The Mediating Role of Psychological Resilience in the Relationship Between Health Anxiety and Burnout Among Healthcare Workers Working in Closed Environments

The Mediating Role of Psychological Resilience in the Relationship Between Health Anxiety and Burnout Among Healthcare Workers Working in Closed Environments | 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 The Mediating Role of Psychological Resilience in the Relationship Between Health Anxiety and Burnout Among Healthcare Workers Working in Closed Environments Altuğ ÇAĞATAY, İbrahim ÇAKMAK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8054758/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 Burnout is a pervasive problem among healthcare professionals, particularly in high-stress environments where psychological demands and uncertainty are constant. Health anxiety, intensified by occupational stressors and perceived health risks, can increase vulnerability to burnout. However, psychological resilience may serve as a protective mechanism that buffers the impact of anxiety on occupational well-being. This study aimed to examine the mediating role of psychological resilience in the relationship between health anxiety and burnout among healthcare workers. Methods A cross-sectional, questionnaire-based design was employed. Data were collected from 348 healthcare professionals working in a university hospital in Türkiye. Standardized instruments were used to measure health anxiety, psychological resilience, and burnout. The hypothesized structural model was tested using structural equation modeling (SEM) with maximum likelihood estimation and bias-corrected bootstrapping (5,000 resamples). Model fit was evaluated using multiple indices, including χ²/df, RMSEA, CFI, TLI, and SRMR. Results The final model demonstrated an acceptable fit to the data (χ²/df = 3.83, RMSEA = .090, SRMR = .066, CFI = .89, TLI = .86). Health anxiety significantly and positively predicted burnout (β = .522, p < .001) and negatively predicted psychological resilience (β = –.308, p < .001). Psychological resilience negatively predicted burnout (β = –.210, p < .001). The indirect effect of health anxiety on burnout via resilience was significant (βindirect = .065, 95% BC bootstrap CI [.032, .118]), confirming partial mediation. The model explained 38% of the variance in burnout and 9% in psychological resilience. Conclusions Health anxiety increases burnout among healthcare professionals, while psychological resilience serves as a partial buffer that mitigates this effect. These findings underscore the importance of enhancing resilience-focused interventions—such as stress management, coping skills, and mindfulness-based training—to protect healthcare workers from the psychological consequences of anxiety and emotional exhaustion. Future research should employ longitudinal designs and cross-cultural samples to validate these relationships and inform evidence-based mental health policies in healthcare institutions. This study highlights the importance of resilience-based interventions for sustaining the psychological well-being of healthcare professionals. Health anxiety Burnout Psychological resilience Mediation Healthcare professionals Structural equation modeling Figures Figure 1 Figure 2 Figure 3 1. Introduction Working in closed clinical environments has not only pushed healthcare systems to their limits but has also severely tested the mental and physical resilience of healthcare workers. Burnout has emerged as one of the most common and damaging consequences of this process, as it reduces job performance and negatively affects both patient safety and quality of care (1, 2, 3). Recent studies indicate that emotional exhaustion, depersonalization, and diminished personal accomplishment have significantly increased among frontline healthcare workers during periods of high workload, particularly during the pandemic. However, little is known about how these mechanisms operate among healthcare professionals working in confined and daylight-limited clinical environments, where environmental stressors may further intensify psychological strain. Burnout, defined as emotional exhaustion, depersonalization, and reduced personal accomplishment, is prevalent among stressful occupations such as healthcare (4, 5). Prior research has shown that psychological resilience acts as a critical protective factor, mitigating the adverse impact of occupational stress and preventing burnout (6, 7). For instance, in a study using structural equation modeling (SEM) among nurses, psychological resilience was found to directly and indirectly reduce burnout, mediating up to 48–87% of its effect (6). Similarly, psychological flexibility was found to reduce burnout and the intention to leave among critical care nurses (8). Another important construct that became prominent during the pandemic, especially in closed healthcare environments, is health anxiety—an intense and persistent worry about existing or potential health threats (9). Elevated health anxiety not only impairs quality of life and work performance but also increases the likelihood of burnout (10, 11). Studies have reported that healthcare professionals with higher psychological resilience experience less burnout and lower levels of health anxiety and depression (12). Likewise, research in China has shown that low job satisfaction and psychological well-being (flourishing) play key roles in burnout formation, indicating that both individual and contextual factors interact in shaping occupational well-being (13). Empirical evidence suggests that health anxiety affects burnout both directly and indirectly through psychological resilience (7). Individuals with lower tolerance for ambiguity and reduced psychological resilience are more prone to stress-induced exhaustion and depersonalization (14). Parallel findings among university students revealed that resilience negatively predicted pandemic-related burnout, suggesting that resilience serves as a psychological buffer against anxiety and stress (15). In addition to individual factors, organizational and social support mechanisms also play a vital role. Longitudinal evidence from Canada indicates that despite high burnout rates, strong institutional support and positive social relationships significantly reduced psychological distress (16). Similarly, job stress and work commitment have been identified as mediating variables linking pandemic severity to burnout (13). Supportive work environments, emotional safety, and social support systems enhance resilience and reduce burnout levels (17). Collectively, these findings underscore the importance of protecting the mental health of healthcare workers to sustain both workforce well-being and the quality of healthcare delivery. Recent studies have consistently demonstrated the protective role of psychological resilience against burnout and health anxiety (18, 19). However, existing research has largely focused on general hospital settings or pandemic-related stress without specifically examining healthcare workers operating in enclosed clinical environments, where limited space and reduced daylight exposure may amplify psychological vulnerability. The mediating mechanisms linking health anxiety, resilience, and burnout in such settings remain empirically underexplored. Addressing this gap, the present study investigates the direct and indirect relationships among health anxiety, psychological resilience, and burnout among healthcare professionals working in enclosed clinical environments in Türkiye, using a structural equation modeling approach. It is hypothesized that psychological resilience partially mediates the relationship between health anxiety and burnout, functioning as a psychological buffer against stress-induced exhaustion. By integrating these constructs into a unified analytical framework, this study aims to advance theoretical understanding of resilience as a mediator and offer practical insights for developing evidence-based interventions to reduce burnout in healthcare institutions. 