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flow chart of the study participation. After exclusion of the students who gave a wrong answer to at least one of the two attention questions (e.g., \u0026ldquo;In order to check your attention, please answer \u0026lsquo;Slightly agree\u0026rsquo; to this question.\u0026rdquo;), scored implausibly low on the GERT-S, or encountered technical issues, each data collection wave included respectively 885, 1032, 1049, and 812 answered questionnaires, which represented 49.36%, 57.56%, 58.51%, and 45.29% of each wave\u0026rsquo;s eligible students. From those, we excluded 169 repeat students to have only linear curriculum trajectories as well as 15 students who had missing data on all the variables of interest across all the data collection waves. Consequently, the final sample included 1667 medical students who filled in a total of 3224 questionnaires.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e The medical students in our final sample were between 17 and 49 years old, with a mean age of 21.80 years (standard deviation\u0026thinsp;=\u0026thinsp;3.09) at first participation. Among them, 67.19% self-identified as female, 0.84% self-identified as nonbinary, and 31.97% as male, which corresponds to the gender proportions generally observed in the Lausanne Medical School. Half of the medical students had a partner (52.37%), and the majority had one (21.66%) or two (53.57%) parents with higher education (college or university diploma). An important proportion of the students, 23.88%, indicated having consulted a psychotherapist in the last 12 months, which is more than twice as much as the corresponding rates in the general population of similar ages (9% for the 15\u0026ndash;24 years old and 11% for the 25\u0026ndash;34 years old [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]).\u003c/p\u003e \u003cp\u003eTrajectories of Empathy and Emotion Recognition\u003c/p\u003e \u003cp\u003eThe results of the LMMs modelling empathy trajectories from the first to the sixth year of medical school are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the JSPE-S, the cognitive dimension of the QCAE, and the affective dimension of the QCAE, we observed a significant general increase over the curriculum year (\u003cem\u003estandardized b\u003c/em\u003e [\u003cem\u003estd. b\u003c/em\u003e]\u0026thinsp;=\u0026thinsp;.21, .05, and .06, respectively), taking a nonlinear concave shape (\u003cem\u003estd. b\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.16, \u0026minus;\u0026thinsp;.04, and \u0026minus;\u0026thinsp;.03, respectively). However, the pairwise comparisons to each preceding year displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that the shape of the empathy trajectories might be one of significant increase followed by a plateau. Indeed, we observed significant increases in JSPE-S scores from year 1 up to year 4, followed by a plateau, with no significant changes from year 4 to 6. For both the cognitive and affective dimensions of the QCAE, we observe a single significant increase from year 2 to year 3, followed also by a plateau with no significant changes between year 3 and 6. For the behavioral dimension of empathy measured with the AMSP, the curriculum years had no significant impact, indicating a stable trajectory. Finally, the emotion recognition test (GERT-S) results followed a linear trajectory of significant increase (\u003cem\u003estd. b\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.20), and pairwise comparisons to each previous year revealed significant increases from year 2 up to year 5. The trajectories\u0026rsquo; effect sizes are small for the JSPE-S and the GERT-S and even smaller for the cognitive and affective dimensions of the QCAE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear mixed models of empathy trajectories including psychosocial and health-related covariates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eJSPE-S\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eQCAE-Cognitive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eQCAE-Affective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eAMSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003eGERT-S\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003estd. b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003estd. b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003estd. b\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e 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colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurriculum year^2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentifying as male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychotherapist consultation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotion-focused coping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProblem-focused coping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelp-seeking coping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout-Emotional exhaustion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout-Cynicism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout-Academic efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction with health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003eindividuals\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e1664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e1662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003eobservations\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e3205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e3213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e3213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e3204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e3207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e33.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eτ\u003csub\u003e00 individuals\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e36.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e26.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e14.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e8.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAICc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e22066.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e20015.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e18203.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e18422.