Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic

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However, there are fewer studies of sleep disorders in medical students after the COVID-19 pandemic. This study aimed to investigate the prevalence and factors influencing sleep disorders among Chinese medical students after COVID-19. Methods We enrolled 1194 medical students. The Self-administered scale was used to collect the demographic characteristics. The Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) were used to assess subjects' depression, anxiety, and sleep disorders, respectively. The chi-square test and binary logistic regression were used to identify factors that influence sleep disorders. The receiver operating characteristic (ROC) curve was used to assess the predictive value of relevant variables for sleep disorders. Results We found that the prevalence of sleep disorders among medical students after COVID-19 was 82.3%. According to logistic regression results, medical students with depression were 1.151 times more likely to have sleep disorders than those without depression (OR = 1.151, 95% CI 1.114 to 1.188). Doctoral students were 1.908 times more likely to have sleep disorders than graduate and undergraduate students (OR = 1.908, 95% CI 1.264 to 2.880). In addition, the area under the ROC curve for depression is 0.689. Conclusion The prevalence of sleep disorders among medical students is high after COVID-19. In addition, high academic level and depression are risk factors for sleep disorders. Therefore, medical colleges and administrators should pay more attention to sleep disorders in medical students after the COVID-19 pandemic. Regular assessment of sleep disorders and depression is extremely necessary. Sleep Disorders Medical students COVID-19 Depression Anxiety Figures Figure 1 Figure 2 1. Introduction COVID-19, caused by a novel coronavirus that emerged in December 2019 [ 1 ], has profoundly impacted healthcare systems and populations worldwide [ 2 – 4 ]. With a case fatality rate ranging from 1–7%, the virus continues to exert long-term health effects on individuals even after recovery, commonly called post-COVID or long COVID-19 [ 2 , 5 , 6 ]. The extended consequences of COVID-19 extend to various organs and systems, posing a sustained threat to both physical and mental health [ 2 , 4 , 5 , 7 ], with infection serving as a risk factor for exacerbating psychological disorders [ 8 ]. For example, some studies have found that long after infection with COVID-19, patients continue to experience sleep disorders, anxiety, and depression problems [ 9 , 10 ]. During the COVID-19 pandemic, medical students faced significant physical and mental stressors [ 11 – 13 ], including high workload and challenges due to insufficient protective measures [ 14 ]. The prevalence of sleep disorders among medical students during this period was notable [ 15 ]. For instance, Greek medical students experienced severe disruptions in sleep quality during the pandemic, with the majority reporting insomnia (65.9%) and poor sleep quality (52.4%) [ 15 ]. A multinational cross-sectional survey reported that 73.5% of medical students had poor sleep quality during the pandemic [ 16 ]. Furthermore, a meta-analysis encompassing 201 studies found a sleep disorder prevalence of 52% among medical students during the COVID-19 outbreak [ 17 ]. Sleep disorders persist in medical students even after the initial surge of the pandemic [ 18 ]. However, research on the prevalence of sleep disorders and influencing factors in medical student’s post-pandemic remains limited [ 19 ]. It is crucial to study the mental health of medical students because China faces a critical issue of medical student attrition [ 20 , 21 ], coupled with challenges such as limited job opportunities, declining public trust in doctors leading to decreased self-identity [ 22 ], low-income [ 23 ], challenging work environments [ 24 ], and uneven distribution of medical resources [ 25 ]. During the COVID-19 outbreak, a survey found that 58.4% of respondents wanted to change their profession after graduation [ 26 ]. Therefore, research is urgently needed to explore Chinese medical students' mental well-being and identify relevant factors to formulate effective interventions. The impact of the pandemic on medical students’ mental health is enduring and requires long-term monitoring [ 2 , 27 ]. Previous studies have found that the risk of sleep disorders remains elevated one year after significant events such as a pandemic [ 8 ]. However, no previous study has explored sleep disorders in post-COVID-19 medical students. Thus, the objectives of the current study include: 1) To investigate the prevalence of sleep disorders among medical students after the COVID-19 outbreak. And 2) to explore the factors influencing sleep disorders in medical students after the COVID-19 pandemic. 2. Methods 2.1 Study design and procedure A total of 1194 medical students were recruited for this study from 9 to 20 July 2023 (as shown in Fig. 1 ). Before the poll began, we ensured that all participants had electronically given their informed consent. Participants needed to be able to read Chinese, understand the survey's goals, be informed, and agree to take part in the study for it to be considered. Refusal to participate in this study, more than 15% missing data, and inconsistent response completeness were used as exclusion criteria. After data collection was complete, two researchers double-checked the questionnaire for accuracy. The flowchart for this study is shown in Fig. 1 . The protocol for the research project had been approved by the Institutional Review Board (IRB) of the Tianjin Anding Hospital (approving number: 2023-027). The research was carried out after the principles laid out in the 1964 Declaration of Helsinki and its later revisions. Confidentiality of responses was assured, and all participants gave their informed consent. 2.2 Infection of medical students with COVID-19 The well-known medical illness known as post-viral infection syndrome (PVIS) varies in severity after acute viral infection recovery. It is marked by different degrees of physical, cognitive, and emotional impairment [ 28 ]. For this reason, we divided the medical students into three groups according to the frequency of COVID-19 infections: once, more than two times, and no. 2.3 Demographic characteristics Academic level, gender, birthplace, weekly exercise frequency, personality trait, health condition, been infected with COVID-19, in clinic rotation, financial pressure, employment pressure, disruption of medical education, wish to return to clinical rotation, pursuing clinical work after graduation, the impact of COVID-19 and worried about being infected were among the demographic characteristics listed. Each participant's frequency of being infected with COVID-19 was evaluated using the following three-point dimensions: "once," "more than two times," and "no." Every participant was asked to rate the epidemic's impact on their future medical careers on a three-point scale: "no impact," "positive," and "negative." 2.4 Pittsburgh Sleep Quality Index (PSQI) The PSQI [ 2 ] was utilized to evaluate sleep disorders [ 29 ]. Seven sections comprise the 19-question questionnaire: sleep duration, sleep disturbance, sleep latency, subjective sleep quality, habitual sleep efficiency, daytime dysfunction, and sleep medications. With a total of 21 points, the PSQI reflects the quality of each component. Participants in this study were classified as having a sleep disorder if their overall PSQI score was 8 or above [ 30 ]. This scale had a Cronbach's alpha of 0.83 [ 31 ]. 2.5 Self-rating Anxiety Scale (SAS) SAS was used to assess anxiety symptoms [ 32 ]. Each symptom on the SAS questionnaire has a frequency scale from 1 to 4, comprising 20 items. After determining the raw score for each item, we multiplied it by 1.25 to get the standardized value. The SAS norm, which captures the subjective experiences of people who tend to stress out, was utilized to establish a cutoff threshold for anxiety at 50 points on a standardized scale [ 33 ]. A Cronbach's alpha of 0.84 was recorded for this scale [ 34 ]. 2.6 Self-rating Depression Scale (SDS) SDS, a brief 20-question questionnaire, was used to measure depressive symptoms [ 35 ]. The validity and reliability of this lengthy self-administered survey are high. One can get an overall SDS score by adding the results from all 20 questions. To get the standardized score, multiply the SDS score by 1.25, keeping the whole value. The SDS norm, which represents the subjective experiences of individuals with depression, was utilized to establish a cutoff point for depression at 40 on the standardized total score [ 36 ]. The SDS has a Cronbach's alpha of 0.73 [ 37 ]. 2.7 Statistical analysis The statistical analysis was conducted using SPSS 25.0. The use of descriptive analysis allowed for the characterization of demographic features. To display categorical data, we use the numbers N and %, while to summarize quantitative data, we use the numbers M and SD. Dissimilarities between pupils suffering from sleep disorders and those without were examined using a chi-square test. Furthermore, binary logistic regression was employed to examine potential risk factors for sleep disorders, with all variables that were significant in the chi-square test serving as dependent variables. We employed the enter-LR technique. The 95% confidence interval (CI) of the ratio of ratios (OR) showed the degree to which different factors were linked to sleep disorders. For the test, a p-value less than 0.05 was deemed statistically significant. Analyses were performed using the receiver operating characteristic curve (ROC curve) to assess the predictive value of relevant variables for sleep disorders. 3. Results 3.1 Demographic characteristics of participants The prevalence of sleep disorders among medical students was 82.3% (N = 983). 69.8% were undergraduate and graduate students (N = 833), and 30.2% were doctoral students (N = 361). Table 1 shows the demographic characteristics of the participants. 50.2% were male participants (N = 599) and 49.8% were female participants (N = 595), who accounted for almost the same percentage of the total. In addition, 60.6% (N = 724) of the medical students had been infected once, 29.3% (N = 350) had been infected more than two times, and 10.1% (N = 120) had not been infected with COVID-19. Table 1 Socio-demographic characteristics of the medical students after the COVID-19 epidemic (N = 1194) Variables Frequency % Academic level Undergraduate and Postgraduate 833 69.8 Doctor 361 30.2 Gender Male 599 50.2 Female 595 49.8 Birthplace Countryside 618 51.8 Town 576 48.2 Weekly exercise frequency Never 397 33.2 One or two times 443 37.1 More than three times 354 29.6 Personality trait Introversion 578 48.4 Extroversion 616 51.6 Health condition Fine 464 38.9 General and with chronic disease 730 61.1 Been infected with COVID-19 Yes, once 724 60.6 Yes, more than two times 350 29.3 No 120 10.1 In clinic rotation Yes 626 52.4 No 568 47.6 Financial pressure Yes 637 53.4 No 557 46.6 Employment pressure Yes 651 54.5 No 543 45.5 Disruption of medical education Yes 602 50.4 No 592 49.6 Wish to return to clinical rotation Yes 567 47.5 No 627 52.5 Pursuing clinical work after graduation Yes 655 54.9 No 539 45.1 The impact of COVID-19 No impact 385 32.2 Positive 393 32.9 Negative 416 34.8 Worried about being infected Yes 607 50.8 No 587 49.2 Note: The question of “Financial pressure”: Are you feeling heavy financial pressure? The question of “Employment pressure”: Are you feeling heavy employment pressure? The question of “Disruption of medical education”: Has the epidemic disrupted medical education? The question of “Wish to return to clinical rotation”: Do you wish to return to clinical rotation on time? The question of “Pursuing clinical work after graduation”: Whether you wish to pursue a clinical career after graduation? The question of “The impact of COVID-19”: What do you think about the impact of COVID-19 on healthcare? The question of “Worried about being infected”: Are you worried about infecting COVID-19 when you return to your clinical rotation? 