From adversity to depression: a structural equation modelling study of the public health burden and mediating pathways among female college students in China

From adversity to depression: a structural equation modelling study of the public health burden and mediating pathways among female college students in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From adversity to depression: a structural equation modelling study of the public health burden and mediating pathways among female college students in China Zhen Zhou, Ling Yuan, Mao-Rui Yang, Zhao-Xin Wang, Di-Er Cheng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7494165/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Mental health challenges such as anxiety, insomnia, burnout, and depression are increasingly recognised as major public health concerns among university students. Female undergraduates may be particularly vulnerable due to gendered social expectations, academic pressures, and socioeconomic inequalities. However, the interplay of these conditions and their determinants remains underexplored in Chinese populations. Methods A cross-sectional survey was conducted among 354 female undergraduates at Hainan Medical University in 2024. Validated self-report scales assessed anxiety, sleep quality, burnout, and depression. Structural equation modelling (SEM) was used to compare direct and indirect pathways, with demographic and academic factors (birthplace, major, monthly living expenses, and GPA) included as predictors. Results The indirect effect model fit the data better than the direct model (χ²/df = 2.15, CFI = 0.95, RMSEA = 0.05). Sleep disturbances significantly predicted anxiety (β = 0.45, p < 0.001) and burnout (β = 0.38, p < 0.001), while anxiety mediated the association between sleep disturbances and depression (indirect effect = 0.64, 95% CI [0.38, 1.10], p = 0.001). Burnout did not significantly mediate this pathway (p = 0.149). Students in clinical medicine and those with lower monthly expenditure reported greater sleep disturbances, urban students experienced more somatic depressive symptoms, and low GPA was associated with higher burnout-related behaviours. Conclusions Female undergraduates face interconnected mental health risks that constitute a pressing public health issue. Sleep disturbances and anxiety emerged as key prevention targets, while socioeconomic and academic disparities further increased vulnerability. Public health strategies should prioritise gender-sensitive, population-level interventions, including sleep health promotion, financial support, and campus-based mental health services to reduce risks of depression and academic burnout. Anxiety Insomnia Burnout Depression Female college students Structural equation modelling Public health China Figures Figure 1 Figure 2 Background The Prevalence and Interplay of Anxiety, Sleep Disorders, Burnout, and Depression in Female College Students Mental health issues among female college students represent a significant global concern and a critical public health priority, with implications for equity in education and long-term wellbeing. Anxiety, sleep disturbances, burnout, and depression form interconnected crises shaped by gender-specific experiences, acting as core indicators of psychological distress. Epidemiological data reveal striking gender disparities: global studies report severe anxiety symptoms in over 35% of female students and depression in 25–30%, rates approximately 1.5–2 times higher than those observed in male peers [ 1 ]. Recent large-scale Chinese surveys and meta-analyses confirm similarly high or higher prevalence rates of anxiety and depression in female undergraduates [ 2 – 4 ]. Sleep disturbances are also common, affecting 25–40% of female students in international samples [ 5 ], with recent Chinese studies documenting high co-occurrence of poor sleep quality and psychological distress in this group[ 6 – 8 ]. Burnout—characterised by emotional exhaustion and academic cynicism—impacts up to 45% of female undergraduates, particularly in high-pressure fields such as STEM and medicine [ 9 ]. Critically, these conditions rarely occur in isolation; they interact through complex, gendered pathways. As evidenced in our findings, sleep disturbances significantly predict both anxiety and burnout, with anxiety subsequently mediating depression risk. Anxiety, often intensified by gendered social expectations, is associated with hypothalamic-pituitary-adrenal (HPA) axis hyperactivity. This neurobiological response disrupts circadian rhythms, elevating vulnerability to sleep disturbances [ 10 ]. Female students with elevated anxiety exhibit a 2–3 times greater risk of developing significant sleep problems. Conversely, impaired sleep hinders emotional regulation capacity, thereby amplifying susceptibility to depressive symptoms [ 11 ]—a pathway empirically supported by our model showing sleep disturbances exert indirect effects on depression through anxiety. Burnout functions bidirectionally within this nexus. Gendered academic pressures, such as stereotype threat in male-dominated fields and disproportionate workload burdens, are well-documented contributors to burnout in students [ 12 , 13 ]. Recent evidence from Chinese adolescent populations further shows that academic stress can impair sleep quality, with anxiety and school burnout acting as sequential mediators [ 14 ]. Across multiple studies, approximately 41% of students report high levels of emotional exhaustion, the core component of burnout [ 15 ]. Cross‑sectional surveys of medical and nursing students in Asia and Europe show similar patterns; high emotional exhaustion, depersonalisation and low personal accomplishment are prevalent [ 16 , 17 ]. Burnout is closely linked to sleep problems: Iranian nursing students with greater sleep disturbances reported higher academic burnout scores, whereas greater social intimacy appeared protective [ 18 ]. Longitudinal studies further suggest that emotional exhaustion and excessive daytime sleepiness reinforce each other, creating a vicious cycle that diminishes academic efficacy [ 19 ]. The Role of Demographic and Academic Factors in Shaping Female Students’ Mental Health Demographic characteristics and academic experiences act as critical contextual amplifiers within the anxiety-sleep-burnout-depression cycle. These factors intersect with gender-specific stressors, potentially heightening vulnerability to psychological distress among female undergraduates. Birthplace introduces distinct challenges shaped by pre-college environments and resources. Female students from rural backgrounds often enter university with limited prior access to mental health services and may face greater cultural stigma regarding help-seeking. This resource gap can amplify burnout risk, with studies suggesting rural female students experience rates 30–40% higher than their urban counterparts [ 20 ]. Our findings further reveal a nuanced pattern: urban students exhibited more somatic depressive symptoms, potentially reflecting heightened exposure to urban stressors such as intense academic competition, cost-of-living pressures, or social comparison, which may manifest physically. Academic majors, especially in high-stress fields, significantly interact with gender to affect risk. For example, majors like clinical medicine—known for intense schedules, high-stakes assessments, and documented gender disparities in workload and support- are linked to burnout rates that are 2–3 times higher among female students compared to students in less demanding disciplines [ 12 ]. Supporting this, our findings show that majoring in high-pressure areas such as clinical medicine, common in our sample, is a key predictor of sleep disturbances. Socioeconomic status, operationalised here as monthly living expenses, generates unique financial stressors that often carry gendered dimensions. Female students reporting lower living expenses (<¥1,000) may experience heightened anxiety about basic needs, part-time work demands, or perceived social disadvantage, contributing to poorer mental health. Research indicates these students report approximately 40% higher anxiety levels and 30% more sleep problems compared to peers with more adequate financial resources [ 21 ]. This aligns with our discovery that lower living expenses correlate with increased sleep instability and greater reliance on sleep medication, suggesting financial precarity directly impacts sleep physiology and coping mechanisms. Academic performance (GPA) exhibits a complex, bidirectional relationship with mental health, moderated by gendered experiences of achievement pressure. Lower GPA can fuel negative self-perceptions, self-doubt, and fear of failure—concerns often intensified by societal expectations of female competence and perfectionism. This, in turn, increases vulnerability to anxiety and burnout [ 9 ]. Our data support this link, revealing that students with GPAs < 2.0 exhibit significantly more burnout-related inappropriate academic behaviours compared to higher-performing peers. Unresolved Questions and the Need for the Current Study Critical questions remain about how demographic and academic factors influence the relationships among the four outcome indicators. For example, does rural birthplace directly cause depression, or does it influence it through limited mental health resources before college, leading to increased anxiety, which then disrupts sleep and worsens burnout? How does financial stress affect anxiety—through sleep problems, burnout, or both? These gaps highlight a broader issue: research often combines genders, ignoring how female-specific experiences shape the dynamics of these outcomes [ 1 , 22 ]. Our study addresses these gaps by focusing on female undergraduates (n = 354) from Hainan Medical University, a sample with diverse backgrounds 49.72% rural, 38.98% in clinical medicine that allows analysis of demographic influences. Using structural equation modelling (SEM) - ideal for testing direct and indirect effects [ 23 ] - we examine whether factors like birthplace and living expenses operate through direct pathways or via intermediaries like anxiety, as suggested by our preliminary finding that the indirect effect model fits better than the direct effect model. Objectives and Hypotheses of the Current Study This study examines the complex relationships among anxiety, sleep disturbances, burnout, and depression in female college students, with emphasis on how birthplace, major, living expenses, and GPA influence these pathways. Using structural equation modelling (SEM), two models are tested: the direct effect model, where demographic/academic factors directly predict anxiety, sleep disturbances, burnout, and depression; and the indirect effect model, demographic/academic factors predict sleep disturbances, which then predict anxiety and burnout; anxiety subsequently predicts depression, while burnout’s role in depression is explored. The hypotheses are: (1) female students from rural areas, enrolled in high-stress majors like clinical medicine, with lower living expenses and lower GPAs, will show higher levels of anxiety, sleep problems, burnout, and depression. (2) Sleep disturbances will positively predict symptoms of anxiety and burnout in female college students. (3) Anxiety, sleep disturbances, and burnout will each positively predict depressive symptoms. (4) The indirect effect model will demonstrate a significantly better fit than the direct effect model for female college students. Method Participants The sample comprised female students from Hainan Medical University in Hainan Province, southern China. Recruitment took place on campus at Hainan Medical University starting July 6th, 2024. To qualify, participants had to be enrolled