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c20\" namest=\"c18\"\u003e \u003cp\u003e17664.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"20\"\u003eSignificant p-values are indicated in bold. Std\u0026thinsp;=\u0026thinsp;Standardized, JSPE-S\u0026thinsp;=\u0026thinsp;Jefferson Scale of Physician Empathy Student version, QCAE\u0026thinsp;=\u0026thinsp;Questionnaire of Cognitive and Affective Empathy, AMSP\u0026thinsp;=\u0026thinsp;Ability to Monitor Self-Presentation, GERT-S\u0026thinsp;=\u0026thinsp;Geneva Emotion Recognition Test Short. Standardized \u003cem\u003eβ\u003c/em\u003es between 0.10\u0026ndash;0.29 are considered small, those between 0.30\u0026ndash;0.49 are considered medium, and those 0.50 or greater are considered large effect sizes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of students\u0026rsquo; empathy per curriculum year and comparisons to previous years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%Missing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eComparison to previous year: \u003cem\u003ez\u003c/em\u003e (SE) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJSPE-S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQCAE-Cognitive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQCAE-Affective\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAMSP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGERT-S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYear 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eSignificant p-values are indicated in bold. Comparisons to previous years were adjusted for multiple comparisons using the Holm-Bonferroni method. JSPE-S\u0026thinsp;=\u0026thinsp;Jefferson Scale of Physician Empathy Student version, QCAE\u0026thinsp;=\u0026thinsp;Questionnaire of Cognitive and Affective Empathy, AMSP\u0026thinsp;=\u0026thinsp;Ability to Monitor Self-Presentation, GERT-S\u0026thinsp;=\u0026thinsp;Geneva Emotion Recognition Test Short form.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eInfluence of Psychosocial and Health-Related Covariates\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, several covariates significantly influenced different measures of empathy and emotion recognition. Nevertheless, identifying as male was the only covariate with an absolute average effect size large enough to be considered small (absolute average \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13). Compared with the students identifying as female or nonbinary, the students identifying as male had lower scores on the JSPE, QCAE (both cognitive and affective), and GERT-S, whereas the reverse was observed for the AMSP, with students identifying as male having higher scores than students identifying as female or nonbinary. Note that the interaction effect between gender identification and curriculum year was also investigated and was never significant, indicating that even if students\u0026rsquo; level of empathy varied according to their gender identification, the slopes of the trajectories were similar. All other covariates had absolute average effect sizes that were too low to even be considered small.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to offer an up-to-date insight into empathy trajectories in today\u0026rsquo;s undergraduate medical education. Its results do not confirm the widely broadcasted decline in empathy during medical school. Among the five included instruments measuring different dimensions of empathy and emotion recognition, none followed a decreasing trajectory. On the contrary, this study shows a general enhancement in cognitive and affective empathy, especially in the first curriculum years, and a steady improvement in emotion recognition abilities. Behavioral empathy, on the other hand, remained relatively stable during medical school.\u003c/p\u003e \u003cp\u003eThe present study is not the first to show an improvement in empathy during medical school [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but its results contradict those reported in previous studies using the JSPE [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These differences might be due to geo-sociocultural factors such as differences in the conceptualization of empathy, different ways to promote empathy, and differences in the educational environment, resources and logistics within a given medical school. Nevertheless, in the present study, an increase was observed across different measures and different dimensions of empathy. Importantly, the measure that shows the most consistent increase across time is the emotion recognition test (GERT-S). This test does not rely on self-reports and could be considered a more reliable estimate of the students\u0026rsquo; actual abilities, which strengthens our findings that the students tended to become more empathic during their medical school. Medical curricula have significantly changed in recent decades, and communication skills training has become a central part of medical education. Our results indicate that improvement in empathy mainly occurs in the first three years of the six-year curriculum, suggesting that these first years are pivotal times for the development of interactional skills. At our university, several courses that aim to sensitize medical students to empathy and to the importance of interaction skills have been implemented at the beginning of medical school. The improvement in empathy in the first years of medical school observed in our study might indicate the effect of such teaching. The results of the present study show a plateau during the last years of the curriculum, which corresponds to medical students\u0026rsquo; entry into clinical work [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Encountering the complexities of clinical practice has previously been incriminated as the source of a drop in empathy. However, the present study\u0026rsquo;s results indicate that medical students\u0026rsquo; empathy withstood these difficulties and remained stable in the second half of the curriculum. Additionally, the plateau observed in the present study may reflect a standard learning curve, where improvement in empathy is observed up to a maximum that cannot easily be surpassed for the time being.