2. Method This section includes information on the model to be followed in the research, the study universe and sample group, the tools and methods to be used in the data collection process, the principles for conducting the research within the framework of ethical rules, and the method by which the obtained data will be analyzed. Study design and setting This research was designed as a cross-sectional study conducted among healthcare workers employed in confined hospital environments. Data collection took place between 02 February and 14 February 2024 at Tokat Gaziosmanpaşa University Hospital, Turkey. The study followed the STROBE reporting guidelines for cross-sectional observational studies. Participants and sampling The study population included physicians, nurses, and allied healthcare staff working in inpatient units without natural ventilation or limited space. Sample size was calculated using Cochran’s (20) formula for an unknown population, yielding a minimum of 348 participants, which was achieved in the final dataset. Inclusion criteria were: being a full-time healthcare worker at the hospital and volunteering to participate; exclusion criteria included administrative staff or incomplete responses. The response rate was 31.6% (348 completed questionnaires out of 1100 invited healthcare workers). The sample consisted of 348 healthcare professionals from a total population of 1,100, yielding a response rate of 31.6%. According to the 10:1 parameter-to-sample ratio rule (21), the sample size was adequate for SEM analyses. Sample size adequacy was evaluated using G*Power 3.1. For a linear multiple regression (test: R² deviation from zero) with two predictors, the observed R² = 0.38 corresponds to an effect size f² = 0.613. Using α = 0.05, N = 348, and 2 predictors, the post-hoc calculated power (1 − β) is ≈ 1.00. Thus, the study had more than adequate power to detect the observed effects. In addition to the achieved post-hoc power (1 − β ≈ 1.00) based on the observed effect size (f² = 0.613, N = 348, α = 0.05), a sensitivity analysis was conducted using G*Power 3.1 to estimate required sample sizes for different hypothetical effect sizes. For detecting a small effect (f² = 0.02) with 80% power, 396 participants would be required; for a medium effect (f² = 0.15), 68 participants; and for a large effect (f² = 0.35), 38 participants. According to Cohen’s (22) benchmarks, this corresponds to a large effect size, confirming that the observed relationships are both statistically and practically significant.The current sample size (N = 348) therefore provides well above the required power for detecting even medium-sized effects, confirming that the study is statistically well-powered. Measures Health Anxiety Inventory (HAI) : Original scale developed by Salkovskis et al. (23), Turkish adaptation validated by Aydemir et al. (24). Cronbach’s α in this study was .91. Psychological Resilience Scale : Psychological Resilience Scale: Developed by Friborg et al. (25) to measure individuals’ psychological resilience. The Turkish validity and reliability study was conducted by Basım and Çetin (26). The scale consists of 33 items rated on a 5-point Likert type scale. In the present study, Cronbach’s α was .80. Maslach Burnout Inventory (MBI) : Maslach and Jackson (27); Turkish version validated by Ergin (28). Cronbach’s α values in this study were .88 (emotional exhaustion), .84 (depersonalization), and .86 (personal accomplishment). All instruments were rated on Likert-type scales. Higher scores indicated greater health anxiety, resilience, and burnout, respectively. Data collection procedure Questionnaires were administered electronically and on paper during work shifts. Participation was voluntary and anonymous. Written informed consent was obtained from all respondents before participation. Data were analyzed using AMOS 24 with the Maximum Likelihood (ML) estimator. Normality was examined through skewness and kurtosis (values within ± 2), and no severe multivariate outliers were detected (Mahalanobis distance, p < .001). Missing values (< 2%) were handled using full information maximum likelihood (FIML). The model was assessed with χ²/df, RMSEA, SRMR, CFI, TLI, and GFI indices. Bootstrap resampling (5,000 iterations) was applied to test indirect effects. Common method bias was assessed using Harman’s single-factor test, which revealed that the first factor accounted for 31.2% of the variance, suggesting that common method bias was not a major concern. To assess potential common method variance (CMV), both procedural and statistical remedies were applied. Procedurally, data were collected anonymously, and respondents were assured of confidentiality to reduce social desirability bias. Additionally, items measuring the predictor and criterion variables were interspersed to minimize response patterning. Statistically, Harman’s single-factor test revealed that the first factor accounted for 31.2% of the total variance, below the recommended 40% threshold, indicating that no single factor dominated the data. To further confirm this result, a Confirmatory Factor Analysis (CFA) common latent factor test was conducted by adding a common method factor to the measurement model. The comparison between the original and method-factor models showed no significant improvement in model fit (ΔCFI = .001, ΔRMSEA = .001), suggesting that common method bias was not a major concern in this study. Prior to the main analyses, data were examined for univariate and multivariate normality. Skewness and kurtosis values for all observed indicators ranged between − 1.25 and + 1.42, indicating mild deviations from normality. Mardia’s multivariate kurtosis coefficient (5.73) suggested slight non-normality in the multivariate distribution. Outlier analysis using the Mahalanobis D² statistic identified 11 extreme cases (p < .001), which were removed from the dataset to improve model robustness. The remaining data (n = 348) were analyzed using the Maximum Likelihood (ML) estimation method in AMOS 24. Although minor deviations from normality were present, the ML estimator was deemed appropriate because the sample size exceeded 200 and the deviations were within acceptable limits (29). To ensure robustness, the model was also tested using robust Maximum Likelihood (MLR) estimation in a sensitivity analysis, which produced nearly identical parameter estimates and fit indices. Therefore, ML results are reported for consistency and interpretability. Given that the observed variables were measured on Likert-type scales (1–5), alternative estimators such as WLSMV were considered; however, due to the continuous treatment of Likert scores and adequate sample size, ML estimation was retained as the primary analytic approach (30). Measurement Reliability and Validity Reliability and validity analyses confirmed the adequacy of all constructs. Cronbach’s α coefficients ranged from .80 to .92, composite reliability (CR) values exceeded .70, and average variance extracted (AVE) values exceeded .50, indicating satisfactory internal consistency and convergent validity. Statistical analysis All analyses were conducted using AMOS v.24 (IBM Corp., Armonk, NY, USA). Prior to structural modeling, descriptive statistics, reliability coefficients (Cronbach’s α, composite reliability), and confirmatory factor analyses (CFA) were performed. Convergent and discriminant validity were assessed using average variance extracted (AVE) and Fornell–Larcker criterion. Structural equation modeling (SEM) was applied to test the hypothesized mediating effect of psychological resilience between health anxiety and burnout. The maximum likelihood estimator with robust standard errors (MLR) was used, given the Likert-scale nature of the items. Model fit was assessed using χ²/df, RMSEA with 90% CI, CFI, TLI, and SRMR, following Hu and Bentler’s (31) thresholds. The mediating effect was tested using a bias-corrected bootstrap with 5000 resamples, generating 95% confidence intervals for indirect effects. A mediation effect was considered significant when the CI did not include zero. Effect sizes (standardized β) and the proportion of the total effect explained by the indirect pathway were also reported. Ethical considerations Ethical approval was obtained from Tokat Gaziosmanpaşa University Clinical Research Ethics Committee (approval numbers: 02.02.2024/24-KAEK-023 and 14.02.2024/24-KAEK-029). Institutional permission was granted by the hospital administration (date: 05.02.2024). All procedures complied with the Declaration of Helsinki. 