in a full-time undergraduate program and give informed consent. Those with a self-reported history of severe mental illnesses, such as schizophrenia or neurological disorders, were excluded to focus on subclinical symptoms. A total of 354 female students participated in the study—recruitment involved convenience sampling, with invitations posted on university bulletin boards and online student groups. Before data collection, ethical approval was obtained from the Hainan Medical University Institutional Review Board, and all participants gave written informed consent. Measures Anxiety Anxiety symptoms were evaluated using the Self-Rating Anxiety Scale (SAS)[ 24 ], a widely adopted 20-item self-report tool designed to assess the severity of anxiety in adults. The scale covers both psychological symptoms (e.g., "I feel nervous and anxious") and physical symptoms (e.g., "I have a sense of tightness in my chest"), with 15 items positively worded (indicating the presence of anxiety) and five items reverse-coded (indicating absence of anxiety, e.g., "I feel calm and can sit still easily"). Participants indicated how often they experienced each symptom over the past week on a 4-point Likert scale: 1 = "none or rarely," 2 = "sometimes," 3 = "often," and 4 = "almost always." Raw scores are then converted to standard scores via a conversion table, with scores of 50 or above signifying the presence of anxiety: 50–59 suggests mild anxiety, 60–69 moderate, and 70 or more severe. The SAS has shown reliable psychometric properties in Chinese populations, with previous studies reporting internal consistency (Cronbach’s α = 0.83–0.88 [ 27 , 28 ]) and test-retest reliability (r = 0.82–0.85). In this sample, internal consistency was also satisfactory (Cronbach’s α = 0.85), indicating it is appropriate for assessing anxiety symptoms among college students. Sleep Disorders Sleep disorders were measured using the Self-Rating Scale of Sleep (SRSS, Chinese-standardised version)[ 25 ], a 10-item self-report tool created to assess sleep quality and disturbances in Chinese populations. The scale addresses main aspects of sleep health, such as trouble falling asleep, sleep duration, sleep consistency, early awakening, nightmare frequency, sleep quality, daytime impairment from poor sleep, use of hypnotic medications, and overall sleep satisfaction. Each item is rated on a 5-point Likert scale (1 = "never," 2 = "rarely," 3 = "sometimes," 4 = "often," 5 = "always") based on experiences over the past week. Total scores range from 10 to 50, with higher scores indicating more severe sleep disturbances: 10–20 points = normal sleep, 21–30 points = mild sleep disorders, 31–40 points = moderate sleep disorders, and 41–50 points = severe sleep disorders. The SRSS has been extensively validated among Chinese samples, demonstrating strong internal consistency (Cronbach’s α = 0.78–0.85) and test-retest reliability (r = 0.76–0.82) in prior research. This supports its appropriateness for evaluating sleep issues in non-clinical groups. In the present sample, internal consistency was also acceptable (Cronbach’s α = 0.81), confirming its reliability for assessing sleep problems in college students. Burnout Burnout was measured using the College Student Learning Burnout Scale, revised by Chinese scholar Lian Rong[ 26 ]. This popular tool for assessing academic burnout among Chinese college students includes 20 items divided into three factors aligned with the focus of the current study. Emotional exhaustion (low mood): 8 items measure feelings of fatigue and indifference towards studying, such as “Early in the morning, thinking about facing a day of study makes me feel tired“ and "After a full day of studying, I feel exhausted.“ Inappropriate behaviour: 6 items identify maladaptive academic behaviours like "I rarely study after class” and "I only study when there are exams." Low sense of achievement: 6 items assess feelings of incompetence in academic performance, for example, “Mastering professional knowledge is difficult for me" and "College studies have not fully demonstrated my abilities." Participants rated each item based on their experiences over the past month using a 5-point Likert scale: 1 = "completely inconsistent," 2 = "relatively inconsistent," 3 = "uncertain," 4 = "relatively consistent," 5 = "completely consistent." Note that 8 items (1, 3, 6, 8, 11, 13, 15, 18) are reverse-scored to minimise response bias. Higher total scores indicate more severe burnout, while subscale scores specify the intensity of each component. The scale has shown robust psychometric properties in earlier research: the overall Cronbach’s α is 0.865, with subscale α coefficients of 0.812 for emotional exhaustion, 0.704 for inappropriate behaviour, and 0.731 for low sense of achievement. The correlations between each subscale and the total scale range from 0.704 to 0.914 (all p < 0.001), indicating good structural validity. In this sample, confirmatory factor analysis further supported the three-factor model, with high composite reliability (CR = 0.898) and average variance extracted (AVE = 0.748), reflecting strong convergent validity. Internal consistency was also satisfactory for each subscale: emotional exhaustion (α = 0.83), inappropriate behaviour (α = 0.72), and low sense of achievement (α = 0.75), confirming the scale’s appropriateness for assessing burnout in this group of female college students. Depressive Symptoms Depressive symptoms were assessed using the Self-Rating Depression Scale (SDS)[ 27 ], a 20-item self-report instrument designed to measure the presence and severity of depression in adults. The scale encompasses various symptom domains, including emotional (e.g., "I feel downhearted and blue"), cognitive (e.g., “I find it hard to make decisions"), somatic (e.g., “I have trouble falling asleep"), and motivational (e.g., "I have lost interest in things I used to enjoy") aspects. Of the 20 items, 10 are positively worded (indicating depressive symptoms), and 10 are reverse-scored (indicating the absence of symptoms, e.g., "I feel hopeful about the future"). Participants assess each item based on how often they experienced the symptom over the past week, using a 4-point Likert scale: 1 = "none or rarely," 2 = "sometimes," 3 = "often," 4 = "almost always." Raw scores are transformed into standard scores by multiplying the raw total by 1.25. Standard scores of 53 or higher indicate the presence of depressive symptoms, with 53–62 reflecting mild depression, 63–72 indicating moderate depression, and scores of 73 or above signifying severe depression. The SDS has shown excellent psychometric qualities in Chinese populations. Past research indicated internal consistency values (Cronbach’s α) ranging from 0.82 to 0.89 and test-retest reliability (r) from 0.73 to 0.85, confirming its appropriateness for evaluating depressive symptoms in both clinical and non-clinical groups. In this sample, internal consistency was again solid (Cronbach’s α = 0.86), affirming its reliability for assessing depressive symptoms among college students. Procedure Participants were recruited through convenience sampling among full-time undergraduate female students at Hainan Medical University. Recruitment materials, such as study aims, eligibility criteria, and a link to the online survey, were shared via official university channels, like departmental WeChat groups, student union announcements and campus bulletin boards. A total of 354 eligible students consented to participate voluntarily, and there were no exclusions due to withdrawals during data collection. Before enrolment, participants viewed an electronic informed consent form on the survey platform that explained the study’s goal, examining links between anxiety, sleep, burnout, and depression, data confidentiality, and their right to withdraw at any time without penalty. They provided digital consent by checking a box and proceeding to the survey, with their agreement securely stored in the platform’s database [ 28 ]. Data was collected online through a professional questionnaire platform (Wenjuanxing), which ensured standardisation and accessibility on various devices like smartphones, tablets, or computers. The electronic survey was designed to lead participants through a predetermined sequence of measures: Demographic questionnaire (collecting gender, birthplace, major, monthly living expenses, and GPA); Self-Rating Anxiety Scale (SAS); Self-Rating Scale of Sleep (SRSS); College Student Learning Burnout Scale (Lian Rong’s version); Self-Rating Depression Scale (SDS). Participants completed the survey at their convenience, with the average completion time being 30–40 minutes. No personal identifiers were collected to maintain anonymity, and no monetary or material incentives were provided for participation. Statistical Analyses Data were analysed using IBM SPSS Statistics 26.0 and AMOS 24.0. First, preliminary descriptive statistics, including means, standard deviations, skewness, and kurtosis, were calculated for all variables, such as anxiety, sleep disorders, burnout, depression, and demographic covariates, to characterize their distribution patterns. Normality was assessed by checking if skewness fell within ± 2 and kurtosis within ± 3 for the suitability of subsequent parametric tests. The internal consistency of all scales was evaluated using Cronbach’s coefficients, with a threshold of ≥ 0.70 indicating acceptable reliability. Then, Confirmatory Factor Analysis (CFA) with maximum likelihood estimation was employed to test the factor structures of the scales. Model fit was assessed using indices such as χ²/df (with a threshold of 0.90), Root Mean Square Error of Approximation (RMSEA, < 0.08), and Standardized Root Mean Squared Residual (SRMR, 0.70) and Average Variance Extracted (AVE, > 0.50). Structural Equation Modelling (SEM) was used to compare direct and indirect effect models to explore relationships among variables. The direct effect model posited that demographic factors directly predict mental health variables and that anxiety, sleep disorders, and burnout directly predict depression. The indirect effect model assumed that demographic factors predict anxiety, which then influences sleep disorders and burnout, ultimately affecting depression. Model fit was assessed using the same CFA-related indices, and indirect effects were evaluated using bias-corrected bootstrap 95% confidence intervals. Finally, one-way ANOVA was applied to examine differences in mental health variables across demographic groups, birthplace, major, monthly living expenses, and GPA. When significant effects were detected (p < 0.05), Bonferroni post-hoc tests were conducted for pairwise comparisons. Results Sample characteristics and descriptive statistics The final sample consisted of 354 female undergraduate students from Hainan Medical University. Key demographic characteristics relevant to subsequent analyses are summarised in Table 1 , including distributions by birthplace, major, monthly living expenses, and GPA. Notably, rural students (49.72%) and students majoring in clinical medicine (38.98%) made up the largest subgroups, while monthly living expenses were most common in the ¥2,000–2,499 range (31.07%). These characteristics were included as covariates in structural models to control for potential confounding effects. Table 1 Sample characteristics by demographic groups Indices Criteria n Percentage Birth of Urban 178 50.28% Place Rural 176 49.72% Clinical Medicine 138 38.98% Preventive Medicine 58 16.38% Major Management 89 25.14% Inspection and Quarantine 59 16.67% Others 10 2.82% Less than 1,000 (excluding 1,000) 16 4.52% Monthly 1,000–1,499 85 24.01% Living 1,500-1,999 96 27.12% Expenses 2,000–2,499 110 31.07% 2,500-2,999 25 7.06% 3,000 and above 22 