3.2 Clinical characteristics of participants The total scores of SDS, SAS and PSQI of the participants were 49.25 ± 5.678, 44.39 ± 7.693 and 10.14 ± 3.326 respectively. Of the seven components of the PSQI, the mean scores of subjective sleep quality, sleep latency, sleep duration, sleep disturbance, the use of sleep medication, and daytime dysfunction were above 1. In addition, the mean scores for solely habitual sleep efficiency were less than 1 (Table 2 ). Table 2 The scores of PSQI, SAS, and SDS of the participants Variables Mean SD PSQI total score 10.14 3.326 Subjective sleep quality 1.46 1.107 Sleep latency 1.72 0.869 Sleep duration 1.45 1.088 Habitual sleep efficiency 0.60 0.849 Sleep disturbance 1.84 0.562 Sleep medications 1.39 1.143 Daytime dysfunction 1.68 0.873 Anxiety total score 44.39 7.693 Depression total score 49.25 5.678 Note: PSQI, Pittsburgh Sleep Quality Index; SDS, Self-rating Depression Scale; SAS, Self-rating Anxiety Scale. 3.3 Comparison of medical students with and without sleep disorders Table 3 compares the differences between medical students with and without sleep disorders. Doctoral students were more likely to have sleep disorders than undergraduate and graduate students (χ2 = 19.594, P < 0.001). Medical students with chronic conditions and general health status were more likely to have sleep disorders than medical students with good health status (χ2 = 23.289, P < 0.001). Medical students who wanted to work in clinical medicine after graduation were more likely to have sleep disorders than those who did not want to work in clinical medicine (χ2 = 21.272, P < 0.001). Table 3 Comparison of medical students with and without sleep disorders after the COVID-19 epidemic Variables Participants(N = 1194) None-Sleep disorders (N = 211) Sleep disorders (N = 983) χ 2 P Academic level 19.594 < 0.001 Undergraduate and Postgraduate 174 (14.6%) 659 (55.2%) Doctor 37 (3.1%) 324 (27.1%) Gender 3.804 0.051 Male 93 (7.8%) 506 (42.4%) Female 118 (9.9%) 477 (39.9%) Birthplace 0.238 0.626 Countryside 106 (8.9%) 512 (42.9%) Town 105 (8.8%) 471 (39.4%) Weekly exercise frequency 4.753 0.093 Never 68 (5.7%) 329 (27.6%) One or two times 91(7.6%) 325 (29.5%) More than three times 52 (4.4%) 302 (25.3%) Personality trait 0.017 0.896 Introversion 103 (8.6%) 475 (39.8%) Extroversion 108 (9.0%) 508 (42.5%) Health condition 23.289 < 0.001 Fine 113 (9.5%) 351 (29.4%) General and with chronic disease 98 (8.2%) 632 (52.9%) Been infected with COVID-19 2.901 0.235 Yes, once 120 (10.1%) 604 (50.6%) Yes, more than two times 72 (6.0%) 278 (23.3%) No 19(1.6%) 101(8.5%) In clinic rotation 0.303 0.582 Yes 107 (9.0%) 519 (43.5%) No 104 (8.7%) 464 (38.9%) Financial pressure 0.153 0.696 Yes 110 (9.2%) 527 (44.1%) No 101 (8.5%) 456 (38.2%) Employment pressure 8.344 0.004 Yes 134 (11.2%) 517 (43.3%) No 77 (6.4%) 466 (39.0%) Disruption of medical education 0.009 0.925 Yes 107 (9.0%) 495 (41.5%) No 104 (8.7%) 488 (40.9%) Wish to return to clinical rotation 0.624 0.430 Yes 95 (8.0%) 472 (39.5%) No 116 (9.7%) 511 (42.8%) Pursuing clinical work after graduation 21.272 < 0.001 Yes 146 (12.2%) 509 (42.6%) No 65 (5.4%) 474 (39.7%) The impact of COVID-19 2.241 0.326 No impact 77 (6.4%) 308 (25.8%) Positive 67 (5.6%) 326 (27.3%) Negative 67 (5.6%) 349 (29.2%) Worried about being infected 0.905 0.342 Yes 101 (8.5%) 506 (42.4%) No 110 (9.2%) 477 (39.9%) Depression or not 267.378 < 0.001 Yes 148 (12.4%) 976 (81.7%) No 63 (5.3%) 7 (0.6%) Anxiety or not 46.489 < 0.001 Yes 114 (9.5%) 757 (63.4%) No 97 (8.1%) 226 (18.9%) Note: The question of “Financial pressure”: Are you feeling heavy financial pressure? The question of “Employment pressure”: Are you feeling heavy employment pressure? The question of “Disruption of medical education”: Has the epidemic disrupted medical education? The question of “Wish to return to clinical rotation”: Do you wish to return to clinical rotation on time? The question of “Pursuing clinical work after graduation”: Whether you wish to pursue a clinical career after graduation? The question of “The impact of COVID-19”: What do you think about the impact of COVID-19 on healthcare? The question of “Worried about being infected”: Are you worried about infecting COVID-19 when you return to your clinical rotation? Bolding means the P < 0.05. Table 4 Influencing factors of sleep disorders among medical students after the COVID-19 epidemic period Influence factors B Std. error Wald P OR 95%CI Model 1 Academic level 0.808 0.196 16.947 < 0.001 2.243 1.527~3.295 Health condition 0.681 0.157 18.900 < 0.001 1.976 1.454~2.686 Employment pressure 0.423 0.161 6.911 0.009 1.526 1.114~2.091 Pursuing clinical work after graduation 0.673 0.165 16.564 < 0.001 1.960 1.418~2.711 Constant -2.065 0.456 20.483 < 0.001 0.127 Model 2 Academic level 0.733 0.200 13.465 < 0.001 2.082 1.407~3.080 Health condition 0.497 0.164 9.299 0.002 1.643 1.193~2.264 Employment pressure 0.352 0.165 4.531 0.033 1.422 1.028~1.965 Pursuing clinical work after graduation 0.514 0.171 9.075 0.003 1.671 1.197~2.335 Anxiety 0.059 0.009 47.543 < 0.001 1.061 1.043~1.079 Constant -4.519 0.587 59.310 < 0.001 0.011 Model 3 Academic level 0.646 0.210 9.472 0.002 1.908 1.264~2.880 Health condition 0.206 0.179 1.329 0.249 1.229 0.866~1.744 Employment pressure 0.204 0.177 1.336 0.248 1.227 0.867 ~ 1.735 Pursuing clinical work after graduation 0.306 0.181 2.865 0.091 1.358 0.953~1.934 Anxiety 0.001 0.010 0.012 0.912 1.001 0.981 ~ 1.022 Depression 0.140 0.016 72.396 < 0.001 1.151 1.114 ~ 1.188 Constant -8.825 0.948 86.636 < 0.001 0.000 Note: The question of “Employment pressure”: Are you feeling heavy employment pressure? The question of “Pursuing clinical work after graduation”: Whether you wish to pursue a clinical career after graduation? Bolding means the P < 0.05. Interestingly, medical students who did not feel employment stress were likelier to have sleep disorders than those who felt employment stress (χ2 = 8.344, P = 0.004). However, there was no significant difference in the prevalence of sleep disorders among medical students who had been infected once, infected multiple times, and those who had not been infected with COVID-19 (χ2 = 2.901, P = 0.235). Notably, medical students with depression (χ2 = 267.378, P < 0.001) and anxiety (χ2 = 46.489, P < 001) were more likely to have sleep disorders than those without depression and anxiety. 3.4 Risk factors of sleep disorders in medical students Next, we used binary logistic regression to explore the factors influencing sleep disorders among medical students after the epidemic. The presence of a sleep disorder was used as the dependent variable, and variables with significant differences in univariate analyses were used as independent variables. Firstly, we included the four independent variables: academic level, health condition, employment pressure, and pursuit of clinical work (Model 1). The results found that doctoral students had a 2.243 times higher risk of developing sleep disorders than graduate and undergraduate students (OR = 2.243, 95% CI 1.527–3.295). Medical students with general health status and chronic diseases had 1.976 times the risk of developing sleep disorders than medical students with good health status (OR = 1.976, 95% CI 1.454–2.686). In addition, medical students who wished to work in clinical medicine after graduation had 1.960 times the risk of developing sleep disorders than those who did not want to work in clinical medicine (OR = 1.960, 95% CI 1.418–2.711). Interestingly, students who did not feel under heavy employment pressure had a 1.526 times higher risk of developing sleep disorders than those who did not feel under heavy employment pressure (OR = 1.526, 95% CI 1.114–2.091). Secondly, we added anxiety as a dependent variable for further analysis (Model 2). We found that academic level (OR = 2.082, 95% CI 1.407–3.080), health condition (OR = 1.643, 95% CI 1.193–2.264), employment pressure (OR = 1.422, 95% CI 1.028–1.965), and pursuing clinical work after graduation (OR = 1.671, 95% CI 1.197–2.335) still influence sleep disorders among medical students. In addition, anxiety was a risk factor for sleep disorders among medical students after the epidemic (OR = 1.061, 95% CI 1.043–1.079). Finally, we added depression as a dependent variable (Model 3). The results found that depression was a risk factor for sleep disorders among medical students after the epidemic (OR = 1.151, 95% CI 1.114–1.188). In addition, academic level influenced sleep disorders among medical students (OR = 1.908, 95% CI 1.264–2.880). However, health condition (P = 0.249), employment pressure (P = 0.248), pursuing clinical work after graduation (P = 0.091), and anxiety (P = 0.912) did not influence sleep disorders among medical students. In addition, the area under the ROC curve for depression is 0.689. 