2.1. Conceptual Framework and Research Model This study investigates the relationships among health anxiety, burnout, and psychological resilience among healthcare professionals working in enclosed clinical environments. Specifically, the research examines how health anxiety directly influences burnout and indirectly affects it through the mediating role of psychological resilience. Furthermore, the study explores whether these psychological constructs differ significantly according to key demographic (gender, marital status, educational level, professional specialization) and environmental (exposure to daylight, type of clinical setting) variables. The research was designed within the framework of a descriptive and correlational model, aiming to identify existing relationships among variables rather than manipulate them experimentally. In accordance with the methodological approach proposed by Karasar (32), this model describes the current state of the phenomena as they naturally occur within the healthcare context. Within the conceptual model, health anxiety is hypothesized to have a direct positive effect on burnout and a negative effect on psychological resilience, whereas psychological resilience is expected to have a negative effect on burnout. Moreover, psychological resilience is proposed to partially mediate the relationship between health anxiety and burnout, serving as a psychological buffer that mitigates the detrimental influence of health-related concerns on emotional exhaustion. Accordingly, the model provides an integrative framework that links anxiety-driven psychological processes to occupational outcomes through resilience mechanisms. The hypothesized structural relationships were empirically tested using structural equation modeling (SEM) to determine the strength, direction, and significance of the proposed paths. Figure 1. Conceptual model illustrating the hypothesized direct and indirect relationships among health anxiety, psychological resilience, and burnout among healthcare professionals. Health anxiety is hypothesized to have a direct positive effect on burnout and a negative effect on psychological resilience, whereas psychological resilience is expected to negatively predict burnout, indicating a partial mediating effect between health anxiety and burnout. Figure 1: Conceptual Model Conceptual model illustrating the hypothesized direct and indirect relationships among health anxiety, psychological resilience, and burnout. Health anxiety is proposed to have a direct positive effect on burnout and a negative effect on psychological resilience, whereas psychological resilience is expected to negatively predict burnout. Research hypotheses H1: Health anxiety significantly and positively predicts burnout levels of healthcare professionals. H2: Health anxiety significantly and negatively predicts the psychological resilience levels of healthcare workers. H3: Psychological resilience significantly and negatively predicts burnout levels of healthcare professionals. H4: Psychological resilience plays a partial mediator role in the relationship between health anxiety and burnout. 3. Results This section presents the results of confirmatory factor analysis (CFA) and structural equation modeling (SEM), including tests of the measurement model, structural relationships, and mediation effects. The results of the analyses provide empirical evidence for the hypothesized relationships among health anxiety, psychological resilience, and burnout in healthcare professionals working in confined environments. Before testing the structural model, the measurement model was evaluated to confirm the reliability and validity of the constructs. All scales demonstrated satisfactory internal consistency, and confirmatory factor analysis (CFA) results indicated acceptable model fit, supporting the distinctiveness of the three latent variables. Descriptive statistics and correlation analyses were conducted to examine the general tendencies and interrelations among the study variables. Subsequently, the hypothesized structural equation model (SEM) was tested to assess both direct and indirect effects, including the mediating role of psychological resilience in the relationship between health anxiety and burnout. The measurement model demonstrated acceptable fit: χ²(238) = 546.21, χ²/df = 2.30, RMSEA = .061 (90% CI [.055, .068]), CFI = .911, TLI = .903, SRMR = .041. All standardized factor loadings were significant (p < .001) and ranged from .41 to .92, indicating convergent validity. The structural model also exhibited an adequate fit: χ²(238) = 912.87, χ²/df = 3.83, RMSEA = .090 (90% CI [.082, .097]), CFI = .89, TLI = .86, SRMR = .066. These indices collectively support the acceptability of the final model for hypothesis testing. Descriptive Statistics A total of 348 healthcare workers participated in the study, all of whom were employed at a university hospital in Türkiye. The mean age of the participants was 33.10 years (SD = 7.85), and their average professional experience was 10.61 years. Of the total sample, 67.5% were women and 32.5% were men. In terms of marital status, 63.5% were married and 36.5% were single. Regarding educational status, 48.3% of the participants held a bachelor’s degree, 28.2% had an associate degree, 13.8% were high school graduates, and 9.8% held a postgraduate degree. The majority of participants were nurses or midwives (45.4%), followed by health technicians/technologists (23.6%), healthcare support staff (17.0%), physicians (6.6%), healthcare license holders (5.7%), and healthcare administrators (1.7%). Analysis of working conditions showed that 94% of participants reported working in closed environments, and 76% stated that they had limited or no exposure to natural daylight during working hours. Descriptive findings further revealed that the mean score of health anxiety was 38.83 (SD = 10.03), the mean burnout score was 71.47 (SD = 10.77), and the mean psychological resilience score was 103.02 (SD = 8.72), indicating moderate levels of burnout and psychological resilience among the participants, along with a relatively elevated level of health anxiety. Findings Regarding the Measurement Tool Before testing the structural relationships, the measurement properties of the study variables were examined through reliability and validity analyses. Internal consistency reliability was assessed using Cronbach’s alpha coefficients, and the results indicated that all measurement instruments demonstrated acceptable to high levels of reliability. Specifically, Cronbach’s alpha was .91 for the Health Anxiety Inventory, .80 for the Psychological Resilience Scale, and ranged between .84 and .88 for the subdimensions of the Maslach Burnout Inventory, indicating strong internal consistency. Convergent validity was evaluated using standardized factor loadings, Composite Reliability (CR), and Average Variance Extracted (AVE). All standardized loadings were statistically significant (p < .001) and ranged from .57 to .86, exceeding the recommended threshold of .50. CR values ranged between .84 and .92, surpassing the acceptable level of .70, while AVE values were between .52 and .58, meeting the minimum criterion of .50. These findings demonstrated that the observed variables adequately represented their corresponding latent constructs. Discriminant validity was also confirmed based on the Fornell–Larcker criterion. The square root of the AVE values for each construct was greater than the inter-construct correlation coefficients, indicating that each latent variable was empirically distinct from the others. Taken together, the results confirmed that the measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity, supporting its adequacy for further structural equation modeling. The measurement model consists of three latent factors (psychological resilience, burnout, and health anxiety) and eleven observed variables (structural style, perception of the future, family cohesion, self-perception, social competence, social resources, emotional exhaustion, depersonalization, personal accomplishment, hypersensitivity anxiety, and negative consequences). The path diagram of the measurement model is shown in Fig. 2 . Confirmatory factor analysis (CFA) was conducted to assess the factorial validity of the measurement model. All standardized factor loadings were statistically significant (p < .001), indicating that the observed indicators adequately reflected their respective latent constructs. The standardized factor loadings for the Health Anxiety construct ranged from .78 to .90 across its dimensions (e.g., Negative Consequences , Hypersensitivity-Anxiety ). For Psychological Resilience, factor loadings varied between .41 and .78 across dimensions such as Personal Competence , Family Cohesion , Self-Perception , and Social Resources . The factor loadings for the three subdimensions of Burnout ranged between .72 and .92 for Emotional Exhaustion , .59 and .81 for Depersonalization , and –.28 to .62 for Personal Accomplishment (reverse scored). All corresponding t-values exceeded the critical threshold of 1.96 and were statistically significant, supporting the measurement quality of the model. These findings confirm acceptable convergent validity. The analyses revealed that the standardized path coefficients ranged from − 0.277 to 0.925. Except for the personal accomplishment sub-dimension, the T value of all dimensions was greater than 1.96, and the standardized path coefficients in the measurement model were statistically significant (p < 0.001). Confirmatory factor analysis (CFA) was performed to evaluate the factorial validity of the measurement model, which consisted of three latent constructs: health anxiety, psychological resilience, and burnout (with three subdimensions: emotional exhaustion, depersonalization, and personal accomplishment). All observed variables were loaded on their respective latent factors, and all standardized loadings were 708 statistically significant (p < .001). The standardized factor loadings for the Health Anxiety Inventory ranged between .60 and .86, indicating strong item representation of the latent construct. The Psychological Resilience Scale showed item loadings between .57 and .79, while the Maslach Burnout Inventory subdimensions demonstrated loading ranges of .61–.85 for emotional exhaustion, .59–.81 for depersonalization, and .61–.82 for personal accomplishment. All t-values exceeded 1.96, confirming statistical significance at the 0.05 level. Convergent validity was further supported by high standardized factor loadings and AVE values greater than .50, indicating that the items sufficiently explained their respective constructs. Discriminant validity was established as the square roots of AVE values were greater than the inter-construct correlations. Overall, the CFA results confirmed that the three-factor model provided a satisfactory fit, validating the measurement structure for subsequent structural equation modeling (SEM). Structural Model Findings Figure 3 direct and indirect relationships between variables included in the structural model were analyzed. Health anxiety was considered a predictor of burnout, and psychological resilience was considered a mediating variable in this relationship. Data on the path coefficients for structural equation modeling are shown below. Taken together, the findings suggested that healthcare professionals experiencing higher levels of health anxiety are at increased risk of burnout. However, psychological resilience serves as a protective factor that weakens this relationship. These results highlight the importance of resilience-enhancing interventions to mitigate burnout in high-stress healthcare environments. As shown in Table 1 , health anxiety positively predicted burnout (β = .522, SE = .062, t = 8.401, p < .001; 95% BC CI [.401, .643]), and negatively predicted psychological resilience (β = –.308, SE = .027, t = − 4.039, p < .001; 95% BC CI [–.362, –.241]). Psychological resilience negatively predicted burnout (β = –.210, SE = .058, t = − 3.208, p < .001; 95% BC CI [–.310, –.119]). The indirect effect of health anxiety on burnout via resilience was significant (βindirect = .065, 95% BC CI [.032, .118]), confirming partial mediation. The final model explained 38% of the variance in burnout and 9% in resilience. Table 1 Path Coefficients Table in the Structural Model Path β (Standardized) SE t-value p-value 95% Bootstrap CI Effect Type Health Anxiety → Burnout .522 .062 8.401 < .001 [.401, .643] Direct Health Anxiety → Psychological Resilience –.308 .027 –4.039 < .001 [–.362, –.241] Direct Psychological Resilience → Burnout –.210 .058 –3.208 < .001 [–.310, –.119] Direct Indirect Effect (HA → PR → BO) .065 .021 — < .01 [.032, .118] Indirect Total Effect (HA → BO) .587 — — < .001 [.473, .708] Total R² (Burnout) = .38 — R² (Psychological Resilience) = .09 Note. Standardized beta coefficients are reported. Bootstrap confidence intervals were calculated using 5,000 resamples (bias-corrected). HA = Health Anxiety; PR = Psychological Resilience; BO = Burnout. The structural model explained 38% of the variance in burnout and 9% of the variance in psychological resilience. Health anxiety had a significant direct positive effect on burnout and a negative effect on psychological resilience. Psychological resilience partially mediated the relationship between health anxiety and burnout. Table 1 presents the direct effects estimated in the structural equation model. The findings revealed that health anxiety had a significant positive effect on burnout (β = .522, p < .001), indicating that healthcare professionals with higher levels of health-related anxiety were more likely to experience burnout. In addition, health anxiety had a significant negative effect on psychological resilience (β = –.308, p < .001), demonstrating that increasing levels of anxiety reduced individuals' psychological coping capacity. Psychological resilience, in turn, had a significant negative effect on burnout (β = –.210, p < .001), suggesting that resilience served as a protective factor by mitigating burnout levels among healthcare workers. All regression paths were statistically significant, and the bootstrap confidence intervals did not include zero, supporting the robustness of these relationships. These results support the hypothesized model and provide empirical evidence for the mediating role of psychological resilience in the relationship between health anxiety and burnout. Table 2 Means, standard deviations, and correlations among study variables Variable M SD 1 2 3 4 5 1. Health Anxiety 38.83 10.03 — 2. Psychological Resilience 103.02 8.72 –.308*** — 3. Emotional Exhaustion (Burnout-EE) 28.14 6.21 .481*** –.226*** — 4. Depersonalization (Burnout-DP) 16.34 4.82 .462*** –.204*** .612*** — 5. Personal Accomplishment (Burnout-PA)* 26.98 5.43 –.405*** .233*** –.388*** –.374*** — Note. ***p < .001. Higher scores in PA indicate lower burnout , as the subscale is reverse interpreted relative to EE and DP. Table 2 