6.21% Less than 2 10 2.82% 2-2.49 29 8.19% GPAs 2.5–2.99 64 18.08% 3-3.49 130 36.72% 3.5 and above 121 34.18% Descriptive statistics for anxiety, sleep disorders, burnout, and depression (including subscales) are shown in Table 2 . Overall, the mean scores for all variables are within the subclinical range, with the greatest variability seen in the "emotional exhaustion" subscale of burnout (SD = 11.48) and the least in "medication status" for sleep disorders (SD = 0.57). Skewness and kurtosis values indicate mild departures from normality for some subscales, like the somatisation dimension of anxiety: skewness = 1.56, but these are considered acceptable for parametric analyses. Table 2 Descriptive statistics for all variables Dimensions Indices Min Max Mean SD Skewness Kurtosis Anxiety Somatization dimension 8.000 32.000 11.791 3.800 1.556 4.286 Sense of anxiety dimension 4.000 16.000 6.647 2.378 0.896 0.854 Insomnia Sleep instability 1.000 5.000 1.825 0.966 1.079 0.520 Early awakening 1.000 5.000 1.881 1.047 1.133 0.656 Nightmares and night terrors 1.000 5.000 1.610 0.868 1.628 2.714 Medication status 1.000 5.000 1.189 0.569 3.427 12.638 Mood after insomnia 1.000 5.000 3.263 1.343 -0.037 -1.072 Depression Somatic symptom dimension 5.000 20.000 8.364 2.443 0.848 1.628 Dysthymia 3.000 12.000 4.475 1.386 1.147 2.309 Burnout Low mood 30.000 103.000 53.215 11.479 0.637 0.792 Inappropriate behaviour 15.000 51.000 26.186 6.683 0.739 0.303 Low sense of achievement 13.000 49.000 28.952 6.229 -0.078 -0.179 Reliability and confirmatory factor analysis Internal consistency for all scales was adequate (Table 3 ). Cronbach’s α coefficients ranged from 0.72 (inappropriate behaviour subscale of burnout) to 0.86 (depression total scale), surpassing the 0.70 threshold for acceptable reliability. Confirmatory factor analysis (CFA) confirmed the factor structure of each measure (Fig. 1 ). For anxiety, the 2-factor model (somatisation, sense of anxiety) showed good fit (χ²/df = 2.31, CFI = 0.94, RMSEA = 0.06, SRMR = 0.05), with factor loadings from 0.79 to 0.94. The 5-factor model for sleep disorders (sleep instability, early awakening, nightmares, medication status, mood after insomnia) had acceptable fit (χ²/df = 2.89, CFI = 0.92, RMSEA = 0.07), although “medication status” loaded weakly (λ = 0.27). Burnout’s 3-factor model (emotional exhaustion, inappropriate behaviour, low sense of achievement) fit well (χ²/df = 2.15, CFI = 0.95, RMSEA = 0.05), with factor loadings ≥ 0.76. For depression, the 2-factor model (somatic symptoms, dysthymia) indicated adequate fit (χ²/df = 2.56, CFI = 0.93, RMSEA = 0.06). Table 3 Reliability coefficients (Cronbach’s α) for total scales and subscales Dimensions Indices Estimate AVE CR Insomnia Sleep instability 0.695 0.332 0.697 Early awakening 0.703 Nightmares and night terrors 0.597 Medication status 0.502 Mood after insomnia 0.273 Anxiety Somatization dimension 0.787 0.689 0.815 Sense of anxiety dimension 0.871 Burnout Low mood 0.972 0.748 0.898 Inappropriate behavior 0.851 Low sense of achievement 0.759 Depression Somatic symptom dimension 0.776 0.570 0.726 Dysthymia 0.733 Structural Equation Modelling results The indirect effect model (Fig. 2 ) showed a better fit than the direct effect model (Table 4), consistent with SEM best practices for testing mediational pathways [ 16 ]. Key paths included: Sleep disorders positively predicted anxiety (Estimate = 4.498, p < 0.001) and burnout (Estimate = 20.184, p < 0.001). Anxiety significantly predicted depression (Estimate = 0.464, p < 0.001), aligning with prior evidence that anxiety mediates sleep-depression links [ 4 ] Sleep disorders exerted a significant indirect effect on depression via anxiety (Estimate = 0.641, 95% CI = [0.382, 1.103], p = 0.001), but not through burnout (p = 0.149)—a finding contrasting with occupational studies [ 13 ] yet consistent with academic contexts where burnout effects may be buffered [ 9 ]. Differential tests across demographic groups ANOVA results showed that urban students scored higher on the somatic symptom dimension of depression than rural students (M = 8.65 vs. 8.07, F = 2.238, p = 0.026). Students with GPAs less than 2.0 reported more “inappropriate behaviour” (burnout) than those with GPAs between 3.0 and 3.49 (M = 30.50 vs. 25.05, F = 4.159, p = 0.001). Monthly living expenses under ¥1,000 were linked to greater sleep instability (F = 3.146, p = 0.015) and medication use (F = 4.323, p = 0.001). No significant differences were observed across majors. Discussion The current study examined the relationships among anxiety, sleep disorders, burnout, and depression in female college students, as well as the impact of demographic factors, using structural equation modelling. The main findings, placed within the context of existing research, provide insights into the mental health dynamics of this group, with implications for theory, practice, and future studies. Key Findings and Alignment with Existing Literature The factor structures of all measures—anxiety (2-factor), sleep disorders (5-factor), burnout (3-factor), and depression (2-factor)—demonstrated acceptable to good fit (χ²/df < 3, RMSEA < 0.08, SRMR < 0.08), confirming their validity in this sample. This aligns with psychometric research on college student mental health scales, emphasising that robust factor structures are essential for ensuring measures accurately capture intended constructs [ 29 ]. Regarding relational pathways, the indirect effect model showed that sleep disorders significantly predicted anxiety and burnout, with anxiety serving as a primary mediator between sleep and depression. This aligns with previous longitudinal studies by Baglioni [ 10 ], who found that sleep issues tend to come before and worsen anxiety, which then elevates depressive symptoms—especially in young adults facing academic stress. Interestingly, burnout did not predict depression, differing from findings in professional settings [ 13 ], but aligning with research on college students, where social support or academic engagement may buffer burnout effects [ 9 , 30 ]. Demographic patterns added further context to these findings. Urban students exhibited more somatic depressive symptoms than rural peers (M = 8.65 vs. 8.07), reflecting heightened urban stressors like academic competition [ 20 ]. Students with monthly expenditure <¥1,000 reported greater sleep instability (F = 3.146, p = 0.015), corroborating socioeconomic stress as a sleep disruptor [ 21 ]. Lower GPA predicted burnout-related behaviours (F = 4.159, p = 0.001), reinforcing bidirectional academic-mental health cycles [ 9 ]. Implications for Practice These findings offer actionable insights for developing targeted mental health interventions in university settings. Critically, the identified mediation pathway—where sleep disturbances fuel anxiety, which in turn elevates depression risk—highlights the paramount importance of early identification and intervention for insomnia and anxiety symptoms to disrupt this progression toward clinical depression. Building on this, effective evidence-based strategies include reducing late-night screen time and promoting regular routines to decrease emotional distress, especially in students experiencing financial hardships. Anxiety treatments like cognitive-behavioural therapy (CBT), focusing on somatic symptoms, may help prevent depression, which is especially beneficial for urban students. For students with lower GPAs, academic support such as time management workshops can help decrease burnout behaviours, breaking the cycle of poor performance and emotional exhaustion. Future Directions Longitudinal studies are needed to clarify causality in the sleep-anxiety-depression cycle. Expanding samples to include multiple institutions and male students will improve generalizability. Exploring why burnout did not directly predict depression—potentially due to gendered coping strategies [ 31 ] could clarify context-specific protective factors. Qualitative methods should examine lived experiences underlying demographic differences [ 20 ]. Additionally, differences [ 20 ]. Additionally, predictive modelling studies show similar trends [ 32 ], integrating qualitative methods like interviews can examine lived experiences, driving demographic differences, such as rural versus urban stressors. Limitations Several limitations should be acknowledged. First, the cross-sectional design prevents conclusions about causal relationships between sleep, anxiety, burnout, and depression. Longitudinal research is needed to clarify temporal pathways. Second, all measures were self-reported, which may be subject to recall bias and social desirability effects. Third, the sample was drawn from a single medical university in Hainan Province and included only female students, limiting generalisability to other regions, institutions, and male students. Finally, potential confounders such as family background, social support, or personality traits were not assessed and may have influenced the observed associations. These limitations should be considered when interpreting the findings, and future research should address them. Conclusions This study suggests a complex interplay between sleep disturbances, anxiety, burnout, and depression among female college students, with anxiety mediating the impact of poor sleep on depressive symptoms. From a public health perspective, these interconnected challenges represent an essential concern with implications for equity in higher education and long-term wellbeing. Socioeconomic disadvantage and academic pressures may further amplify vulnerability, underscoring the need for interventions that address individual symptoms and broader structural determinants. Universities and policymakers could consider gender-sensitive, population-level approaches that combine sleep health promotion, financial support mechanisms, and accessible campus-based mental health services. Such strategies may help reduce the risk of depression and burnout, foster healthier learning environments and support the wellbeing of young women in higher education. Abbreviations SAS Self-Rating Anxiety Scale SRSS Self-Rating Scale of Sleep SDS Self-Rating Depression Scale SEM Structural Equation Modelling CFA Confirmatory Factor Analysis CFI Comparative Fit Index RMSEA Root Mean Square Error of Approximation SRMR Standardised Root Mean Squared Residual CR Composite Reliability AVE Average Variance Extracted GPA Grade Point Average Declarations Author contributions Z.Z. performed the statistical analyses, interpreted the findings, and drafted the initial manuscript. L.S., HP.H., QY.W., LQ.F., and SF.H. were responsible for participant recruitment and data collection. L.Y., MR.Y., and DE.C. conducted data validation and ensured the accuracy of statistical outputs. ZX.W. and HQ.S. conceived and designed the study framework, and critically reviewed and revised the manuscript for intellectual content. All authors read and approved the final version of the manuscript. Funding This work was supported by the Hainan Provincial Natural Science Foundation of China (825QN324); the Education Department of Hainan Province (Hnky2025ZD-6); and the Student Innovation and Entrepreneurship Training Program at Hainan Medical University (X202411810043). Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was reviewed and approved by the Ethics Committee of Hainan Medical University (Approval No. HYLL-2024-587). The project, titled Construction and Empirical Research on the Model of Integrated Medical and Preventive Intervention for Mental Health of Medical Students in Hainan Based on the IMB Model, was granted approval through a fast review process on 3 June 2024, with validity until 31 December 2027. All participants were informed of the study aims, procedures, potential risks, and their rights, and provided written informed consent prior to participation, in accordance with the Declaration of Helsinki. Consent for publication Not applicable (No individual personal data presented). Competing interests The authors declare that they have no competing interests. Acknowledgement The authors thank all participants and staff at Hainan Medical University who supported this study. References Eisenberg D, Gollust SE, Golberstein E, Hefner JL. Prevalence and correlates of depression, anxiety, and suicidality among university students. Am J Orthopsychiatry. 2007;77(4):534–42. Han SS, Zhang YS, Zhu W. 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Gao R, Wang H, Liu S, Wang X, Song S, Wang Y. Study on anxiety, depression, and sleep conditions and their interrelations among vocational college students during the COVID-19 pandemic management normalization. Front Public Health. 2024;12:1385639. Salmela-Aro K, Upadyaya K. Trajectories of school burnout during upper secondary education and their antecedents and consequences. J Educ Psychol. 2014;106(3):695–710. Baglioni C, Battagliese G, Feige B, Riemann D. Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011;135(1–3):10–9. Goldstone A, Reynolds CF. Sleep and depression. Sleep Med Rev. 2008;12(1):19–29. Dyrbye LN, Thomas MR, Shanafelt TD. Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Acad Med. 2006;81(4):354–73. Schaufeli WB, Bakker AB. Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. J Organizational Behav. 2004;25(3):293–315. Zhu X, Haegele JA, Liu H, Yu F. Academic stress, physical activity, sleep, and mental health among Chinese adolescents. Int J Environ Res Public Health. 2021;18(14):7257. Gómez–Urquiza JL, VSoriano A, Membrive–Jiménez MJ, Ramírez–Baena L, Aguayo–Estremera R, Ortega–Campos E, et al. Prevalence and levels of burnout in nursing students: a systematic review with meta–analysis. Nurse Educ Pract. 2023;72:103753. Szwamel K, Kowalska W, Mazur E, Janus A, Bonikowska I, Jasik–Pyzdrowska J. Determinants of burnout syndrome among undergraduate nursing students in Poland: a cross–sectional study. BMC Med Educ. 2025;25:178. Pokhrel NB, Khadayat R, Tulachan P. Depression, anxiety, and burnout among medical students and residents of a medical school in Nepal: a cross–sectional study. BMC Psychiatry. 2020;20:298. Arbabisarjou A, Seyed Mehdi H, Sharif MR, Haji Alizadeh K, Yarmohammadzadeh P, Feyzollahi Z. The relationship between sleep quality and social intimacy and academic burn–out in students of medical sciences. Global J Health Sci. 2016;8(5):231–40. Pagnin D, de Queiroz V, Carvalho YT, Dutra ASS, Amaral MB, Queiroz TT. The relation between burnout and sleep disorders in medical students. Acad Psychiatry. 2014;38:438–44. Pachankis JE, Hatzenbuehler ML, McLaughlin KA. Rural residence, psychiatric disorders, and access to mental health treatment in the United States. J Consult Clin Psychol. 2015;83(1):151–63. Leach LS, Patton GC. Socioeconomic status and adolescent mental health: A systematic review. Soc Psychiatry Psychiatr Epidemiol. 2013;48(11):1717–30. Twenge JM, Nolen-Hoeksema S. Age, gender, race, socioeconomic status, and birth cohort differences on the Children’s Depression Inventory: A meta-analysis. J Abnorm Psychol. 2002;111(3):578–88. Kline RB. Principles and practice of structural equation modeling. Guilford Press; 2015. Zung WW. A rating instrument for anxiety disorders. Psychosomatics: J Consultation Liaison Psychiatry. 1971;12(6):371–9. J. L: Self-Rating Scale of Sleep (SRSS). Chin J Health Psychol 2012, 20(2):193–5. Chinese. Lian RYLWL. Relationship between professional commitment and learning burnout of undergraduates and scales developing. Acta Physiol Sinica. 2005;37(5):632–6. Chinese. Zung WW. A self-rating depression scale. Arch Gen Psychiatry. 1965;12(1):63–70. Association AP. Guidelines for psychological practice with girls and women. In.; 2019. Garnefski N, Kraaij V, Spinhoven P. Cognitive emotion regulation strategies and depressive symptoms: A comparative study of five age groups. Pers Indiv Differ. 2001;30(8):1311–27. Gomes AR, Faria S, Gonçalves AM. Burnout in students: A systematic review (2019–2024). Behav Sci. 2024;14(2):170. Hyde JS, Mezulis AH, Abramson LY. The ABCs of depression: Integrating affective, biological, and cognitive models to explain the emergence of the gender difference in depression. Psychol Rev. 2008;115(2):291–313. Wang Y, Li X, Zhang L, Zhou Q. Predicting depression among Chinese female college students: A machine learning approach. BMC Public Health. 2025;25:21632. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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1","display":"","copyAsset":false,"role":"figure","size":133556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eillustrates the factor structure of latent variables (Sleep, Anxiety, Burnout, Depression), with standardized factor loadings for observed variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7494165/v1/1e34ef5e96cb93e1412dd8c8.png"},{"id":93222718,"identity":"8b2b2391-b8ae-4270-8995-d12bb3a5c53c","added_by":"auto","created_at":"2025-10-10 11:21:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140487,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003epresents the full structural model, including demographic covariates (Birthplace, Major, Monthly living expenses, GPAs) as exogenous predictors of latent mental health variables.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7494165/v1/c8f8900301bed35943953233.png"},{"id":103994996,"identity":"b869aa83-c16e-4531-a928-121bd84dd494","added_by":"auto","created_at":"2026-03-05 12:26:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7494165/v1/f488e3bc-d30e-41d5-92ff-c43d28502ea4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From adversity to depression: a structural equation modelling study of the public health burden and mediating pathways among female college students in China","fulltext":[{"header":"Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eThe Prevalence and Interplay of Anxiety, Sleep Disorders, Burnout, and Depression in Female College Students\u003c/h2\u003e\u003cp\u003eMental health issues among female college students represent a significant global concern and a critical public health priority, with implications for equity in education and long-term wellbeing. Anxiety, sleep disturbances, burnout, and depression form interconnected crises shaped by gender-specific experiences, acting as core indicators of psychological distress. Epidemiological data reveal striking gender disparities: global studies report severe anxiety symptoms in over 35% of female students and depression in 25\u0026ndash;30%, rates approximately 1.5\u0026ndash;2 times higher than those observed in male peers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent large-scale Chinese surveys and meta-analyses confirm similarly high or higher prevalence rates of anxiety and depression in female undergraduates [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sleep disturbances are also common, affecting 25\u0026ndash;40% of female students in international samples [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], with recent Chinese studies documenting high co-occurrence of poor sleep quality and psychological distress in this group[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Burnout\u0026mdash;characterised by emotional exhaustion and academic cynicism\u0026mdash;impacts up to 45% of female undergraduates, particularly in high-pressure fields such as STEM and medicine [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Critically, these conditions rarely occur in isolation; they interact through complex, gendered pathways. As evidenced in our findings, sleep disturbances significantly predict both anxiety and burnout, with anxiety subsequently mediating depression risk.\u003c/p\u003e\u003cp\u003eAnxiety, often intensified by gendered social expectations, is associated with hypothalamic-pituitary-adrenal (HPA) axis hyperactivity. This neurobiological response disrupts circadian rhythms, elevating vulnerability to sleep disturbances [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Female students with elevated anxiety exhibit a 2\u0026ndash;3 times greater risk of developing significant sleep problems. Conversely, impaired sleep hinders emotional regulation capacity, thereby amplifying susceptibility to depressive symptoms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u0026mdash;a pathway empirically supported by our model showing sleep disturbances exert indirect effects on depression through anxiety.\u003c/p\u003e\u003cp\u003eBurnout functions bidirectionally within this nexus. Gendered academic pressures, such as stereotype threat in male-dominated fields and disproportionate workload burdens, are well-documented contributors to burnout in students [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent evidence from Chinese adolescent populations further shows that academic stress can impair sleep quality, with anxiety and school burnout acting as sequential mediators [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Across multiple studies, approximately 41% of students report high levels of emotional exhaustion, the core component of burnout [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cross‑sectional surveys of medical and nursing students in Asia and Europe show similar patterns; high emotional exhaustion, depersonalisation and low personal accomplishment are prevalent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Burnout is closely linked to sleep problems: Iranian nursing students with greater sleep disturbances reported higher academic burnout scores, whereas greater social intimacy appeared protective [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Longitudinal studies further suggest that emotional exhaustion and excessive daytime sleepiness reinforce each other, creating a vicious cycle that diminishes academic efficacy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe Role of Demographic and Academic Factors in Shaping Female Students\u0026rsquo; Mental Health\u003c/h2\u003e\u003cp\u003eDemographic characteristics and academic experiences act as critical contextual amplifiers within the anxiety-sleep-burnout-depression cycle. These factors intersect with gender-specific stressors, potentially heightening vulnerability to psychological distress among female undergraduates.\u003c/p\u003e\u003cp\u003eBirthplace introduces distinct challenges shaped by pre-college environments and resources. Female students from rural backgrounds often enter university with limited prior access to mental health services and may face greater cultural stigma regarding help-seeking. This resource gap can amplify burnout risk, with studies suggesting rural female students experience rates 30\u0026ndash;40% higher than their urban counterparts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our findings further reveal a nuanced pattern: urban students exhibited more somatic depressive symptoms, potentially reflecting heightened exposure to urban stressors such as intense academic competition, cost-of-living pressures, or social comparison, which may manifest physically.\u003c/p\u003e\u003cp\u003eAcademic majors, especially in high-stress fields, significantly interact with gender to affect risk. For example, majors like clinical medicine\u0026mdash;known for intense schedules, high-stakes assessments, and documented gender disparities in workload and support- are linked to burnout rates that are 2\u0026ndash;3 times higher among female students compared to students in less demanding disciplines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Supporting this, our findings show that majoring in high-pressure areas such as clinical medicine, common in our sample, is a key predictor of sleep disturbances.