4. Discussion The current study is the first large-scale cross-sectional investigation to explore the prevalence of sleep disorders among medical students after the COVID-19 pandemic. Our main findings as follows: firstly, the incidence of sleep disorders among medical students after the COVID-19 pandemic is 82.3%; secondly, academic level, health condition, employment pressure, and pursuing clinical work after graduation influence sleep disorders; and finally, depression and high academic level are independent risk factors of sleep disorders among medical students. The main finding is that the prevalence of sleep disorders is 82.3% among medical students after the COVID-19 pandemic. This result aligns with previous research [ 38 ]. For instance, a meta-analysis shows that sleep disorders are prevalent in the medical student population [ 38 ]. Another study found a 76% incidence of poor sleep quality among medical students [ 39 ]. However, the current study reveals a higher incidence of sleep disorders among medical students than earlier studies during COVID-19 [ 15 , 17 ]. For example, a study in Greece found a sleep disorder incidence of 52.4% among medical students during the COVID-19 pandemic [ 15 ]. Additionally, a systematic review and meta-analysis reported a 52% incidence of sleep disorders among medical students during the COVID-19 pandemic [ 17 ]. These inconsistencies suggest differences in the prevalence of sleep disorders among medical students after and during the COVID-19 epidemic. Furthermore, we found that the academic level influences sleep disorders among medical students after the COVID-19 pandemic. Previous studies have limited exploration of sleep disorder incidence among graduate students [ 40 ]. It is well known that there is a strong relationship between academic level and mental health [ 41 ]. Furthermore, it has been found that the higher the level of education, the higher the prevalence of sleep disorders [ 42 ]. For example, doctoral students had significantly higher levels of anxiety and sleep problems as well as depressive symptoms than master's students [ 43 ]. In addition, one study found that approximately 83% of graduate students experienced sleep disorders during the COVID-19 pandemic [ 40 ]. Our findings provide new literature on the relationship between academic levels and sleep disorders. Another main finding of our study is that depression is an independent risk factor for sleep disorders among medical students after the COVID-19 pandemic. Depression is prevalent, costly, debilitating, and associated with an increased risk of suicide [ 44 – 46 ]. There is a close relationship between depression and sleep disorders [ 47 , 48 ]. Most sleep disorders patients experience depressive episodes, and higher levels of depression are associated with an increased incidence of sleep disorders [ 49 ]. Hence, the relationship between depression and sleep disorders may be bidirectional [ 47 , 48 ]. For instance, a meta-analysis found a positive correlation between depression and sleep disorders [ 50 ]. Studies have shown a higher incidence of depression in patients with sleep disorders [ 49 ]. The reasons of depression affects sleep disorders include: Firstly, Patients with depression have reduced circadian rhythm amplitude, and various treatments for depression have been shown to affect circadian rhythms [ 51 ]. Secondly, severe depressive disorders are associated with functional impairments in the structural network regulating rapid eye movement (REM) sleep [ 52 ]. Finally, the medial prefrontal cortex (mPFC) is a crucial region regulating depression and sleep, and significant changes in neural activity in the mPFC subregions of depression patients have been observed [ 53 ]. Our regression model reveals that anxiety is a factor influencing sleep disorders among medical students after the COVID-19 pandemic, consistent with previous research results [ 54 , 55 ]. For example, research indicates that higher levels of anxiety are associated with a higher incidence of sleep disorders [ 56 ]. A meta-analysis also found a positive correlation between anxiety levels and sleep disorders [ 57 ]. However, when we include depression in the regression model, anxiety does not impact the sleep disorders of medical students. We hypothesize that depression has a more significant influence on sleep disorders among medical students than anxiety. Indeed, the relationship between depression, anxiety, and sleep disorders may be complex [ 58 – 61 ]. Therefore, further research is needed to systematically elucidate the relationship between depression, anxiety, and sleep disorders. It is noteworthy that, before initiating this study, we predicted that infecting COVID-19 would affect the sleep disorders of medical students. Surprisingly, our study results indicate no difference in the incidence of sleep disorders among medical students infected once, more than two times, or not at all with COVID-19. This result may be attributed to China being one of the earliest countries to commence global COVID-19 vaccination campaigns, leading to a significant decline in COVID-19-related mortality rates [ 62 ]. Previous research suggests that repeated COVID-19 infections further increase the risks of death, hospitalization, and sequelae[ 63 ]. However, no research exists exploring the impact of repeated infections and the number of infections on sleep disorders. Therefore, more studies are needed to investigate the effects of COVID-19 infection on sleep. Despite providing valuable insights, our current study has limitations that must be acknowledged. Firstly, the cross-sectional study limits our detailed understanding of how COVID-19 affects sleep disorders. Future research should employ longitudinal studies to understand the temporal effects of these results. Secondly, due to the online survey design, most studies used self-administered questionnaires without clinical diagnostic confirmation. However, all included studies used validated screening tools such as the SDS, SAS, and PSQI. Therefore, future research is recommended to use rigorous clinical diagnosis to confirm our results. Thirdly, the questionnaire did not include whether participants were isolated during the COVID-19 pandemic, and it remains unclear whether this would affect sleep disorders. Finally, the current study only assessed medical students in China, and generalizing the results to the entire medical student population may be challenging. In conclusion, the incidence of sleep disorders is high among medical students after the COVID-19 pandemic. Additionally, depression and high academic levels are independent risk factors for sleep disorders among medical students. Thus, to reduce the incidence of sleep disorders among medical students after the COVID-19 pandemic, targeted intervention strategies must be developed. Declarations Conflict of Interest Statement: The authors declare no conflicts of interest. Funding: None. Ethics approval and consent to participate: The protocol for the research project had been approved by the Institutional Review Board (IRB) of the Tianjin Anding Hospital (approving number: 2023-027), and had therefore been performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Patients' informed consent forms were obtained, and their anonymity was protected. Data availability statement: The data supporting this study's findings are available on request from the corresponding author. Author’s contribution statement: Jiao Liu, Daliang Sun and Guoshuai Luo were responsible for the study design. Jiao Liu, Baozhu Li and Ran Zhang were responsible for patient recruitment and data collection. Jiao Liu was responsible for statistical analysis. Jiao Liu, Daliang Sun and Guoshuai Luo were involved in conceptualizing, writing and editing the manuscript, as well as responding to reviewers. All authors contributed to and approved the final manuscript. Acknowledgements: We want to extend our gratitude to Shuo Wang, Yifan Jing, Zaimina Xuekelaiti, Yan Zhou, Ru Hao, Lidan Yuan, Linxuan Wang, and Ziqing Zhang from Tianjin Medical University for their assistance in collecting data. The authors thank the subjects whose participation made this study possible. Consent for publication Not applicable. References Kevadiya BD, et al. Diagnostics for SARS-CoV-2 infections. Nat Mater. 2021;20(5):593–605. Higgins V, et al. COVID-19: from an acute to chronic disease? Potential long-term health consequences. Crit Rev Clin Lab Sci. 2021;58(5):297–310. Chotpitayasunondh T, et al. Influenza and COVID-19: What does co-existence mean? Influenza Other Respir Viruses. 2021;15(3):407–12. Chatterjee AB. COVID-19, Critical Illness, and Sleep. Crit Care Med. 2022;50(6):1021–3. Davis HE, et al. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133–46. Su S, et al. Epidemiology, clinical presentation, pathophysiology, and management of long COVID: an update. Mol Psychiatry. 2023;28(10):4056–69. Zarei M, et al. Long-term side effects and lingering symptoms post COVID-19 recovery. Rev Med Virol. 2022;32(3):e2289. Xie Y, Xu E, Al-Aly Z. Risks of mental health outcomes in people with covid-19: cohort study. BMJ. 2022;376:e068993. Zawilska JB, Kuczyńska K. Psychiatric and neurological complications of long COVID. J Psychiatr Res. 2022;156:349–60. Pandharipande P, et al. Mitigating neurological, cognitive, and psychiatric sequelae of COVID-19-related critical illness. Lancet Respir Med. 2023;11(8):726–38. Cao W, et al. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 2020;287:112934. Ma Z, et al. Mental health problems and correlates among 746 217 college students during the coronavirus disease 2019 outbreak in China. Epidemiol Psychiatr Sci. 2020;29:e181. Stanyte A, et al. Mental Health and Wellbeing in Lithuanian Medical Students and Resident Doctors During COVID-19 Pandemic. Front Psychiatry. 2022;13:871137. Qian F, et al. Should Medical Students Be Overprotected? A Survey from China and Review About the Roles of Medical Student Under the COVID-19. Risk Manag Healthc Policy. 2023;16:327–35. Eleftheriou A, et al. Sleep Quality and Mental Health of Medical Students in Greece During the COVID-19 Pandemic. Front Public Health. 2021;9:775374. Tahir MJ, et al. Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey. PLoS ONE. 2021;16(11):e0259594. Peng P, et al. The prevalence and risk factors of mental problems in medical students during COVID-19 pandemic: A systematic review and meta-analysis. J Affect Disord. 2023;321:167–81. Pinzon RT, et al. Persistent neurological manifestations in long COVID-19 syndrome: A systematic review and meta-analysis. J Infect Public Health. 2022;15(8):856–69. Cheng J, et al. Mental health and cognitive function among medical students after the COVID-19 pandemic in China. Front Public Health. 2023;11:1233975. Zeng J, Zeng XX, Tu Q. A gloomy future for medical students in China. Lancet. 2013;382(9908):1878. Peng P, et al. High prevalence and risk factors of dropout intention among Chinese medical postgraduates. Med Educ Online. 2022;27(1):2058866. Zhao D, Zhang Z. Changes in public trust in physicians: empirical evidence from China. Front Med. 2019;13(4):504–10. Feng J, et al. The prevalence of turnover intention and influencing factors among emergency physicians: A national observation. J Glob Health. 2022;12:04005. Gao X, Xu Y. Overwork death among doctors a challenging issue in China. Int J Cardiol. 2019;289:152. Wu Q, Zhao L, Ye XC. Short Healthc professionals China Bmj. 2016;354:i4860. Cai CZ et al. Effect of the COVID-19 Pandemic on Medical Student Career Perceptions: Perspectives from Medical Students in China. Int J Environ Res Public Health, 2021. 18(10). Wang S, et al. Prevalence and influencing factors of sleep disturbance among medical students under the COVID-19 pandemic. Eur Arch Psychiatry Clin Neurosci; 2023. Lippi G, Sanchis-Gomar F, Henry BM. COVID-19 and its long-term sequelae: what do we know in 2023? Pol Arch Intern Med, 2023. 133(4). Doi Y, et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res. 2000;97(2–3):165–72. Ho RT, Fong TC. Factor structure of the Chinese version of the Pittsburgh sleep quality index in breast cancer patients. Sleep Med. 2014;15(5):565–9. Benítez ID, et al. Sleep and Circadian Health of Critical COVID-19 Survivors 3 Months After Hospital Discharge. Crit Care Med. 2022;50(6):945–54. Zung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971;12(6):371–9. Dunstan DA, Scott N. Norms for Zung's Self-rating Anxiety Scale. BMC Psychiatry. 2020;20(1):90. Liu CY, et al. The prevalence and influencing factors in anxiety in medical workers fighting COVID-19 in China: a cross-sectional survey. Epidemiol Infect. 2020;148:e98. Zung WW, SELF-RATING DEPRESSION. SCALE. Arch Gen Psychiatry, 1965. 