the correlation analysis revealed significant relationships among the study variables. Health anxiety was positively correlated with emotional exhaustion (r = .481, p < .001) and depersonalization (r = .462, p < .001), and negatively correlated with personal accomplishment (r = –.405, p < .001), indicating that higher levels of health anxiety were associated with greater burnout among healthcare workers. Psychological resilience showed a negative correlation with emotional exhaustion (r = –.226, p < .001) and depersonalization (r = –.204, p < .001), and a positive correlation with personal accomplishment (r = .233, p < .001), suggesting that resilience served as a protective factor against burnout. The correlations among the subdimensions of burnout were also moderate to strong; emotional exhaustion and depersonalization were positively correlated (r = .612, p < .001), while both were negatively associated with personal accomplishment. These findings support the hypothesized relationships among variables and justify further structural equation modeling to test direct and indirect effects. According to the findings obtained as a result of the analysis, the acceptance and rejection status of the hypotheses are as follows: H1 , which proposed that health anxiety would positively predict burnout levels, was supported. Health anxiety significantly and positively predicted burnout among healthcare professionals (β = .522, SE = .062, t = 8.401, p < .001; 95% BC CI [.401, .643]). This finding indicates that as health anxiety increases, burnout levels also rise, reflecting a direct risk association between psychological stress and emotional exhaustion. H2 predicted that health anxiety would negatively predict psychological resilience. The results confirmed this hypothesis (β = –.308, SE = .027, t = − 4.039, p < .001; 95% BC CI [–.362, –.241]). Thus, higher levels of health anxiety were associated with lower levels of resilience, implying that persistent health-related worries reduce individuals’ capacity for adaptive coping. H3 , which posited that psychological resilience negatively predicts burnout, was also supported (β = –.210, SE = .058, t = − 3.208, p < .001; 95% BC CI [–.310, –.119]). These results suggest that resilience functions as a protective psychological resource that reduces the likelihood of burnout symptoms among healthcare employees. H4 examined the mediating role of psychological resilience in the relationship between health anxiety and burnout. The bootstrap analysis revealed a significant indirect effect of health anxiety on burnout via resilience (βindirect = .065, 95% BC CI [.032, .118]). Because the direct effect of health anxiety on burnout remained significant (βdirect = .522, p < .001), partial mediation was confirmed. The indirect pathway accounted for approximately 11% of the total effect (βtotal = .587), indicating that resilience partially buffers the detrimental impact of health anxiety on burnout. Overall, all four hypotheses (H1–H4) were statistically supported, confirming the proposed conceptual model. The final model explained 38% of the variance in burnout and 9% of the variance in psychological resilience, demonstrating satisfactory explanatory power for psychological and occupational outcomes among healthcare workers. 4. Discussion The present study provides empirical support for the hypothesized relationships between health anxiety, psychological resilience, and burnout among healthcare professionals working in confined clinical settings. All four hypotheses were supported, indicating that resilience plays a protective yet partial mediating role in the link between health anxiety and burnout. The results provide robust empirical evidence supporting all proposed hypotheses. Consistent with H1 , health anxiety was found to be a significant positive predictor of burnout. This result aligns with prior studies showing that sustained anxiety related to health threats or infection risk can heighten emotional exhaustion and depersonalization in healthcare settings (33, 34, 35, 36). In environments with high uncertainty and continuous exposure to patient suffering, anxiety may escalate job stress and resource depletion, ultimately leading to burnout. Supporting H2 , health anxiety was negatively associated with psychological resilience. This finding is congruent with previous literature suggesting that anxiety reduces self-efficacy and adaptive coping capacities, thereby undermining resilience (37, 38, 39). When healthcare workers perceive themselves as vulnerable to illness or incapable of controlling health-related risks, their psychological flexibility tends to decline. H3 further confirmed the protective role of resilience against burnout. Individuals with higher levels of psychological resilience experience less emotional exhaustion and depersonalization and maintain a greater sense of personal accomplishment (40, 41, 42). This finding supports the stress-buffering perspective, suggesting that resilient healthcare professionals can manage occupational challenges more effectively. Finally, H4 demonstrated that psychological resilience partially mediates the relationship between health anxiety and burnout. The mediation pattern indicates that while health anxiety exerts a direct detrimental impact on burnout, part of this relationship operates indirectly through reduced resilience. In other words, resilience serves as a partial buffer that mitigates—but does not fully eliminate—the adverse effects of health anxiety on burnout. (43, 44, 45). This partial mediation highlights the dual role of resilience: as both an individual protective factor and a mechanism through which anxiety influences occupational outcomes. The findings suggest that healthcare organizations should prioritize resilience-oriented interventions—such as cognitive-behavioral training, mindfulness-based stress reduction, and structured peer support programs—to mitigate the psychological burden of health anxiety and prevent burnout. Future research should employ longitudinal designs and cross-cultural samples to validate the temporal and contextual stability of these findings. 5. Conclusion The present study provides empirical evidence that health anxiety is a critical psychological factor influencing burnout among healthcare professionals, and that psychological resilience serves as a partial mediator in this relationship. Consistent with theoretical perspectives on stress and coping, the findings indicate that elevated levels of health anxiety heighten emotional exhaustion and depersonalization, whereas resilience mitigates these adverse effects by enabling adaptive coping and emotional regulation. In other words, resilience functions as a protective psychological resource that buffers, but does not completely eliminate, the detrimental influence of anxiety on burnout. By integrating health anxiety, psychological resilience, and burnout into a single structural model, this research contributes to a more comprehensive understanding of psychological mechanisms underlying occupational well-being in healthcare contexts. The results underscore the importance of strengthening resilience-oriented interventions—such as stress management training, cognitive-behavioral coping programs, and mindfulness-based practices—to enhance healthcare workers’ ability to cope with persistent health-related concerns and prevent burnout. From an organizational standpoint, these findings emphasize the need for hospital administrators to monitor health anxiety symptoms, provide supportive supervision, and design workplace environments that promote emotional safety and psychological recovery. Despite its valuable contributions, this study has certain limitations. The cross-sectional design precludes causal inference; therefore, future research should adopt longitudinal or experimental approaches to validate these relationships over time. Additionally, the data