\u003c/p\u003e\u003cp\u003eSocioeconomic status, operationalised here as monthly living expenses, generates unique financial stressors that often carry gendered dimensions. Female students reporting lower living expenses (\u0026lt;\u0026yen;1,000) may experience heightened anxiety about basic needs, part-time work demands, or perceived social disadvantage, contributing to poorer mental health. Research indicates these students report approximately 40% higher anxiety levels and 30% more sleep problems compared to peers with more adequate financial resources [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This aligns with our discovery that lower living expenses correlate with increased sleep instability and greater reliance on sleep medication, suggesting financial precarity directly impacts sleep physiology and coping mechanisms.\u003c/p\u003e\u003cp\u003eAcademic performance (GPA) exhibits a complex, bidirectional relationship with mental health, moderated by gendered experiences of achievement pressure. Lower GPA can fuel negative self-perceptions, self-doubt, and fear of failure\u0026mdash;concerns often intensified by societal expectations of female competence and perfectionism. This, in turn, increases vulnerability to anxiety and burnout [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our data support this link, revealing that students with GPAs\u0026thinsp;\u0026lt;\u0026thinsp;2.0 exhibit significantly more burnout-related inappropriate academic behaviours compared to higher-performing peers.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUnresolved Questions and the Need for the Current Study\u003c/h3\u003e\n\u003cp\u003eCritical questions remain about how demographic and academic factors influence the relationships among the four outcome indicators. For example, does rural birthplace directly cause depression, or does it influence it through limited mental health resources before college, leading to increased anxiety, which then disrupts sleep and worsens burnout? How does financial stress affect anxiety\u0026mdash;through sleep problems, burnout, or both? These gaps highlight a broader issue: research often combines genders, ignoring how female-specific experiences shape the dynamics of these outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study addresses these gaps by focusing on female undergraduates (n\u0026thinsp;=\u0026thinsp;354) from Hainan Medical University, a sample with diverse backgrounds 49.72% rural, 38.98% in clinical medicine that allows analysis of demographic influences. Using structural equation modelling (SEM) - ideal for testing direct and indirect effects [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] - we examine whether factors like birthplace and living expenses operate through direct pathways or via intermediaries like anxiety, as suggested by our preliminary finding that the indirect effect model fits better than the direct effect model.\u003c/p\u003e\n\u003ch3\u003eObjectives and Hypotheses of the Current Study\u003c/h3\u003e\n\u003cp\u003eThis study examines the complex relationships among anxiety, sleep disturbances, burnout, and depression in female college students, with emphasis on how birthplace, major, living expenses, and GPA influence these pathways. Using structural equation modelling (SEM), two models are tested: the direct effect model, where demographic/academic factors directly predict anxiety, sleep disturbances, burnout, and depression; and the indirect effect model, demographic/academic factors predict sleep disturbances, which then predict anxiety and burnout; anxiety subsequently predicts depression, while burnout\u0026rsquo;s role in depression is explored. The hypotheses are: (1) female students from rural areas, enrolled in high-stress majors like clinical medicine, with lower living expenses and lower GPAs, will show higher levels of anxiety, sleep problems, burnout, and depression. (2) Sleep disturbances will positively predict symptoms of anxiety and burnout in female college students. (3) Anxiety, sleep disturbances, and burnout will each positively predict depressive symptoms. (4) The indirect effect model will demonstrate a significantly better fit than the direct effect model for female college students.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe sample comprised female students from Hainan Medical University in Hainan Province, southern China. Recruitment took place on campus at Hainan Medical University starting July 6th, 2024. To qualify, participants had to be enrolled in a full-time undergraduate program and give informed consent. Those with a self-reported history of severe mental illnesses, such as schizophrenia or neurological disorders, were excluded to focus on subclinical symptoms.\u003c/p\u003e\u003cp\u003eA total of 354 female students participated in the study\u0026mdash;recruitment involved convenience sampling, with invitations posted on university bulletin boards and online student groups. Before data collection, ethical approval was obtained from the Hainan Medical University Institutional Review Board, and all participants gave written informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eAnxiety\u003c/h2\u003e\u003cp\u003eAnxiety symptoms were evaluated using the Self-Rating Anxiety Scale (SAS)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], a widely adopted 20-item self-report tool designed to assess the severity of anxiety in adults. The scale covers both psychological symptoms (e.g., \"I feel nervous and anxious\") and physical symptoms (e.g., \"I have a sense of tightness in my chest\"), with 15 items positively worded (indicating the presence of anxiety) and five items reverse-coded (indicating absence of anxiety, e.g., \"I feel calm and can sit still easily\"). Participants indicated how often they experienced each symptom over the past week on a 4-point Likert scale: 1 = \"none or rarely,\" 2 = \"sometimes,\" 3 = \"often,\" and 4 = \"almost always.\" Raw scores are then converted to standard scores via a conversion table, with scores of 50 or above signifying the presence of anxiety: 50\u0026ndash;59 suggests mild anxiety, 60\u0026ndash;69 moderate, and 70 or more severe.\u003c/p\u003e\u003cp\u003eThe SAS has shown reliable psychometric properties in Chinese populations, with previous studies reporting internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.83\u0026ndash;0.88 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]) and test-retest reliability (r\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.85). In this sample, internal consistency was also satisfactory (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.85), indicating it is appropriate for assessing anxiety symptoms among college students.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eSleep Disorders\u003c/h3\u003e\n\u003cp\u003eSleep disorders were measured using the Self-Rating Scale of Sleep (SRSS, Chinese-standardised version)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], a 10-item self-report tool created to assess sleep quality and disturbances in Chinese populations. The scale addresses main aspects of sleep health, such as trouble falling asleep, sleep duration, sleep consistency, early awakening, nightmare frequency, sleep quality, daytime impairment from poor sleep, use of hypnotic medications, and overall sleep satisfaction.\u003c/p\u003e\u003cp\u003eEach item is rated on a 5-point Likert scale (1 = \"never,\" 2 = \"rarely,\" 3 = \"sometimes,\" 4 = \"often,\" 5 = \"always\") based on experiences over the past week. Total scores range from 10 to 50, with higher scores indicating more severe sleep disturbances: 10\u0026ndash;20 points\u0026thinsp;=\u0026thinsp;normal sleep, 21\u0026ndash;30 points\u0026thinsp;=\u0026thinsp;mild sleep disorders, 31\u0026ndash;40 points\u0026thinsp;=\u0026thinsp;moderate sleep disorders, and 41\u0026ndash;50 points\u0026thinsp;=\u0026thinsp;severe sleep disorders.\u003c/p\u003e\u003cp\u003eThe SRSS has been extensively validated among Chinese samples, demonstrating strong internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.78\u0026ndash;0.85) and test-retest reliability (r\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.82) in prior research. This supports its appropriateness for evaluating sleep issues in non-clinical groups. In the present sample, internal consistency was also acceptable (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.81), confirming its reliability for assessing sleep problems in college students.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBurnout\u003c/h2\u003e\u003cp\u003eBurnout was measured using the College Student Learning Burnout Scale, revised by Chinese scholar Lian Rong[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This popular tool for assessing academic burnout among Chinese college students includes 20 items divided into three factors aligned with the focus of the current study.\u003c/p\u003e\u003cp\u003eEmotional exhaustion (low mood): 8 items measure feelings of fatigue and indifference towards studying, such as \u0026ldquo;Early in the morning, thinking about facing a day of study makes me feel tired\u0026ldquo; and \"After a full day of studying, I feel exhausted.\u0026ldquo; Inappropriate behaviour: 6 items identify maladaptive academic behaviours like \"I rarely study after class\u0026rdquo; and \"I only study when there are exams.\" Low sense of achievement: 6 items assess feelings of incompetence in academic performance, for example, \u0026ldquo;Mastering professional knowledge is difficult for me\" and \"College studies have not fully demonstrated my abilities.\"\u003c/p\u003e\u003cp\u003eParticipants rated each item based on their experiences over the past month using a 5-point Likert scale: 1 = \"completely inconsistent,\" 2 = \"relatively inconsistent,\" 3 = \"uncertain,\" 4 = \"relatively consistent,\" 5 = \"completely consistent.\" Note that 8 items (1, 3, 6, 8, 11, 13, 15, 18) are reverse-scored to minimise response bias. Higher total scores indicate more severe burnout, while subscale scores specify the intensity of each component.\u003c/p\u003e\u003cp\u003eThe scale has shown robust psychometric properties in earlier research: the overall Cronbach\u0026rsquo;s α is 0.865, with subscale α coefficients of 0.812 for emotional exhaustion, 0.704 for inappropriate behaviour, and 0.731 for low sense of achievement. The correlations between each subscale and the total scale range from 0.704 to 0.914 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating good structural validity. In this sample, confirmatory factor analysis further supported the three-factor model, with high composite reliability (CR\u0026thinsp;=\u0026thinsp;0.898) and average variance extracted (AVE\u0026thinsp;=\u0026thinsp;0.748), reflecting strong convergent validity. Internal consistency was also satisfactory for each subscale: emotional exhaustion (α\u0026thinsp;=\u0026thinsp;0.83), inappropriate behaviour (α\u0026thinsp;=\u0026thinsp;0.72), and low sense of achievement (α\u0026thinsp;=\u0026thinsp;0.75), confirming the scale\u0026rsquo;s appropriateness for assessing burnout in this group of female college students.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDepressive Symptoms\u003c/h2\u003e\u003cp\u003eDepressive symptoms were assessed using the Self-Rating Depression Scale (SDS)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], a 20-item self-report instrument designed to measure the presence and severity of depression in adults. The scale encompasses various symptom domains, including emotional (e.g., \"I feel downhearted and blue\"), cognitive (e.g., \u0026ldquo;I find it hard to make decisions\"), somatic (e.g., \u0026ldquo;I have trouble falling asleep\"), and motivational (e.g., \"I have lost interest in things I used to enjoy\") aspects. Of the 20 items, 10 are positively worded (indicating depressive symptoms), and 10 are reverse-scored (indicating the absence of symptoms, e.g., \"I feel hopeful about the future\").