12: p. 63–70. Jokelainen J, et al. Validation of the Zung self-rating depression scale (SDS) in older adults. Scand J Prim Health Care. 2019;37(3):353–7. Shi J, et al. A study on the correlation between family dynamic factors and depression in adolescents. Front Psychiatry. 2022;13:1025168. Seoane HA, et al. Sleep disruption in medicine students and its relationship with impaired academic performance: A systematic review and meta-analysis. Sleep Med Rev. 2020;53:101333. Almojali AI, et al. The prevalence and association of stress with sleep quality among medical students. J Epidemiol Glob Health. 2017;7(3):169–74. Anwer S, et al. Evaluation of Sleep Habits, Generalized Anxiety, Perceived Stress, and Research Outputs Among Postgraduate Research Students in Hong Kong During the Coronavirus (COVID-19) Pandemic. J Multidiscip Healthc. 2021;14:3135–49. Houtepen LC, et al. Associations of adverse childhood experiences with educational attainment and adolescent health and the role of family and socioeconomic factors: A prospective cohort study in the UK. PLoS Med. 2020;17(3):e1003031. Deng J, et al. The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: A systematic review and meta-analysis. Psychiatry Res. 2021;301:113863. Pizuńska D, et al. Well-being among PhD candidates. Psychiatr Pol. 2021;55(4):901–14. Johnson D, et al. Adult mental health outcomes of adolescent depression: A systematic review. Depress Anxiety. 2018;35(8):700–16. Smith K. Mental health: a world of depression. Nature. 2014;515(7526):181. Marwaha S, et al. Novel and emerging treatments for major depression. Lancet. 2023;401(10371):141–53. Urrila AS, et al. Sleep in adolescent depression: physiological perspectives. Acta Physiol (Oxf). 2015;213(4):758–77. Goldstein AN, Walker MP. The role of sleep in emotional brain function. Annu Rev Clin Psychol. 2014;10:679–708. Pandi-Perumal SR, et al. Clarifying the role of sleep in depression: A narrative review. Psychiatry Res. 2020;291:113239. Baglioni C, et al. Sleep and mental disorders: A meta-analysis of polysomnographic research. Psychol Bull. 2016;142(9):969–90. Schulz P, Steimer T. Neurobiology of circadian systems. CNS Drugs. 2009;23(Suppl 2):3–13. Ebdlahad S, et al. Comparing neural correlates of REM sleep in posttraumatic stress disorder and depression: a neuroimaging study. Psychiatry Res. 2013;214(3):422–8. Chang CH, et al. Ventromedial prefrontal cortex regulates depressive-like behavior and rapid eye movement sleep in the rat. Neuropharmacology. 2014;86:125–32. Huang C, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021;397(10270):220–32. Chellappa SL, Aeschbach D. Sleep and anxiety: From mechanisms to interventions. Sleep Med Rev. 2022;61:101583. Palmer CA, Alfano CA. Anxiety modifies the emotional effects of sleep loss. Curr Opin Psychol. 2020;34:100–4. Cox RC, Olatunji BO. Sleep in the anxiety-related disorders: A meta-analysis of subjective and objective research. Sleep Med Rev. 2020;51:101282. Nyer M, et al. Relationship between sleep disturbance and depression, anxiety, and functioning in college students. Depress Anxiety. 2013;30(9):873–80. Difrancesco S, et al. Sleep, circadian rhythm, and physical activity patterns in depressive and anxiety disorders: A 2-week ambulatory assessment study. Depress Anxiety. 2019;36(10):975–86. Deng J, et al. The prevalence of depression, anxiety, and sleep disturbances in COVID-19 patients: a meta-analysis. Ann N Y Acad Sci. 2021;1486(1):90–111. Wang S, et al. Anxiety prevalence and associated factors among frontline nurses following the COVID-19 pandemic: a large-scale cross-sectional study. Front Public Health. 2023;11:1323303. Xu H, et al. Effectiveness of inactivated COVID-19 vaccines against mild disease, pneumonia, and severe disease among persons infected with SARS-CoV-2 Omicron variant: real-world study in Jilin Province, China. Emerg Microbes Infect. 2023;12(1):2149935. Bowe B, Xie Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28(11):2398–405. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 25 Mar, 2024 Editor assigned by journal 25 Mar, 2024 First submitted to journal 21 Mar, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4144293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283483873,"identity":"269d2f34-7765-48e5-af9c-de619984f9b8","order_by":0,"name":"Jiao Liu","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Liu","suffix":""},{"id":283483874,"identity":"045e850b-049d-4878-88eb-a874ebea9151","order_by":1,"name":"Baozhu Li","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Baozhu","middleName":"","lastName":"Li","suffix":""},{"id":283483875,"identity":"789e7bc7-194d-4dbf-a47f-ef62d050a7f9","order_by":2,"name":"Ran Zhang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Zhang","suffix":""},{"id":283483876,"identity":"f1236d8e-ed39-4e07-b514-4a47d4b06077","order_by":3,"name":"Guoshuai Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACPiidwMDA2MDAYMDGwMDe2PjgAx4tbChaDoC08BxuNpxBnBYgOAAiJNLbpDnwaZFIPvyap6Iuz+D44QbmDwV80QY3HzZIMzDYyek24NKSlmbNc+ZwscGZRLDDcjfcTmwwLmBINjY7gEtLjpkxb9uBxA0HkLQkz2A4kLgNv5a6xA3nH0K13DzYcJgHvxbjx7xtzIkbbsBsucHY2IxXC8+zNMY5Zw4nzrwBtOUMUMvMM4nNjDMMcPuFnz358Ic3FXWJfefTHzBU/DmW23f8+PMfHyrs5HBpAbsNymD/wcBwDMo2wKkcBJiRU0cNXqWjYBSMglEwMgEATN9mBT4twGkAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guoshuai","middleName":"","lastName":"Luo","suffix":""},{"id":283483877,"identity":"954677c7-d6a8-4b1e-90f5-d61e4843e261","order_by":4,"name":"Daliang Sun","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Daliang","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-03-21 14:37:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4144293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4144293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53618641,"identity":"85d95c94-42a5-49be-ae00-7c39573d0b04","added_by":"auto","created_at":"2024-03-28 07:13:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80179,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4144293/v1/33f2951f512f43dcfc25994c.png"},{"id":53618640,"identity":"07fa2e14-55b1-4673-8b80-94059f87c13f","added_by":"auto","created_at":"2024-03-28 07:13:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64890,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the factor influencing medical students' sleep disorders after the COVID-19 epidemic. The area beneath the curve was 0.689 for SDS scores.\u003c/p\u003e\n\u003cp\u003eNote: SDS, Self-rating Depression Scale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4144293/v1/0790c385fdf965e68394e4d9.png"},{"id":53619526,"identity":"f5665dd1-3c98-4ccd-a7a0-c5c7dcfc46db","added_by":"auto","created_at":"2024-03-28 07:21:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":642034,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4144293/v1/f98fb077-f0dd-4022-88e4-5034814bc47e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCOVID-19, caused by a novel coronavirus that emerged in December 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], has profoundly impacted healthcare systems and populations worldwide [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. With a case fatality rate ranging from 1\u0026ndash;7%, the virus continues to exert long-term health effects on individuals even after recovery, commonly called post-COVID or long COVID-19 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The extended consequences of COVID-19 extend to various organs and systems, posing a sustained threat to both physical and mental health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with infection serving as a risk factor for exacerbating psychological disorders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, some studies have found that long after infection with COVID-19, patients continue to experience sleep disorders, anxiety, and depression problems [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, medical students faced significant physical and mental stressors [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], including high workload and challenges due to insufficient protective measures [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The prevalence of sleep disorders among medical students during this period was notable [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, Greek medical students experienced severe disruptions in sleep quality during the pandemic, with the majority reporting insomnia (65.9%) and poor sleep quality (52.4%) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A multinational cross-sectional survey reported that 73.5% of medical students had poor sleep quality during the pandemic [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, a meta-analysis encompassing 201 studies found a sleep disorder prevalence of 52% among medical students during the COVID-19 outbreak [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Sleep disorders persist in medical students even after the initial surge of the pandemic [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, research on the prevalence of sleep disorders and influencing factors in medical student\u0026rsquo;s post-pandemic remains limited [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is crucial to study the mental health of medical students because China faces a critical issue of medical student attrition [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], coupled with challenges such as limited job opportunities, declining public trust in doctors leading to decreased self-identity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], low-income [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], challenging work environments [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and uneven distribution of medical resources [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. During the COVID-19 outbreak, a survey found that 58.4% of respondents wanted to change their profession after graduation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, research is urgently needed to explore Chinese medical students' mental well-being and identify relevant factors to formulate effective interventions.\u003c/p\u003e \u003cp\u003eThe impact of the pandemic on medical students\u0026rsquo; mental health is enduring and requires long-term monitoring [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have found that the risk of sleep disorders remains elevated one year after significant events such as a pandemic [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, no previous study has explored sleep disorders in post-COVID-19 medical students. Thus, the objectives of the current study include: 1) To investigate the prevalence of sleep disorders among medical students after the COVID-19 outbreak. And 2) to explore the factors influencing sleep disorders in medical students after the COVID-19 pandemic.