were collected from healthcare professionals working in a single hospital context, which may limit generalizability. Future studies are encouraged to replicate this model in different cultural and organizational settings, explore potential moderating factors such as gender or profession type, and examine intervention-based strategies to enhance resilience and reduce burnout. In conclusion, this study highlights psychological resilience as a vital mechanism linking health anxiety and burnout among healthcare workers. Fostering resilience not only supports individual mental well-being but also contributes to the sustainability of healthcare systems facing chronic stress and uncertainty. The findings provide a theoretical and practical foundation for developing preventive strategies and mental health policies aimed at protecting healthcare professionals’ psychological resources and improving their long-term occupational health. This study employed a cross-sectional design, which precludes causal inferences among health anxiety, burnout, and resilience. The observed relationships should therefore be interpreted as associations rather than cause–effect links. Future research using longitudinal or experimental designs could better clarify the directionality and potential causal mechanisms underlying these relationships. Abbreviations HA Health Anxiety PR Psychological Resilience BO Burnout EE Emotional Exhaustion DP Depersonalization PA Personal Accomplishment SEM Structural Equation Modeling CFA Confirmatory Factor Analysis AVE Average Variance Extracted CR Composite Reliability CI Confidence Interval SD Standard Deviation SE Standard Error β Standardized Regression Coefficient (Beta) χ²/df Chi – square to Degrees of Freedom Ratio CFI Comparative Fit Index TLI Tucker – Lewis Index RMSEA Root Mean Square Error of Approximation SRMR Standardized Root Mean Square Residual NFI Normed Fit Index IFI Incremental Fit Index GFI Goodness of Fit Index AGFI Adjusted Goodness of Fit Index α Cronbach’s Alpha (Reliability Coefficient) f² Effect Size (Cohen’s f – squared) R² Coefficient of Determination Declarations Authors’ contributions Study concept and design: AÇ, and İÇ; acquisition of data: AÇ, and İÇ; analysis and interpretation of data: AÇ; drafting of the manuscript: AÇ; critical revision of the manuscript: AÇ, and İÇ; statistical analysis:AÇ; and study supervision: AÇ, and İÇ. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability No datasets were generated or analysed during the current study. Ethics Approval and Consent to Participate: Ethics committee approval for the study was obtained in accordance with the Declaration of Helsinki, with the decision of the Tokat Gaziosmanpaşa University Graduate Education Institute, dated February 2, 2024, and numbered 394121. Approval was also obtained with the letter of the Tokat Gaziosmanpaşa University Health Research and Application Center, dated February 14, 2024, and numbered 399109. Competing interests The authors declare no competing interests References Chen Q, Shen S, Liang Y, Kong L, Zhuang S, Li C. Analysis of mental health of healthcare workers and its influencing factors in three consecutive years. Work. 2025;80(3):1296–303. https://doi.org/10.1177/10519815241289827 Ruini C, Pira GL, Cordella E, Vescovelli F. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8054758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543840328,"identity":"66f47412-afba-4543-82ca-6fc9f7971d97","order_by":0,"name":"Altuğ ÇAĞATAY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACdhReBYSSwKuFmRnOAuIzcC0GRGphbCNCC38z/zHJLwx28vLu5w9+5p1XK2fOwHzwNg/Dn3xcWiQOM7NJyzAkG248k8wszbvtuLFlA1uyNQ+DgWUDLj0gLRIMBxg3NiQzALUcS9xwgMdMGqgFp8vkoVrsN/Y/Zv7NOwekhf8bXi0GQC2SHxgOJM6XSGaT5m2oAdnChleL4WFmY2sGg+TkDRKPzSznHDtgbNnMZmw5x8AYpxa5440Pb/6osLOd35/4+Mabmjo5c/bmhzfeVMjhiRhghPAApQ0OMDAw8TAcZjAARxReDcAI/AEKhwYwo46Q4lEwCkbBKBiBAAAlTEtUGTtDsQAAAABJRU5ErkJggg==","orcid":"","institution":"Tokat Gaziosmanpaşa University","correspondingAuthor":true,"prefix":"","firstName":"Altuğ","middleName":"","lastName":"ÇAĞATAY","suffix":""},{"id":543840329,"identity":"2a7d5cb8-a6f7-4521-a6ef-0124646787f2","order_by":1,"name":"İbrahim ÇAKMAK","email":"","orcid":"","institution":"Tokat Gaziosmanpaşa University","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"","lastName":"ÇAKMAK","suffix":""}],"badges":[],"createdAt":"2025-11-07 08:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8054758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8054758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95783842,"identity":"2d8e2e10-d7e0-4d41-9bf9-37d442dc44d6","added_by":"auto","created_at":"2025-11-13 04:33:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConceptual Model\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e· Health Anxiety (HA)\u003c/p\u003e\n\u003cp\u003e· Psychological Resilience (PR)\u003c/p\u003e\n\u003cp\u003e· Burnout (BO)\u003c/p\u003e\n\u003cp\u003e· Mediated pathway: HA → PR → BO\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8054758/v1/d680a9e0546bfaa91d44e2fd.png"},{"id":95783843,"identity":"76d2459b-5955-41a6-9e84-fc451311c827","added_by":"auto","created_at":"2025-11-13 04:33:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMeasurement Model (CFA Results)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePRS: Psychological Resilience Scale\u003c/p\u003e\n\u003cp\u003eBLS: Burnout Level Scale\u003c/p\u003e\n\u003cp\u003eHAS: Health Anxiety Scale\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8054758/v1/e32fd83526325a63478dec14.png"},{"id":95802210,"identity":"03d0219b-b244-4b2c-bb11-82caf0617d1b","added_by":"auto","created_at":"2025-11-13 08:27:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStructural Model (SEM Results)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8054758/v1/60ec0c68fd62d2fef94ffa8a.png"},{"id":95819132,"identity":"a9265301-823f-4f91-b6ee-44db514cb676","added_by":"auto","created_at":"2025-11-13 10:37:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1100533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8054758/v1/160e8305-39c6-4e44-9fa3-12c681e0f1f3.pdf"},{"id":95783845,"identity":"2614158e-4049-4d9c-92ae-c990e8a272cd","added_by":"auto","created_at":"2025-11-13 04:33:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":47598,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8054758/v1/4d66d17435bd5760da87cac1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Mediating Role of Psychological Resilience in the Relationship Between Health Anxiety and Burnout Among Healthcare Workers Working in Closed Environments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWorking in closed clinical environments has not only pushed healthcare systems to their limits but has also severely tested the mental and physical resilience of healthcare workers. Burnout has emerged as one of the most common and damaging consequences of this process, as it reduces job performance and negatively affects both patient safety and quality of care (1, 2, 3). Recent studies indicate that emotional exhaustion, depersonalization, and diminished personal accomplishment have significantly increased among frontline healthcare workers during periods of high workload, particularly during the pandemic. However, little is known about how these mechanisms operate among healthcare professionals working in confined and daylight-limited clinical environments, where environmental stressors may further intensify psychological strain.\u003c/p\u003e\u003cp\u003eBurnout, defined as emotional exhaustion, depersonalization, and reduced personal accomplishment, is prevalent among stressful occupations such as healthcare (4, 5). Prior research has shown that psychological resilience acts as a critical protective factor, mitigating the adverse impact of occupational stress and preventing burnout (6, 7). For instance, in a study using structural equation modeling (SEM) among nurses, psychological resilience was found to directly and indirectly reduce burnout, mediating up to 48\u0026ndash;87% of its effect (6). Similarly, psychological flexibility was found to reduce burnout and the intention to leave among critical care nurses (8).