\u003c/p\u003e\u003cp\u003eParticipants assess each item based on how often they experienced the symptom over the past week, using a 4-point Likert scale: 1 = \"none or rarely,\" 2 = \"sometimes,\" 3 = \"often,\" 4 = \"almost always.\" Raw scores are transformed into standard scores by multiplying the raw total by 1.25. Standard scores of 53 or higher indicate the presence of depressive symptoms, with 53\u0026ndash;62 reflecting mild depression, 63\u0026ndash;72 indicating moderate depression, and scores of 73 or above signifying severe depression.\u003c/p\u003e\u003cp\u003eThe SDS has shown excellent psychometric qualities in Chinese populations. Past research indicated internal consistency values (Cronbach\u0026rsquo;s α) ranging from 0.82 to 0.89 and test-retest reliability (r) from 0.73 to 0.85, confirming its appropriateness for evaluating depressive symptoms in both clinical and non-clinical groups. In this sample, internal consistency was again solid (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.86), affirming its reliability for assessing depressive symptoms among college students.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003e Participants were recruited through convenience sampling among full-time undergraduate female students at Hainan Medical University. Recruitment materials, such as study aims, eligibility criteria, and a link to the online survey, were shared via official university channels, like departmental WeChat groups, student union announcements and campus bulletin boards. A total of 354 eligible students consented to participate voluntarily, and there were no exclusions due to withdrawals during data collection.\u003c/p\u003e\u003cp\u003eBefore enrolment, participants viewed an electronic informed consent form on the survey platform that explained the study\u0026rsquo;s goal, examining links between anxiety, sleep, burnout, and depression, data confidentiality, and their right to withdraw at any time without penalty. They provided digital consent by checking a box and proceeding to the survey, with their agreement securely stored in the platform\u0026rsquo;s database [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData was collected online through a professional questionnaire platform (Wenjuanxing), which ensured standardisation and accessibility on various devices like smartphones, tablets, or computers. The electronic survey was designed to lead participants through a predetermined sequence of measures:\u003c/p\u003e\u003cp\u003eDemographic questionnaire (collecting gender, birthplace, major, monthly living expenses, and GPA);\u003c/p\u003e\u003cp\u003eSelf-Rating Anxiety Scale (SAS);\u003c/p\u003e\u003cp\u003eSelf-Rating Scale of Sleep (SRSS);\u003c/p\u003e\u003cp\u003eCollege Student Learning Burnout Scale (Lian Rong\u0026rsquo;s version);\u003c/p\u003e\u003cp\u003eSelf-Rating Depression Scale (SDS).\u003c/p\u003e\u003cp\u003eParticipants completed the survey at their convenience, with the average completion time being 30\u0026ndash;40 minutes. No personal identifiers were collected to maintain anonymity, and no monetary or material incentives were provided for participation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\u003cp\u003eData were analysed using IBM SPSS Statistics 26.0 and AMOS 24.0. First, preliminary descriptive statistics, including means, standard deviations, skewness, and kurtosis, were calculated for all variables, such as anxiety, sleep disorders, burnout, depression, and demographic covariates, to characterize their distribution patterns. Normality was assessed by checking if skewness fell within \u0026plusmn;\u0026thinsp;2 and kurtosis within \u0026plusmn;\u0026thinsp;3 for the suitability of subsequent parametric tests.\u003c/p\u003e\u003cp\u003eThe internal consistency of all scales was evaluated using Cronbach\u0026rsquo;s coefficients, with a threshold of \u0026ge;\u0026thinsp;0.70 indicating acceptable reliability. Then, Confirmatory Factor Analysis (CFA) with maximum likelihood estimation was employed to test the factor structures of the scales. Model fit was assessed using indices such as χ\u0026sup2;/df (with a threshold of \u0026lt;\u0026thinsp;3), Comparative Fit Index (CFI, \u0026gt;\u0026thinsp;0.90), Root Mean Square Error of Approximation (RMSEA, \u0026lt;\u0026thinsp;0.08), and Standardized Root Mean Squared Residual (SRMR, \u0026lt;\u0026thinsp;0.08). Convergent validity was further examined through Composite Reliability (CR, \u0026gt;\u0026thinsp;0.70) and Average Variance Extracted (AVE, \u0026gt;\u0026thinsp;0.50).\u003c/p\u003e\u003cp\u003eStructural Equation Modelling (SEM) was used to compare direct and indirect effect models to explore relationships among variables. The direct effect model posited that demographic factors directly predict mental health variables and that anxiety, sleep disorders, and burnout directly predict depression. The indirect effect model assumed that demographic factors predict anxiety, which then influences sleep disorders and burnout, ultimately affecting depression. Model fit was assessed using the same CFA-related indices, and indirect effects were evaluated using bias-corrected bootstrap 95% confidence intervals.\u003c/p\u003e\u003cp\u003eFinally, one-way ANOVA was applied to examine differences in mental health variables across demographic groups, birthplace, major, monthly living expenses, and GPA. When significant effects were detected (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Bonferroni post-hoc tests were conducted for pairwise comparisons.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSample characteristics and descriptive statistics\u003c/h2\u003e\u003cp\u003eThe final sample consisted of 354 female undergraduate students from Hainan Medical University. Key demographic characteristics relevant to subsequent analyses are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including distributions by birthplace, major, monthly living expenses, and GPA. Notably, rural students (49.72%) and students majoring in clinical medicine (38.98%) made up the largest subgroups, while monthly living expenses were most common in the \u0026yen;2,000\u0026ndash;2,499 range (31.07%). These characteristics were included as covariates in structural models to control for potential confounding effects.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample characteristics by demographic groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth of\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.28%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinical Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.98%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreventive Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.38%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.14%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInspection and Quarantine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.67%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.82%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 1,000 (excluding 1,000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.52%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,000\u0026ndash;1,499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.01%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,500-1,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.12%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpenses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,000\u0026ndash;2,499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.07%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,500-2,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.06%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,000 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.21%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.82%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2-2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.19%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPAs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5\u0026ndash;2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3-3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDescriptive statistics for anxiety, sleep disorders, burnout, and depression (including subscales) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, the mean scores for all variables are within the subclinical range, with the greatest variability seen in the \"emotional exhaustion\" subscale of burnout (SD\u0026thinsp;=\u0026thinsp;11.48) and the least in \"medication status\" for sleep disorders (SD\u0026thinsp;=\u0026thinsp;0.57). Skewness and kurtosis values indicate mild departures from normality for some subscales, like the somatisation dimension of anxiety: skewness\u0026thinsp;=\u0026thinsp;1.56, but these are considered acceptable for parametric analyses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics for all variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDimensions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomatization dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSense of anxiety dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsomnia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep instability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarly awakening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNightmares and night terrors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedication status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMood after insomnia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-1.072\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=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomatic symptom dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDysthymia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurnout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow mood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInappropriate behaviour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow sense of achievement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eReliability and confirmatory factor analysis\u003c/h2\u003e\u003cp\u003eInternal consistency for all scales was adequate (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cronbach\u0026rsquo;s α coefficients ranged from 0.72 (inappropriate behaviour subscale of burnout) to 0.86 (depression total scale), surpassing the 0.70 threshold for acceptable reliability. Confirmatory factor analysis (CFA) confirmed the factor structure of each measure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For anxiety, the 2-factor model (somatisation, sense of anxiety) showed good fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.31, CFI\u0026thinsp;=\u0026thinsp;0.94, RMSEA\u0026thinsp;=\u0026thinsp;0.06, SRMR\u0026thinsp;=\u0026thinsp;0.05), with factor loadings from 0.79 to 0.94. The 5-factor model for sleep disorders (sleep instability, early awakening, nightmares, medication status, mood after insomnia) had acceptable fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.89, CFI\u0026thinsp;=\u0026thinsp;0.92, RMSEA\u0026thinsp;=\u0026thinsp;0.07), although \u0026ldquo;medication status\u0026rdquo; loaded weakly (λ\u0026thinsp;=\u0026thinsp;0.27). Burnout\u0026rsquo;s 3-factor model (emotional exhaustion, inappropriate behaviour, low sense of achievement) fit well (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.15, CFI\u0026thinsp;=\u0026thinsp;0.95, RMSEA\u0026thinsp;=\u0026thinsp;0.05), with factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.76. For depression, the 2-factor model (somatic symptoms, dysthymia) indicated adequate fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.56, CFI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.06).