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and procedure\u003c/h2\u003e \u003cp\u003eA total of 1194 medical students were recruited for this study from 9 to 20 July 2023 (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Before the poll began, we ensured that all participants had electronically given their informed consent. Participants needed to be able to read Chinese, understand the survey's goals, be informed, and agree to take part in the study for it to be considered. Refusal to participate in this study, more than 15% missing data, and inconsistent response completeness were used as exclusion criteria. After data collection was complete, two researchers double-checked the questionnaire for accuracy. The flowchart for this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e The protocol for the research project had been approved by the Institutional Review Board (IRB) of the Tianjin Anding Hospital (approving number: 2023-027). The research was carried out after the principles laid out in the 1964 Declaration of Helsinki and its later revisions. Confidentiality of responses was assured, and all participants gave their informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Infection of medical students with COVID-19\u003c/h2\u003e \u003cp\u003eThe well-known medical illness known as post-viral infection syndrome (PVIS) varies in severity after acute viral infection recovery. It is marked by different degrees of physical, cognitive, and emotional impairment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For this reason, we divided the medical students into three groups according to the frequency of COVID-19 infections: once, more than two times, and no.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Demographic characteristics\u003c/h2\u003e \u003cp\u003eAcademic level, gender, birthplace, weekly exercise frequency, personality trait, health condition, been infected with COVID-19, in clinic rotation, financial pressure, employment pressure, disruption of medical education, wish to return to clinical rotation, pursuing clinical work after graduation, the impact of COVID-19 and worried about being infected were among the demographic characteristics listed. Each participant's frequency of being infected with COVID-19 was evaluated using the following three-point dimensions: \"once,\" \"more than two times,\" and \"no.\" Every participant was asked to rate the epidemic's impact on their future medical careers on a three-point scale: \"no impact,\" \"positive,\" and \"negative.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Pittsburgh Sleep Quality Index (PSQI)\u003c/h2\u003e \u003cp\u003eThe PSQI [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] was utilized to evaluate sleep disorders [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Seven sections comprise the 19-question questionnaire: sleep duration, sleep disturbance, sleep latency, subjective sleep quality, habitual sleep efficiency, daytime dysfunction, and sleep medications. With a total of 21 points, the PSQI reflects the quality of each component. Participants in this study were classified as having a sleep disorder if their overall PSQI score was 8 or above [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This scale had a Cronbach's alpha of 0.83 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Self-rating Anxiety Scale (SAS)\u003c/h2\u003e \u003cp\u003eSAS was used to assess anxiety symptoms [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Each symptom on the SAS questionnaire has a frequency scale from 1 to 4, comprising 20 items. After determining the raw score for each item, we multiplied it by 1.25 to get the standardized value. The SAS norm, which captures the subjective experiences of people who tend to stress out, was utilized to establish a cutoff threshold for anxiety at 50 points on a standardized scale [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A Cronbach's alpha of 0.84 was recorded for this scale [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Self-rating Depression Scale (SDS)\u003c/h2\u003e \u003cp\u003eSDS, a brief 20-question questionnaire, was used to measure depressive symptoms [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The validity and reliability of this lengthy self-administered survey are high. One can get an overall SDS score by adding the results from all 20 questions. To get the standardized score, multiply the SDS score by 1.25, keeping the whole value. The SDS norm, which represents the subjective experiences of individuals with depression, was utilized to establish a cutoff point for depression at 40 on the standardized total score [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The SDS has a Cronbach's alpha of 0.73 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted using SPSS 25.0. The use of descriptive analysis allowed for the characterization of demographic features. To display categorical data, we use the numbers N and %, while to summarize quantitative data, we use the numbers M and SD. Dissimilarities between pupils suffering from sleep disorders and those without were examined using a chi-square test.\u003c/p\u003e \u003cp\u003eFurthermore, binary logistic regression was employed to examine potential risk factors for sleep disorders, with all variables that were significant in the chi-square test serving as dependent variables. We employed the enter-LR technique. The 95% confidence interval (CI) of the ratio of ratios (OR) showed the degree to which different factors were linked to sleep disorders. For the test, a p-value less than 0.05 was deemed statistically significant. Analyses were performed using the receiver operating characteristic curve (ROC curve) to assess the predictive value of relevant variables for sleep disorders.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic characteristics of participants\u003c/h2\u003e \u003cp\u003eThe prevalence of sleep disorders among medical students was 82.3% (N\u0026thinsp;=\u0026thinsp;983). 69.8% were undergraduate and graduate students (N\u0026thinsp;=\u0026thinsp;833), and 30.2% were doctoral students (N\u0026thinsp;=\u0026thinsp;361). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic characteristics of the participants. 50.2% were male participants (N\u0026thinsp;=\u0026thinsp;599) and 49.8% were female participants (N\u0026thinsp;=\u0026thinsp;595), who accounted for almost the same percentage of the total. In addition, 60.6% (N\u0026thinsp;=\u0026thinsp;724) of the medical students had been infected once, 29.3% (N\u0026thinsp;=\u0026thinsp;350) had been infected more than two times, and 10.1% (N\u0026thinsp;=\u0026thinsp;120) had not been infected with COVID-19.\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\u003eSocio-demographic characteristics of the medical students after the COVID-19 epidemic (N\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate and Postgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirthplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountryside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly exercise frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne or two times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than three times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonality trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntroversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtroversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral and with chronic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeen infected with COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, once\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, more than two times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn clinic rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisruption of medical education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWish to return to clinical rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePursuing clinical work after graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe impact of COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorried about being infected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: The question of \u0026ldquo;Financial pressure\u0026rdquo;: Are you feeling heavy financial pressure? The question of \u0026ldquo;Employment pressure\u0026rdquo;: Are you feeling heavy employment pressure? The question of \u0026ldquo;Disruption of medical education\u0026rdquo;: Has the epidemic disrupted medical education? The question of \u0026ldquo;Wish to return to clinical rotation\u0026rdquo;: Do you wish to return to clinical rotation on time? The question of \u0026ldquo;Pursuing clinical work after graduation\u0026rdquo;: Whether you wish to pursue a clinical career after graduation? The question of \u0026ldquo;The impact of COVID-19\u0026rdquo;: What do you think about the impact of COVID-19 on healthcare? The question of \u0026ldquo;Worried about being infected\u0026rdquo;: Are you worried about infecting COVID-19 when you return to your clinical rotation?\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Clinical characteristics of participants\u003c/h2\u003e \u003cp\u003eThe total scores of SDS, SAS and PSQI of the participants were 49.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.678, 44.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.693 and 10.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.326 respectively. Of the seven components of the PSQI, the mean scores of subjective sleep quality, sleep latency, sleep duration, sleep disturbance, the use of sleep medication, and daytime dysfunction were above 1. In addition, the mean scores for solely habitual sleep efficiency were less than 1 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eThe scores of PSQI, SAS, and SDS of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep latency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitual sleep efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaytime dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: PSQI, Pittsburgh Sleep Quality Index; SDS, Self-rating Depression Scale; SAS, Self-rating Anxiety Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of medical students with and without sleep disorders\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e compares the differences between medical students with and without sleep disorders. Doctoral students were more likely to have sleep disorders than undergraduate and graduate students (χ2\u0026thinsp;=\u0026thinsp;19.594, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Medical students with chronic conditions and general health status were more likely to have sleep disorders than medical students with good health status (χ2\u0026thinsp;=\u0026thinsp;23.289, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Medical students who wanted to work in clinical medicine after graduation were more likely to have sleep disorders than those who did not want to work in clinical medicine (χ2\u0026thinsp;=\u0026thinsp;21.272, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of medical students with and without sleep disorders after the COVID-19 epidemic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eParticipants(N\u0026thinsp;=\u0026thinsp;1194)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNone-Sleep disorders\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;211)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSleep\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003edisorders\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;983)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eχ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate and Postgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e659 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e324 (27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e506 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e477 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirthplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountryside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e512 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e471 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly exercise frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e329 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne or two times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91(7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than three times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302 (25.