\u003c/p\u003e\u003cp\u003eAnother important construct that became prominent during the pandemic, especially in closed healthcare environments, is health anxiety\u0026mdash;an intense and persistent worry about existing or potential health threats (9). Elevated health anxiety not only impairs quality of life and work performance but also increases the likelihood of burnout (10, 11). Studies have reported that healthcare professionals with higher psychological resilience experience less burnout and lower levels of health anxiety and depression (12). Likewise, research in China has shown that low job satisfaction and psychological well-being (flourishing) play key roles in burnout formation, indicating that both individual and contextual factors interact in shaping occupational well-being (13).\u003c/p\u003e\u003cp\u003eEmpirical evidence suggests that health anxiety affects burnout both directly and indirectly through psychological resilience (7). Individuals with lower tolerance for ambiguity and reduced psychological resilience are more prone to stress-induced exhaustion and depersonalization (14). Parallel findings among university students revealed that resilience negatively predicted pandemic-related burnout, suggesting that resilience serves as a psychological buffer against anxiety and stress (15).\u003c/p\u003e\u003cp\u003eIn addition to individual factors, organizational and social support mechanisms also play a vital role. Longitudinal evidence from Canada indicates that despite high burnout rates, strong institutional support and positive social relationships significantly reduced psychological distress (16). Similarly, job stress and work commitment have been identified as mediating variables linking pandemic severity to burnout (13). Supportive work environments, emotional safety, and social support systems enhance resilience and reduce burnout levels (17). Collectively, these findings underscore the importance of protecting the mental health of healthcare workers to sustain both workforce well-being and the quality of healthcare delivery.\u003c/p\u003e\u003cp\u003eRecent studies have consistently demonstrated the protective role of psychological resilience against burnout and health anxiety (18, 19). However, existing research has largely focused on general hospital settings or pandemic-related stress without specifically examining healthcare workers operating in enclosed clinical environments, where limited space and reduced daylight exposure may amplify psychological vulnerability. The mediating mechanisms linking health anxiety, resilience, and burnout in such settings remain empirically underexplored.\u003c/p\u003e\u003cp\u003eAddressing this gap, the present study investigates the direct and indirect relationships among health anxiety, psychological resilience, and burnout among healthcare professionals working in enclosed clinical environments in T\u0026uuml;rkiye, using a structural equation modeling approach. It is hypothesized that psychological resilience partially mediates the relationship between health anxiety and burnout, functioning as a psychological buffer against stress-induced exhaustion. By integrating these constructs into a unified analytical framework, this study aims to advance theoretical understanding of resilience as a mediator and offer practical insights for developing evidence-based interventions to reduce burnout in healthcare institutions.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003eThis section includes information on the model to be followed in the research, the study universe and sample group, the tools and methods to be used in the data collection process, the principles for conducting the research within the framework of ethical rules, and the method by which the obtained data will be analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design and setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was designed as a cross-sectional study conducted among healthcare workers employed in confined hospital environments. Data collection took place between 02 February and 14 February 2024 at Tokat Gaziosmanpaşa University Hospital, Turkey. The study followed the STROBE reporting guidelines for cross-sectional observational studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants and sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population included physicians, nurses, and allied healthcare staff working in inpatient units without natural ventilation or limited space. Sample size was calculated using Cochran\u0026rsquo;s (20) formula for an unknown population, yielding a minimum of 348 participants, which was achieved in the final dataset. Inclusion criteria were: being a full-time healthcare worker at the hospital and volunteering to participate; exclusion criteria included administrative staff or incomplete responses. The response rate was 31.6% (348 completed questionnaires out of 1100 invited healthcare workers).\u003c/p\u003e\n\u003cp\u003eThe sample consisted of 348 healthcare professionals from a total population of 1,100, yielding a response rate of 31.6%. According to the 10:1 parameter-to-sample ratio rule (21), the sample size was adequate for SEM analyses.\u003c/p\u003e\n\u003cp\u003eSample size adequacy was evaluated using G*Power 3.1. For a linear multiple regression (test: R\u0026sup2; deviation from zero) with two predictors, the observed R\u0026sup2; = 0.38 corresponds to an effect size f\u0026sup2; = 0.613. Using \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05, N\u0026thinsp;=\u0026thinsp;348, and 2 predictors, the post-hoc calculated power (1\u0026thinsp;\u0026minus;\u0026thinsp;\u0026beta;) is \u0026asymp;\u0026thinsp;1.00. Thus, the study had more than adequate power to detect the observed effects. In addition to the achieved post-hoc power (1\u0026thinsp;\u0026minus;\u0026thinsp;\u0026beta;\u0026thinsp;\u0026asymp;\u0026thinsp;1.00) based on the observed effect size (f\u0026sup2; = 0.613, N\u0026thinsp;=\u0026thinsp;348, \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05), a sensitivity analysis was conducted using G*Power 3.1 to estimate required sample sizes for different hypothetical effect sizes. For detecting a \u003cem\u003esmall\u003c/em\u003e effect (f\u0026sup2; = 0.02) with 80% power, 396 participants would be required; for a \u003cem\u003emedium\u003c/em\u003e effect (f\u0026sup2; = 0.15), 68 participants; and for a large effect (f\u0026sup2; = 0.35), 38 participants. According to Cohen\u0026rsquo;s (22) benchmarks, this corresponds to a large effect size, confirming that the observed relationships are both statistically and practically significant.The current sample size (N\u0026thinsp;=\u0026thinsp;348) therefore provides well above the required power for detecting even medium-sized effects, confirming that the study is statistically well-powered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eHealth Anxiety Inventory (HAI)\u003c/em\u003e: Original scale developed by Salkovskis et al. (23), Turkish adaptation validated by Aydemir et al. (24). Cronbach\u0026rsquo;s \u0026alpha; in this study was .91.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ePsychological Resilience Scale\u003c/em\u003e: Psychological Resilience Scale: Developed by Friborg et al. (25) to measure individuals\u0026rsquo; psychological resilience. The Turkish validity and reliability study was conducted by Basım and \u0026Ccedil;etin (26). The scale consists of 33 items rated on a 5-point Likert type scale. In the present study, Cronbach\u0026rsquo;s \u0026alpha; was .80.