\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\u003eReliability coefficients (Cronbach\u0026rsquo;s α) for total scales and subscales\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\"\u003e\u003cp\u003eDimensions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsomnia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep instability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarly awakening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.703\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNightmares and night terrors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.597\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedication status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.502\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMood after insomnia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.273\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomatization dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSense of anxiety dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.871\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\u003eBurnout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow mood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInappropriate behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.851\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow sense of achievement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.759\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomatic symptom dimension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDysthymia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.733\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\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStructural Equation Modelling results\u003c/h2\u003e\u003cp\u003eThe indirect effect model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed a better fit than the direct effect model (Table\u0026nbsp;4), consistent with SEM best practices for testing mediational pathways [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Key paths included: Sleep disorders positively predicted anxiety (Estimate\u0026thinsp;=\u0026thinsp;4.498, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and burnout (Estimate\u0026thinsp;=\u0026thinsp;20.184, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Anxiety significantly predicted depression (Estimate\u0026thinsp;=\u0026thinsp;0.464, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), aligning with prior evidence that anxiety mediates sleep-depression links [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Sleep disorders exerted a significant indirect effect on depression via anxiety (Estimate\u0026thinsp;=\u0026thinsp;0.641, 95% CI = [0.382, 1.103], p\u0026thinsp;=\u0026thinsp;0.001), but not through burnout (p\u0026thinsp;=\u0026thinsp;0.149)\u0026mdash;a finding contrasting with occupational studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] yet consistent with academic contexts where burnout effects may be buffered [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDifferential tests across demographic groups\u003c/h2\u003e\u003cp\u003eANOVA results showed that urban students scored higher on the somatic symptom dimension of depression than rural students (M\u0026thinsp;=\u0026thinsp;8.65 vs. 8.07, F\u0026thinsp;=\u0026thinsp;2.238, p\u0026thinsp;=\u0026thinsp;0.026). Students with GPAs less than 2.0 reported more \u0026ldquo;inappropriate behaviour\u0026rdquo; (burnout) than those with GPAs between 3.0 and 3.49 (M\u0026thinsp;=\u0026thinsp;30.50 vs. 25.05, F\u0026thinsp;=\u0026thinsp;4.159, p\u0026thinsp;=\u0026thinsp;0.001). Monthly living expenses under \u0026yen;1,000 were linked to greater sleep instability (F\u0026thinsp;=\u0026thinsp;3.146, p\u0026thinsp;=\u0026thinsp;0.015) and medication use (F\u0026thinsp;=\u0026thinsp;4.323, p\u0026thinsp;=\u0026thinsp;0.001). No significant differences were observed across majors.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study examined the relationships among anxiety, sleep disorders, burnout, and depression in female college students, as well as the impact of demographic factors, using structural equation modelling. The main findings, placed within the context of existing research, provide insights into the mental health dynamics of this group, with implications for theory, practice, and future studies.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eKey Findings and Alignment with Existing Literature\u003c/h2\u003e\u003cp\u003eThe factor structures of all measures\u0026mdash;anxiety (2-factor), sleep disorders (5-factor), burnout (3-factor), and depression (2-factor)\u0026mdash;demonstrated acceptable to good fit (χ\u0026sup2;/df\u0026thinsp;\u0026lt;\u0026thinsp;3, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.08, SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.08), confirming their validity in this sample. This aligns with psychometric research on college student mental health scales, emphasising that robust factor structures are essential for ensuring measures accurately capture intended constructs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding relational pathways, the indirect effect model showed that sleep disorders significantly predicted anxiety and burnout, with anxiety serving as a primary mediator between sleep and depression. This aligns with previous longitudinal studies by Baglioni [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], who found that sleep issues tend to come before and worsen anxiety, which then elevates depressive symptoms\u0026mdash;especially in young adults facing academic stress. Interestingly, burnout did not predict depression, differing from findings in professional settings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but aligning with research on college students, where social support or academic engagement may buffer burnout effects [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDemographic patterns added further context to these findings. Urban students exhibited more somatic depressive symptoms than rural peers (M\u0026thinsp;=\u0026thinsp;8.65 vs. 8.07), reflecting heightened urban stressors like academic competition [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Students with monthly expenditure \u0026lt;\u0026yen;1,000 reported greater sleep instability (F\u0026thinsp;=\u0026thinsp;3.146, p\u0026thinsp;=\u0026thinsp;0.015), corroborating socioeconomic stress as a sleep disruptor [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Lower GPA predicted burnout-related behaviours (F\u0026thinsp;=\u0026thinsp;4.159, p\u0026thinsp;=\u0026thinsp;0.001), reinforcing bidirectional academic-mental health cycles [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eImplications for Practice\u003c/h2\u003e\u003cp\u003eThese findings offer actionable insights for developing targeted mental health interventions in university settings. Critically, the identified mediation pathway\u0026mdash;where sleep disturbances fuel anxiety, which in turn elevates depression risk\u0026mdash;highlights the paramount importance of early identification and intervention for insomnia and anxiety symptoms to disrupt this progression toward clinical depression.\u003c/p\u003e\u003cp\u003eBuilding on this, effective evidence-based strategies include reducing late-night screen time and promoting regular routines to decrease emotional distress, especially in students experiencing financial hardships. Anxiety treatments like cognitive-behavioural therapy (CBT), focusing on somatic symptoms, may help prevent depression, which is especially beneficial for urban students. For students with lower GPAs, academic support such as time management workshops can help decrease burnout behaviours, breaking the cycle of poor performance and emotional exhaustion.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eLongitudinal studies are needed to clarify causality in the sleep-anxiety-depression cycle. Expanding samples to include multiple institutions and male students will improve generalizability. Exploring why burnout did not directly predict depression\u0026mdash;potentially due to gendered coping strategies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] could clarify context-specific protective factors. Qualitative methods should examine lived experiences underlying demographic differences [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, differences [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, predictive modelling studies show similar trends [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], integrating qualitative methods like interviews can examine lived experiences, driving demographic differences, such as rural versus urban stressors.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the cross-sectional design prevents conclusions about causal relationships between sleep, anxiety, burnout, and depression. Longitudinal research is needed to clarify temporal pathways. Second, all measures were self-reported, which may be subject to recall bias and social desirability effects. Third, the sample was drawn from a single medical university in Hainan Province and included only female students, limiting generalisability to other regions, institutions, and male students. Finally, potential confounders such as family background, social support, or personality traits were not assessed and may have influenced the observed associations. These limitations should be considered when interpreting the findings, and future research should address them.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study suggests a complex interplay between sleep disturbances, anxiety, burnout, and depression among female college students, with anxiety mediating the impact of poor sleep on depressive symptoms. From a public health perspective, these interconnected challenges represent an essential concern with implications for equity in higher education and long-term wellbeing. Socioeconomic disadvantage and academic pressures may further amplify vulnerability, underscoring the need for interventions that address individual symptoms and broader structural determinants.\u003c/p\u003e\u003cp\u003eUniversities and policymakers could consider gender-sensitive, population-level approaches that combine sleep health promotion, financial support mechanisms, and accessible campus-based mental health services. Such strategies may help reduce the risk of depression and burnout, foster healthier learning environments and support the wellbeing of young women in higher education.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSelf-Rating Anxiety Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSelf-Rating Scale of Sleep\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSelf-Rating Depression Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStructural Equation Modelling\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComparative Fit Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandardised Root Mean Squared Residual\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComposite Reliability\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAVE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage Variance Extracted\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGrade Point Average\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eZ.Z. performed the statistical analyses, interpreted the findings, and drafted the initial manuscript. L.S., HP.H., QY.W., LQ.F., and SF.H. were responsible for participant recruitment and data collection. L.Y., MR.Y., and DE.C. conducted data validation and ensured the accuracy of statistical outputs. ZX.W. and HQ.S. conceived and designed the study framework, and critically reviewed and revised the manuscript for intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Hainan Provincial Natural Science Foundation of China (825QN324); the Education Department of Hainan Province (Hnky2025ZD-6); and the Student Innovation and Entrepreneurship Training Program at Hainan Medical University (X202411810043).