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonality trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntroversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e475 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtroversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e508 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e351 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral and with chronic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e632 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeen infected with COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, once\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e604 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, more than two times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e278 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19(1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn clinic rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e464 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e527 (44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e456 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e517 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466 (39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisruption of medical education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e495 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e488 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWish to return to clinical rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e472 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e511 (42.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePursuing clinical work after graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e474 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe impact of COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e326 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorried about being infected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e506 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e477 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression or not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e267.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e976 (81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety or not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e757 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The question of \u0026ldquo;Financial pressure\u0026rdquo;: Are you feeling heavy financial pressure? The question of \u0026ldquo;Employment pressure\u0026rdquo;: Are you feeling heavy employment pressure? The question of \u0026ldquo;Disruption of medical education\u0026rdquo;: Has the epidemic disrupted medical education? The question of \u0026ldquo;Wish to return to clinical rotation\u0026rdquo;: Do you wish to return to clinical rotation on time? The question of \u0026ldquo;Pursuing clinical work after graduation\u0026rdquo;: Whether you wish to pursue a clinical career after graduation? The question of \u0026ldquo;The impact of COVID-19\u0026rdquo;: What do you think about the impact of COVID-19 on healthcare? The question of \u0026ldquo;Worried about being infected\u0026rdquo;: Are you worried about infecting COVID-19 when you return to your clinical rotation? Bolding means the P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\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=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluencing factors of sleep disorders among medical students after the COVID-19 epidemic period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluence factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.527~3.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.454~2.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.114~2.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePursuing clinical work after graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.418~2.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.407~3.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.193~2.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.028~1.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePursuing clinical work after graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.197~2.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.043~1.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.264~2.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.866~1.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.867\u0026thinsp;~\u0026thinsp;1.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePursuing clinical work after graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.953~1.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.981\u0026thinsp;~\u0026thinsp;1.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.114\u0026thinsp;~\u0026thinsp;1.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The question of \u0026ldquo;Employment pressure\u0026rdquo;: Are you feeling heavy employment pressure? The question of \u0026ldquo;Pursuing clinical work after graduation\u0026rdquo;: Whether you wish to pursue a clinical career after graduation? Bolding means the P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInterestingly, medical students who did not feel employment stress were likelier to have sleep disorders than those who felt employment stress (χ2\u0026thinsp;=\u0026thinsp;8.344, P\u0026thinsp;=\u0026thinsp;0.004). However, there was no significant difference in the prevalence of sleep disorders among medical students who had been infected once, infected multiple times, and those who had not been infected with COVID-19 (χ2\u0026thinsp;=\u0026thinsp;2.901, P\u0026thinsp;=\u0026thinsp;0.235). Notably, medical students with depression (χ2\u0026thinsp;=\u0026thinsp;267.378, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and anxiety (χ2\u0026thinsp;=\u0026thinsp;46.489, P\u0026thinsp;\u0026lt;\u0026thinsp;001) were more likely to have sleep disorders than those without depression and anxiety.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Risk factors of sleep disorders in medical students\u003c/h2\u003e \u003cp\u003eNext, we used binary logistic regression to explore the factors influencing sleep disorders among medical students after the epidemic. The presence of a sleep disorder was used as the dependent variable, and variables with significant differences in univariate analyses were used as independent variables.\u003c/p\u003e \u003cp\u003eFirstly, we included the four independent variables: academic level, health condition, employment pressure, and pursuit of clinical work (Model 1). The results found that doctoral students had a 2.243 times higher risk of developing sleep disorders than graduate and undergraduate students (OR\u0026thinsp;=\u0026thinsp;2.243, 95% CI 1.527\u0026ndash;3.295). Medical students with general health status and chronic diseases had 1.976 times the risk of developing sleep disorders than medical students with good health status (OR\u0026thinsp;=\u0026thinsp;1.976, 95% CI 1.454\u0026ndash;2.686). In addition, medical students who wished to work in clinical medicine after graduation had 1.960 times the risk of developing sleep disorders than those who did not want to work in clinical medicine (OR\u0026thinsp;=\u0026thinsp;1.960, 95% CI 1.418\u0026ndash;2.711). Interestingly, students who did not feel under heavy employment pressure had a 1.526 times higher risk of developing sleep disorders than those who did not feel under heavy employment pressure (OR\u0026thinsp;=\u0026thinsp;1.526, 95% CI 1.114\u0026ndash;2.091).\u003c/p\u003e \u003cp\u003eSecondly, we added anxiety as a dependent variable for further analysis (Model 2). We found that academic level (OR\u0026thinsp;=\u0026thinsp;2.082, 95% CI 1.407\u0026ndash;3.080), health condition (OR\u0026thinsp;=\u0026thinsp;1.643, 95% CI 1.193\u0026ndash;2.264), employment pressure (OR\u0026thinsp;=\u0026thinsp;1.422, 95% CI 1.028\u0026ndash;1.965), and pursuing clinical work after graduation (OR\u0026thinsp;=\u0026thinsp;1.671, 95% CI 1.197\u0026ndash;2.335) still influence sleep disorders among medical students. In addition, anxiety was a risk factor for sleep disorders among medical students after the epidemic (OR\u0026thinsp;=\u0026thinsp;1.061, 95% CI 1.043\u0026ndash;1.079).\u003c/p\u003e \u003cp\u003eFinally, we added depression as a dependent variable (Model 3). The results found that depression was a risk factor for sleep disorders among medical students after the epidemic (OR\u0026thinsp;=\u0026thinsp;1.151, 95% CI 1.114\u0026ndash;1.188). In addition, academic level influenced sleep disorders among medical students (OR\u0026thinsp;=\u0026thinsp;1.908, 95% CI 1.264\u0026ndash;2.880). However, health condition (P\u0026thinsp;=\u0026thinsp;0.249), employment pressure (P\u0026thinsp;=\u0026thinsp;0.248), pursuing clinical work after graduation (P\u0026thinsp;=\u0026thinsp;0.091), and anxiety (P\u0026thinsp;=\u0026thinsp;0.912) did not influence sleep disorders among medical students. In addition, the area under the ROC curve for depression is 0.689.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe current study is the first large-scale cross-sectional investigation to explore the prevalence of sleep disorders among medical students after the COVID-19 pandemic. Our main findings as follows: firstly, the incidence of sleep disorders among medical students after the COVID-19 pandemic is 82.3%; secondly, academic level, health condition, employment pressure, and pursuing clinical work after graduation influence sleep disorders; and finally, depression and high academic level are independent risk factors of sleep disorders among medical students.\u003c/p\u003e \u003cp\u003eThe main finding is that the prevalence of sleep disorders is 82.3% among medical students after the COVID-19 pandemic. This result aligns with previous research [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For instance, a meta-analysis shows that sleep disorders are prevalent in the medical student population [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Another study found a 76% incidence of poor sleep quality among medical students [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, the current study reveals a higher incidence of sleep disorders among medical students than earlier studies during COVID-19 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, a study in Greece found a sleep disorder incidence of 52.4% among medical students during the COVID-19 pandemic [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, a systematic review and meta-analysis reported a 52% incidence of sleep disorders among medical students during the COVID-19 pandemic [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These inconsistencies suggest differences in the prevalence of sleep disorders among medical students after and during the COVID-19 epidemic.