\u003c/p\u003e \u003cp\u003eBehavioral empathy was the only measure that showed stability over time. In the present study, the ability to modify one\u0026rsquo;s behavior according to the social situation at hand (measured via the AMSP) was used as a proxy of behavioral empathy, which is usually defined as the ability to act accurately on the basis of one\u0026rsquo;s understanding of others\u0026rsquo; emotions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given that it does not follow the same trajectory observed in the other empathy measures, this proxy might not totally measure the behavioral pendant of empathy and rather something related to self-monitoring abilities (as the AMSP instrument was originally created for). Any conclusion regarding behavioural empathy trajectories during medical school should be withheld until further studies explore other measures of this concept, the most pertinent being the observation of empathic behaviours during actual interactions.\u003c/p\u003e \u003cp\u003eThe present study explored different psychosocial and health-related factors that could influence empathy trajectories, such as gender, parents\u0026rsquo; education, social support, coping strategies, mental health, or physical activity. The results show that the influence of most of these factors on empathy trajectories was not substantial, which is surprising given that past studies have attested of the influence of several factors on empathy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Gender identification was the only factor that had a substantial influence on the trajectories of empathy, with small effect sizes. We indeed observed that students identifying as male generally had lower trajectories of empathy (cognitive and affective) and emotion recognition than did students identifying as female. This gender difference in levels of empathy has been extensively reported in past studies and has been attributed to biological predispositions or sociocultural influences [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. By accounting for a large array of potentially influential factors, the present study ensured a less biased estimation of empathy trajectories, but the most influential factors might be those not included: the factors pertaining to the educational environment. As a single-site study, the specific influence of the educational environment could not be analysed and is possibly the most determinant factor of medical students\u0026rsquo; empathy trajectories. Future multisite studies are needed to confirm this hypothesis.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis study has several limitations that need to be mentioned. Although a more comprehensive approach to empathy is proposed by encompassing its cognitive, affective, and behavioral dimensions, as well as emotion recognition, the present study cannot claim to cover all aspects of this multifaceted and complex concept. Individuals with more socioeconomic and health issues are known to be less likely to participate in cohort studies [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and might be underrepresented in our sample. Thus, despite a good response rate, empathy trajectories could differ among students who did not volunteer for the study. The presence of a learning effect for the GERT-S cannot be completely dismissed. Nevertheless, learning effects for such a test from one year to the next are likely insignificant, and when looking solely at the students who participated once (and who thus could not have a learning effect), we could observe that students in more advanced curriculum years still had higher GERT-S scores than those in earlier curriculum years did.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe potential decrease in empathy during medical school has been largely broadcasted and raised awareness on the need for more interpersonal skills training. Since then, medical curricula have significantly evolved in that regard and might now be an environment more favourable to the development of interactional skills. Updated insights into the current status of medical students\u0026rsquo; empathy trajectories are thus needed. The present study indicates that empathy improves during medical school even when multiple dimensions of empathy are considered with different instruments, including an emotion recognition test that is not self-reported. Our results further warrant additional studies on the educational environment to identify the factors that most strongly influence medical students\u0026rsquo; empathy trajectories.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAMSP\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ability to Modify Self-Presentation Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGERT-S\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;Geneva Emotion Recognition Test \u0026ndash; Short Form\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICC\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Intraclass correlation coefficient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRI\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interpersonal Reactivity Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJSPE\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Jefferson Scale of Physician Empathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJSPE-S\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Jefferson Scale of Physician Empathy - Student Version\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLMM\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Linear Mixed Model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQCAE\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Questionnaire of Cognitive and Affective Empathy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStd.\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Standardized\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe Research Ethics Committee of the Canton de Vaud approved the ETMED-L project (project number 2020-02474). All participants provided written informed consent.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is available in the zenodo repository, http://doi.org/10.5281/zenodo.11277114\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work is supported by the Swiss National Science Foundation (grant number 10001C_197442). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVC\u003c/strong\u003e: data collection, data management, statistical analyses, and manuscript drafting; \u003cstrong\u003eAB\u003c/strong\u003e: project’s principal investigator, project design, and manuscript drafting; \u003cstrong\u003eCB\u003c/strong\u003e: project design, data collection, and manuscript drafting; \u003cstrong\u003eSR\u003c/strong\u003e: statistical analyses and manuscript drafting; \u003cstrong\u003eMB\u003c/strong\u003e: establishment and refinement of project procedures, critical review of the manuscript; \u003cstrong\u003eMSM\u003c/strong\u003e: project design and critical review of the manuscript; \u003cstrong\u003eSB, KS, JG,\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;PAB\u003c/strong\u003e: establishment and refinement of project procedures and critical review of the manuscript. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors want to thank Sylvie Felix and Fabienne Thévenaz for their help in the ETMED-L project’s data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHojat M, Vergare MJ, Maxwell K, Brainard G, Herrine SK, Isenberg GA, et al. The devil is in the third year: A longitudinal study of erosion of empathy in medical school. Acad Med. 2009;84:1182\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakoul G. Essential elements of communication in medical encounters: The Kalamazoo consensus statement. Acad Med. 2001;76:390\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBachmann C, Abramovitch H, Barbu CG, Cavaco AM, Elorza RD, Haak R, et al. 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New York, NY: Springer; 2013. pp. 245\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee KJ, Roberts G, Doyle LW, Anderson PJ, Carlin JB. Multiple imputation for missing data in a longitudinal cohort study: A tutorial based on a detailed case study involving imputation of missing outcome data. Int J Soc Res Methodol. 2016;19:575\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStataCorp. Stata Statistical Software: Release 17. StataCorp LLC. College Station, TX: 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria: 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice f\u0026eacute;d\u0026eacute;ral de la statistique. Traitement pour probl\u0026egrave;mes psychiques en 2022 [Internet]. Off. F\u0026eacute;d\u0026eacute;ral Stat.2023 [cited 2023 Dec 5]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bfs.admin.ch/asset/fr/28465393\u003c/span\u003e\u003cspan address=\"https://www.bfs.admin.ch/asset/fr/28465393\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristov-Moore L, Simpson EA, Coud\u0026eacute; G, Grigaityte K, Iacoboni M, Ferrari PF. Empathy: Gender effects in brain and behavior. Neurosci Biobehav Rev. 2014;46:604\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRothenb\u0026uuml;hler M, Voorpostel M. Attrition in the Swiss Household Panel: Are Vulnerable Groups more Affected than Others? [Internet]. In: Oris M, Roberts C, Joye D, Ernst St\u0026auml;hli M, editors. Surveying Human Vulnerabilities across the Life Course. Cham: Springer International Publishing; 2016 [cited 2024 Jan 9]. page 223\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-24157-9_10\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-24157-9_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Medical students, empathy, emotion recognition, longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-4913406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4913406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. For more than a decade, the literature has been dominated by the notion that medical students may paradoxically lose their empathy during medical school. However, medical curricula have significantly evolved, and the question is whether this is still the case. The present study aimed to describe the trajectories of different dimensions of empathy from the beginning to the end of a six-year medical curriculum and explore the influence of different psychosocial and health-related factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e. In an open cohort design, all medical students at the University of Lausanne (Switzerland) were invited to complete four waves of yearly online questionnaires. Cognitive, affective, and behavioral empathy were measured with three validated instruments, and emotion recognition was assessed with a performance test. For each measure, linear mixed models including an array of psychosocial and health-related potential covariates were modelled. Different temporal variance-covariance structures and nonlinear trajectories were tested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. The final sample included 3224 questionnaires completed by 1667 medical students. The cognitive and affective dimensions of empathy significantly increased in the first half of medical school, followed by a plateau, whereas behavioral empathy remained stable. For emotion recognition, a significant linear increase was observed. The only covariate with substantial influence was gender: students identifying as male presented similar trajectories of empathy and emotion recognition but with overall lower scores than students identifying as female or nonbinary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. This study revealed significant increases in cognitive empathy, affective empathy, and emotion recognition. Developments in today’s medical curricula may have contributed to the observed increase in empathy. Future multisite studies are warranted to identify the features of the educational environment that impact the trajectories of empathy during medical school.\u003c/p\u003e","manuscriptTitle":"Trajectories of medical students’ empathy nowadays: A longitudinal study using a comprehensive framework of empathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 05:04:38","doi":"10.21203/rs.3.rs-4913406/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-20T12:19:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-19T06:35:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-19T06:32:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2024-08-14T11:48:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3742248d-890d-4c1d-91de-dbc8ef0345ba","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T15:59:24+00:00","versionOfRecord":{"articleIdentity":"rs-4913406","link":"https://doi.org/10.1186/s12909-025-07051-8","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2025-04-15 15:57:11","publishedOnDateReadable":"April 15th, 2025"},"versionCreatedAt":"2024-10-15 05:04:38","video":"","vorDoi":"10.1186/s12909-025-07051-8","vorDoiUrl":"https://doi.org/10.1186/s12909-025-07051-8","workflowStages":[]},"version":"v1","identity":"rs-4913406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4913406","identity":"rs-4913406","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}