\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was reviewed and approved by the Ethics Committee of Hainan Medical University (Approval No. HYLL-2024-587). The project, titled Construction and Empirical Research on the Model of Integrated Medical and Preventive Intervention for Mental Health of Medical Students in Hainan Based on the IMB Model, was granted approval through a fast review process on 3 June 2024, with validity until 31 December 2027. All participants were informed of the study aims, procedures, potential risks, and their rights, and provided written informed consent prior to participation, in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable (No individual personal data presented).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors thank all participants and staff at Hainan Medical University who supported this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEisenberg D, Gollust SE, Golberstein E, Hefner JL. Prevalence and correlates of depression, anxiety, and suicidality among university students. Am J Orthopsychiatry. 2007;77(4):534\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan SS, Zhang YS, Zhu W. Status and epidemiological characteristics of depression and anxiety among Chinese university students in 2023. BMC Public Health. 2025;25:1189.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan GXD. Prevalence of anxiety in college and university students. Lancet Reg Health \u0026ndash; Western Pac 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang JYY, Wang D. Mental health status and influencing factors among Chinese college students during COVID-19. \u003cem\u003eBMC Public Health\u003c/em\u003e 2020, 20:1966.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAssociation ACH. National College Health Assessment II: Reference Group Executive Summary. In. American College Health Association; 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi MMR, Bing M, Liu S. he association between sleep quality and anxiety symptoms: a cross-sectional study based on Tibetan university students at high altitude in China. Front Psychol. 2025;16:1505948.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang YG, Zhang Z, Han J, Zhang L, Chen R, Chen Y, Liu Q, Gao Z, Wu Y. Effect of Sleep Quality on Anxiety and Depression Symptoms among College Students in China\u0026rsquo;s Xizang Region: The Mediating Effect of Cognitive Emotion Regulation. Behav Sci. 2023;13:861.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao R, Wang H, Liu S, Wang X, Song S, Wang Y. Study on anxiety, depression, and sleep conditions and their interrelations among vocational college students during the COVID-19 pandemic management normalization. Front Public Health. 2024;12:1385639.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalmela-Aro K, Upadyaya K. Trajectories of school burnout during upper secondary education and their antecedents and consequences. J Educ Psychol. 2014;106(3):695\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaglioni C, Battagliese G, Feige B, Riemann D. Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011;135(1\u0026ndash;3):10\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoldstone A, Reynolds CF. Sleep and depression. Sleep Med Rev. 2008;12(1):19\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDyrbye LN, Thomas MR, Shanafelt TD. Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Acad Med. 2006;81(4):354\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchaufeli WB, Bakker AB. Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. J Organizational Behav. 2004;25(3):293\u0026ndash;315.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu X, Haegele JA, Liu H, Yu F. Academic stress, physical activity, sleep, and mental health among Chinese adolescents. Int J Environ Res Public Health. 2021;18(14):7257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026oacute;mez\u0026ndash;Urquiza JL, VSoriano A, Membrive\u0026ndash;Jim\u0026eacute;nez MJ, Ram\u0026iacute;rez\u0026ndash;Baena L, Aguayo\u0026ndash;Estremera R, Ortega\u0026ndash;Campos E, et al. Prevalence and levels of burnout in nursing students: a systematic review with meta\u0026ndash;analysis. Nurse Educ Pract. 2023;72:103753.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzwamel K, Kowalska W, Mazur E, Janus A, Bonikowska I, Jasik\u0026ndash;Pyzdrowska J. Determinants of burnout syndrome among undergraduate nursing students in Poland: a cross\u0026ndash;sectional study. BMC Med Educ. 2025;25:178.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePokhrel NB, Khadayat R, Tulachan P. Depression, anxiety, and burnout among medical students and residents of a medical school in Nepal: a cross\u0026ndash;sectional study. BMC Psychiatry. 2020;20:298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArbabisarjou A, Seyed Mehdi H, Sharif MR, Haji Alizadeh K, Yarmohammadzadeh P, Feyzollahi Z. The relationship between sleep quality and social intimacy and academic burn\u0026ndash;out in students of medical sciences. Global J Health Sci. 2016;8(5):231\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePagnin D, de Queiroz V, Carvalho YT, Dutra ASS, Amaral MB, Queiroz TT. The relation between burnout and sleep disorders in medical students. Acad Psychiatry. 2014;38:438\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePachankis JE, Hatzenbuehler ML, McLaughlin KA. Rural residence, psychiatric disorders, and access to mental health treatment in the United States. J Consult Clin Psychol. 2015;83(1):151\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeach LS, Patton GC. Socioeconomic status and adolescent mental health: A systematic review. Soc Psychiatry Psychiatr Epidemiol. 2013;48(11):1717\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTwenge JM, Nolen-Hoeksema S. Age, gender, race, socioeconomic status, and birth cohort differences on the Children\u0026rsquo;s Depression Inventory: A meta-analysis. J Abnorm Psychol. 2002;111(3):578\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKline RB. Principles and practice of structural equation modeling. Guilford Press; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZung WW. A rating instrument for anxiety disorders. Psychosomatics: J Consultation Liaison Psychiatry. 1971;12(6):371\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJ. L: Self-Rating Scale of Sleep (SRSS). Chin J Health Psychol 2012, 20(2):193\u0026ndash;5. Chinese.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLian RYLWL. Relationship between professional commitment and learning burnout of undergraduates and scales developing. Acta Physiol Sinica. 2005;37(5):632\u0026ndash;6. Chinese.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZung WW. A self-rating depression scale. Arch Gen Psychiatry. 1965;12(1):63\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAssociation AP. Guidelines for psychological practice with girls and women. In.; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarnefski N, Kraaij V, Spinhoven P. Cognitive emotion regulation strategies and depressive symptoms: A comparative study of five age groups. Pers Indiv Differ. 2001;30(8):1311\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGomes AR, Faria S, Gon\u0026ccedil;alves AM. Burnout in students: A systematic review (2019\u0026ndash;2024). Behav Sci. 2024;14(2):170.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHyde JS, Mezulis AH, Abramson LY. The ABCs of depression: Integrating affective, biological, and cognitive models to explain the emergence of the gender difference in depression. Psychol Rev. 2008;115(2):291\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Li X, Zhang L, Zhou Q. Predicting depression among Chinese female college students: A machine learning approach. BMC Public Health. 2025;25:21632.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Insomnia, Burnout, Depression, Female college students, Structural equation modelling, Public health, China","lastPublishedDoi":"10.21203/rs.3.rs-7494165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7494165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e Mental health challenges such as anxiety, insomnia, burnout, and depression are increasingly recognised as major public health concerns among university students. Female undergraduates may be particularly vulnerable due to gendered social expectations, academic pressures, and socioeconomic inequalities. However, the interplay of these conditions and their determinants remains underexplored in Chinese populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e A cross-sectional survey was conducted among 354 female undergraduates at Hainan Medical University in 2024. Validated self-report scales assessed anxiety, sleep quality, burnout, and depression. Structural equation modelling (SEM) was used to compare direct and indirect pathways, with demographic and academic factors (birthplace, major, monthly living expenses, and GPA) included as predictors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e The indirect effect model fit the data better than the direct model (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.15, CFI\u0026thinsp;=\u0026thinsp;0.95, RMSEA\u0026thinsp;=\u0026thinsp;0.05). Sleep disturbances significantly predicted anxiety (β\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and burnout (β\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while anxiety mediated the association between sleep disturbances and depression (indirect effect\u0026thinsp;=\u0026thinsp;0.64, 95% CI [0.38, 1.10], p\u0026thinsp;=\u0026thinsp;0.001). Burnout did not significantly mediate this pathway (p\u0026thinsp;=\u0026thinsp;0.149). Students in clinical medicine and those with lower monthly expenditure reported greater sleep disturbances, urban students experienced more somatic depressive symptoms, and low GPA was associated with higher burnout-related behaviours.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e Female undergraduates face interconnected mental health risks that constitute a pressing public health issue. Sleep disturbances and anxiety emerged as key prevention targets, while socioeconomic and academic disparities further increased vulnerability. Public health strategies should prioritise gender-sensitive, population-level interventions, including sleep health promotion, financial support, and campus-based mental health services to reduce risks of depression and academic burnout.\u003c/p\u003e","manuscriptTitle":"From adversity to depression: a structural equation modelling study of the public health burden and mediating pathways among female college students in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 10:57:25","doi":"10.21203/rs.3.rs-7494165/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"892d6e8e-0729-4153-89bf-5c2cbcbb8f31","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T12:26:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 10:57:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7494165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7494165","identity":"rs-7494165","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}