\u003c/p\u003e \u003cp\u003eFurthermore, we found that the academic level influences sleep disorders among medical students after the COVID-19 pandemic. Previous studies have limited exploration of sleep disorder incidence among graduate students [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It is well known that there is a strong relationship between academic level and mental health [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, it has been found that the higher the level of education, the higher the prevalence of sleep disorders [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For example, doctoral students had significantly higher levels of anxiety and sleep problems as well as depressive symptoms than master's students [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In addition, one study found that approximately 83% of graduate students experienced sleep disorders during the COVID-19 pandemic [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our findings provide new literature on the relationship between academic levels and sleep disorders.\u003c/p\u003e \u003cp\u003eAnother main finding of our study is that depression is an independent risk factor for sleep disorders among medical students after the COVID-19 pandemic. Depression is prevalent, costly, debilitating, and associated with an increased risk of suicide [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. There is a close relationship between depression and sleep disorders [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Most sleep disorders patients experience depressive episodes, and higher levels of depression are associated with an increased incidence of sleep disorders [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Hence, the relationship between depression and sleep disorders may be bidirectional [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. For instance, a meta-analysis found a positive correlation between depression and sleep disorders [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Studies have shown a higher incidence of depression in patients with sleep disorders [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The reasons of depression affects sleep disorders include: Firstly, Patients with depression have reduced circadian rhythm amplitude, and various treatments for depression have been shown to affect circadian rhythms [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Secondly, severe depressive disorders are associated with functional impairments in the structural network regulating rapid eye movement (REM) sleep [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Finally, the medial prefrontal cortex (mPFC) is a crucial region regulating depression and sleep, and significant changes in neural activity in the mPFC subregions of depression patients have been observed [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur regression model reveals that anxiety is a factor influencing sleep disorders among medical students after the COVID-19 pandemic, consistent with previous research results [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. For example, research indicates that higher levels of anxiety are associated with a higher incidence of sleep disorders [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. A meta-analysis also found a positive correlation between anxiety levels and sleep disorders [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. However, when we include depression in the regression model, anxiety does not impact the sleep disorders of medical students. We hypothesize that depression has a more significant influence on sleep disorders among medical students than anxiety. Indeed, the relationship between depression, anxiety, and sleep disorders may be complex [\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Therefore, further research is needed to systematically elucidate the relationship between depression, anxiety, and sleep disorders.\u003c/p\u003e \u003cp\u003eIt is noteworthy that, before initiating this study, we predicted that infecting COVID-19 would affect the sleep disorders of medical students. Surprisingly, our study results indicate no difference in the incidence of sleep disorders among medical students infected once, more than two times, or not at all with COVID-19. This result may be attributed to China being one of the earliest countries to commence global COVID-19 vaccination campaigns, leading to a significant decline in COVID-19-related mortality rates [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Previous research suggests that repeated COVID-19 infections further increase the risks of death, hospitalization, and sequelae[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. However, no research exists exploring the impact of repeated infections and the number of infections on sleep disorders. Therefore, more studies are needed to investigate the effects of COVID-19 infection on sleep.\u003c/p\u003e \u003cp\u003eDespite providing valuable insights, our current study has limitations that must be acknowledged. Firstly, the cross-sectional study limits our detailed understanding of how COVID-19 affects sleep disorders. Future research should employ longitudinal studies to understand the temporal effects of these results. Secondly, due to the online survey design, most studies used self-administered questionnaires without clinical diagnostic confirmation. However, all included studies used validated screening tools such as the SDS, SAS, and PSQI. Therefore, future research is recommended to use rigorous clinical diagnosis to confirm our results. Thirdly, the questionnaire did not include whether participants were isolated during the COVID-19 pandemic, and it remains unclear whether this would affect sleep disorders. Finally, the current study only assessed medical students in China, and generalizing the results to the entire medical student population may be challenging.\u003c/p\u003e \u003cp\u003eIn conclusion, the incidence of sleep disorders is high among medical students after the COVID-19 pandemic. Additionally, depression and high academic levels are independent risk factors for sleep disorders among medical students. Thus, to reduce the incidence of sleep disorders among medical students after the COVID-19 pandemic, targeted intervention strategies must be developed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe protocol for the research project had been approved by\u0026nbsp;the Institutional Review Board (IRB) of the Tianjin Anding Hospital (approving number: 2023-027), and had therefore been performed following the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Patients\u0026apos; informed consent forms were obtained, and their anonymity was protected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe data supporting this study\u0026apos;s findings are available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution statement:\u0026nbsp;\u003c/strong\u003eJiao Liu, Daliang Sun and Guoshuai Luo were responsible for the study design. Jiao Liu, Baozhu Li and Ran Zhang were responsible for patient recruitment and data collection. Jiao Liu was responsible for statistical analysis. Jiao Liu, Daliang Sun and Guoshuai Luo were involved in conceptualizing, writing and editing the manuscript, as well as responding to reviewers. All authors contributed to and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe want to extend our gratitude to Shuo Wang, Yifan Jing, Zaimina Xuekelaiti, Yan Zhou, Ru Hao, Lidan Yuan, Linxuan Wang, and Ziqing Zhang from Tianjin Medical University for their assistance in collecting data. The authors thank the subjects whose participation made this study possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKevadiya BD, et al. Diagnostics for SARS-CoV-2 infections. Nat Mater. 2021;20(5):593\u0026ndash;605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins V, et al. COVID-19: from an acute to chronic disease? Potential long-term health consequences. Crit Rev Clin Lab Sci. 2021;58(5):297\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChotpitayasunondh T, et al. Influenza and COVID-19: What does co-existence mean? Influenza Other Respir Viruses. 2021;15(3):407\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee AB. COVID-19, Critical Illness, and Sleep. Crit Care Med. 2022;50(6):1021\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis HE, et al. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu S, et al. Epidemiology, clinical presentation, pathophysiology, and management of long COVID: an update. Mol Psychiatry. 2023;28(10):4056\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarei M, et al. Long-term side effects and lingering symptoms post COVID-19 recovery. Rev Med Virol. 2022;32(3):e2289.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Xu E, Al-Aly Z. Risks of mental health outcomes in people with covid-19: cohort study. BMJ. 2022;376:e068993.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZawilska JB, Kuczyńska K. Psychiatric and neurological complications of long COVID. J Psychiatr Res. 2022;156:349\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandharipande P, et al. Mitigating neurological, cognitive, and psychiatric sequelae of COVID-19-related critical illness. Lancet Respir Med. 2023;11(8):726\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao W, et al. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 2020;287:112934.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Z, et al. Mental health problems and correlates among 746 217 college students during the coronavirus disease 2019 outbreak in China. Epidemiol Psychiatr Sci. 2020;29:e181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStanyte A, et al. Mental Health and Wellbeing in Lithuanian Medical Students and Resident Doctors During COVID-19 Pandemic. Front Psychiatry. 2022;13:871137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian F, et al. Should Medical Students Be Overprotected? A Survey from China and Review About the Roles of Medical Student Under the COVID-19. Risk Manag Healthc Policy. 2023;16:327\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEleftheriou A, et al. Sleep Quality and Mental Health of Medical Students in Greece During the COVID-19 Pandemic. Front Public Health. 2021;9:775374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTahir MJ, et al. Internet addiction and sleep quality among medical students during the COVID-19 pandemic: A multinational cross-sectional survey. PLoS ONE. 2021;16(11):e0259594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng P, et al. The prevalence and risk factors of mental problems in medical students during COVID-19 pandemic: A systematic review and meta-analysis. J Affect Disord. 2023;321:167\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinzon RT, et al. Persistent neurological manifestations in long COVID-19 syndrome: A systematic review and meta-analysis. J Infect Public Health. 2022;15(8):856\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng J, et al. Mental health and cognitive function among medical students after the COVID-19 pandemic in China. Front Public Health. 2023;11:1233975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng J, Zeng XX, Tu Q. A gloomy future for medical students in China. Lancet. 2013;382(9908):1878.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng P, et al. High prevalence and risk factors of dropout intention among Chinese medical postgraduates. Med Educ Online. 2022;27(1):2058866.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao D, Zhang Z. Changes in public trust in physicians: empirical evidence from China. Front Med. 2019;13(4):504\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng J, et al. The prevalence of turnover intention and influencing factors among emergency physicians: A national observation. J Glob Health. 2022;12:04005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao X, Xu Y. Overwork death among doctors a challenging issue in China. Int J Cardiol. 2019;289:152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Zhao L, Ye XC. Short Healthc professionals China Bmj. 2016;354:i4860.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai CZ et al. Effect of the COVID-19 Pandemic on Medical Student Career Perceptions: Perspectives from Medical Students in China. Int J Environ Res Public Health, 2021. 18(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, et al. Prevalence and influencing factors of sleep disturbance among medical students under the COVID-19 pandemic. Eur Arch Psychiatry Clin Neurosci; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi G, Sanchis-Gomar F, Henry BM. COVID-19 and its long-term sequelae: what do we know in 2023? Pol Arch Intern Med, 2023. 133(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoi Y, et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res. 2000;97(2\u0026ndash;3):165\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo RT, Fong TC. Factor structure of the Chinese version of the Pittsburgh sleep quality index in breast cancer patients. Sleep Med. 2014;15(5):565\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen\u0026iacute;tez ID, et al. Sleep and Circadian Health of Critical COVID-19 Survivors 3 Months After Hospital Discharge. Crit Care Med. 2022;50(6):945\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZung WW. A rating instrument for anxiety disorders. Psychosomatics. 1971;12(6):371\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunstan DA, Scott N. Norms for Zung's Self-rating Anxiety Scale. BMC Psychiatry. 2020;20(1):90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu CY, et al. The prevalence and influencing factors in anxiety in medical workers fighting COVID-19 in China: a cross-sectional survey. Epidemiol Infect. 2020;148:e98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZung WW, SELF-RATING DEPRESSION. \u003cem\u003eSCALE.\u003c/em\u003e Arch Gen Psychiatry, 1965. 12: p. 63\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJokelainen J, et al. Validation of the Zung self-rating depression scale (SDS) in older adults. Scand J Prim Health Care. 2019;37(3):353\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi J, et al. A study on the correlation between family dynamic factors and depression in adolescents. Front Psychiatry. 2022;13:1025168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeoane HA, et al. Sleep disruption in medicine students and its relationship with impaired academic performance: A systematic review and meta-analysis. Sleep Med Rev. 2020;53:101333.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmojali AI, et al. The prevalence and association of stress with sleep quality among medical students. J Epidemiol Glob Health. 2017;7(3):169\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnwer S, et al. Evaluation of Sleep Habits, Generalized Anxiety, Perceived Stress, and Research Outputs Among Postgraduate Research Students in Hong Kong During the Coronavirus (COVID-19) Pandemic. J Multidiscip Healthc. 2021;14:3135\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoutepen LC, et al. Associations of adverse childhood experiences with educational attainment and adolescent health and the role of family and socioeconomic factors: A prospective cohort study in the UK. PLoS Med. 2020;17(3):e1003031.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng J, et al. The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: A systematic review and meta-analysis. Psychiatry Res. 2021;301:113863.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePizuńska D, et al. Well-being among PhD candidates. Psychiatr Pol. 2021;55(4):901\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson D, et al. Adult mental health outcomes of adolescent depression: A systematic review. Depress Anxiety. 2018;35(8):700\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith K. Mental health: a world of depression. Nature. 2014;515(7526):181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarwaha S, et al. Novel and emerging treatments for major depression. Lancet. 2023;401(10371):141\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrrila AS, et al. Sleep in adolescent depression: physiological perspectives. Acta Physiol (Oxf). 2015;213(4):758\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein AN, Walker MP. The role of sleep in emotional brain function. Annu Rev Clin Psychol. 2014;10:679\u0026ndash;708.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandi-Perumal SR, et al. Clarifying the role of sleep in depression: A narrative review. Psychiatry Res. 2020;291:113239.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaglioni C, et al. Sleep and mental disorders: A meta-analysis of polysomnographic research. Psychol Bull. 2016;142(9):969\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulz P, Steimer T. Neurobiology of circadian systems. CNS Drugs. 2009;23(Suppl 2):3\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbdlahad S, et al. Comparing neural correlates of REM sleep in posttraumatic stress disorder and depression: a neuroimaging study. Psychiatry Res. 2013;214(3):422\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang CH, et al. Ventromedial prefrontal cortex regulates depressive-like behavior and rapid eye movement sleep in the rat. Neuropharmacology. 2014;86:125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang C, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021;397(10270):220\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChellappa SL, Aeschbach D. Sleep and anxiety: From mechanisms to interventions. Sleep Med Rev. 2022;61:101583.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer CA, Alfano CA. Anxiety modifies the emotional effects of sleep loss. Curr Opin Psychol. 2020;34:100\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox RC, Olatunji BO. Sleep in the anxiety-related disorders: A meta-analysis of subjective and objective research. Sleep Med Rev. 2020;51:101282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyer M, et al. Relationship between sleep disturbance and depression, anxiety, and functioning in college students. Depress Anxiety. 2013;30(9):873\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDifrancesco S, et al. Sleep, circadian rhythm, and physical activity patterns in depressive and anxiety disorders: A 2-week ambulatory assessment study. Depress Anxiety. 2019;36(10):975\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng J, et al. The prevalence of depression, anxiety, and sleep disturbances in COVID-19 patients: a meta-analysis. Ann N Y Acad Sci. 2021;1486(1):90\u0026ndash;111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, et al. Anxiety prevalence and associated factors among frontline nurses following the COVID-19 pandemic: a large-scale cross-sectional study. Front Public Health. 2023;11:1323303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, et al. Effectiveness of inactivated COVID-19 vaccines against mild disease, pneumonia, and severe disease among persons infected with SARS-CoV-2 Omicron variant: real-world study in Jilin Province, China. Emerg Microbes Infect. 2023;12(1):2149935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowe B, Xie Y, Al-Aly Z. Acute and postacute sequelae associated with SARS-CoV-2 reinfection. Nat Med. 2022;28(11):2398\u0026ndash;405.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sleep Disorders, Medical students, COVID-19, Depression, Anxiety","lastPublishedDoi":"10.21203/rs.3.rs-4144293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4144293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe prevalence of sleep disorders among medical students was high during the COVID-19 pandemic. However, there are fewer studies of sleep disorders in medical students after the COVID-19 pandemic. This study aimed to investigate the prevalence and factors influencing sleep disorders among Chinese medical students after COVID-19.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe enrolled 1194 medical students. The Self-administered scale was used to collect the demographic characteristics. The Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) were used to assess subjects' depression, anxiety, and sleep disorders, respectively. The chi-square test and binary logistic regression were used to identify factors that influence sleep disorders. The receiver operating characteristic (ROC) curve was used to assess the predictive value of relevant variables for sleep disorders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that the prevalence of sleep disorders among medical students after COVID-19 was 82.3%. According to logistic regression results, medical students with depression were 1.151 times more likely to have sleep disorders than those without depression (OR\u0026thinsp;=\u0026thinsp;1.151, 95% CI 1.114 to 1.188). Doctoral students were 1.908 times more likely to have sleep disorders than graduate and undergraduate students (OR\u0026thinsp;=\u0026thinsp;1.908, 95% CI 1.264 to 2.880). In addition, the area under the ROC curve for depression is 0.689.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prevalence of sleep disorders among medical students is high after COVID-19. In addition, high academic level and depression are risk factors for sleep disorders. Therefore, medical colleges and administrators should pay more attention to sleep disorders in medical students after the COVID-19 pandemic. Regular assessment of sleep disorders and depression is extremely necessary.\u003c/p\u003e","manuscriptTitle":"Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 07:13:32","doi":"10.21203/rs.3.rs-4144293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-03-25T08:35:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-25T08:35:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-03-21T14:35:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccdfb41d-a959-4a01-bb28-dc32f660f06e","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-03-28T07:13:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 07:13:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4144293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4144293","identity":"rs-4144293","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}