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eMaslach Burnout Inventory (MBI)\u003c/em\u003e: Maslach and Jackson (27); Turkish version validated by Ergin (28). Cronbach\u0026rsquo;s \u0026alpha; values in this study were .88 (emotional exhaustion), .84 (depersonalization), and .86 (personal accomplishment).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll instruments were rated on Likert-type scales. Higher scores indicated greater health anxiety, resilience, and burnout, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuestionnaires were administered electronically and on paper during work shifts. Participation was voluntary and anonymous. Written informed consent was obtained from all respondents before participation.\u003c/p\u003e\n\u003cp\u003eData were analyzed using AMOS 24 with the Maximum Likelihood (ML) estimator. Normality was examined through skewness and kurtosis (values within \u0026plusmn;\u0026thinsp;2), and no severe multivariate outliers were detected (Mahalanobis distance, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Missing values (\u0026lt;\u0026thinsp;2%) were handled using full information maximum likelihood (FIML). The model was assessed with \u0026chi;\u0026sup2;/df, RMSEA, SRMR, CFI, TLI, and GFI indices. Bootstrap resampling (5,000 iterations) was applied to test indirect effects.\u003c/p\u003e\n\u003cp\u003eCommon method bias was assessed using Harman\u0026rsquo;s single-factor test, which revealed that the first factor accounted for 31.2% of the variance, suggesting that common method bias was not a major concern.\u003c/p\u003e\n\u003cp\u003eTo assess potential common method variance (CMV), both procedural and statistical remedies were applied. Procedurally, data were collected anonymously, and respondents were assured of confidentiality to reduce social desirability bias. Additionally, items measuring the predictor and criterion variables were interspersed to minimize response patterning.\u003c/p\u003e\n\u003cp\u003eStatistically, Harman\u0026rsquo;s single-factor test revealed that the first factor accounted for 31.2% of the total variance, below the recommended 40% threshold, indicating that no single factor dominated the data. To further confirm this result, a Confirmatory Factor Analysis (CFA) common latent factor test was conducted by adding a common method factor to the measurement model. The comparison between the original and method-factor models showed no significant improvement in model fit (\u0026Delta;CFI\u0026thinsp;=\u0026thinsp;.001, \u0026Delta;RMSEA\u0026thinsp;=\u0026thinsp;.001), suggesting that common method bias was not a major concern in this study.\u003c/p\u003e\n\u003cp\u003ePrior to the main analyses, data were examined for univariate and multivariate normality. Skewness and kurtosis values for all observed indicators ranged between \u0026minus;\u0026thinsp;1.25 and +\u0026thinsp;1.42, indicating mild deviations from normality. Mardia\u0026rsquo;s multivariate kurtosis coefficient (5.73) suggested slight non-normality in the multivariate distribution. Outlier analysis using the Mahalanobis D\u0026sup2; statistic identified 11 extreme cases (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), which were removed from the dataset to improve model robustness. The remaining data (n\u0026thinsp;=\u0026thinsp;348) were analyzed using the Maximum Likelihood (ML) estimation method in AMOS 24. Although minor deviations from normality were present, the ML estimator was deemed appropriate because the sample size exceeded 200 and the deviations were within acceptable limits (29). To ensure robustness, the model was also tested using robust Maximum Likelihood (MLR) estimation in a sensitivity analysis, which produced nearly identical parameter estimates and fit indices. Therefore, ML results are reported for consistency and interpretability. Given that the observed variables were measured on Likert-type scales (1\u0026ndash;5), alternative estimators such as WLSMV were considered; however, due to the continuous treatment of Likert scores and adequate sample size, ML estimation was retained as the primary analytic approach (30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Reliability and Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReliability and validity analyses confirmed the adequacy of all constructs. Cronbach\u0026rsquo;s \u0026alpha; coefficients ranged from .80 to .92, composite reliability (CR) values exceeded .70, and average variance extracted (AVE) values exceeded .50, indicating satisfactory internal consistency and convergent validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using AMOS v.24 (IBM Corp., Armonk, NY, USA). Prior to structural modeling, descriptive statistics, reliability coefficients (Cronbach\u0026rsquo;s \u0026alpha;, composite reliability), and confirmatory factor analyses (CFA) were performed. Convergent and discriminant validity were assessed using average variance extracted (AVE) and Fornell\u0026ndash;Larcker criterion.\u003c/p\u003e\n\u003cp\u003eStructural equation modeling (SEM) was applied to test the hypothesized mediating effect of psychological resilience between health anxiety and burnout. The maximum likelihood estimator with robust standard errors (MLR) was used, given the Likert-scale nature of the items. Model fit was assessed using \u0026chi;\u0026sup2;/df, RMSEA with 90% CI, CFI, TLI, and SRMR, following Hu and Bentler\u0026rsquo;s (31) thresholds.\u003c/p\u003e\n\u003cp\u003eThe mediating effect was tested using a bias-corrected bootstrap with 5000 resamples, generating 95% confidence intervals for indirect effects. A mediation effect was considered significant when the CI did not include zero. Effect sizes (standardized \u0026beta;) and the proportion of the total effect explained by the indirect pathway were also reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval\u0026nbsp;was obtained from Tokat Gaziosmanpaşa University Clinical Research Ethics Committee (approval numbers: 02.02.2024/24-KAEK-023 and 14.02.2024/24-KAEK-029). Institutional permission was granted by the hospital administration (date: 05.02.2024). All procedures complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Conceptual Framework and Research Model\u003c/h2\u003e\n\u003cp\u003eThis study investigates the relationships among health anxiety, burnout, and psychological resilience among healthcare professionals working in enclosed clinical environments. Specifically, the research examines how health anxiety directly influences burnout and indirectly affects it through the mediating role of psychological resilience. Furthermore, the study explores whether these psychological constructs differ significantly according to key demographic (gender, marital status, educational level, professional specialization) and environmental (exposure to daylight, type of clinical setting) variables.\u003c/p\u003e\n\u003cp\u003eThe research was designed within the framework of a descriptive and correlational model, aiming to identify existing relationships among variables rather than manipulate them experimentally. In accordance with the methodological approach proposed by Karasar (32), this model describes the current state of the phenomena as they naturally occur within the healthcare context.\u003c/p\u003e\n\u003cp\u003eWithin the conceptual model, health anxiety is hypothesized to have a direct positive effect on burnout and a negative effect on psychological resilience, whereas psychological resilience is expected to have a negative effect on burnout. Moreover, psychological resilience is proposed to partially mediate the relationship between health anxiety and burnout, serving as a psychological buffer that mitigates the detrimental influence of health-related concerns on emotional exhaustion.\u003c/p\u003e\n\u003cp\u003eAccordingly, the model provides an integrative framework that links anxiety-driven psychological processes to occupational outcomes through resilience mechanisms. The hypothesized structural relationships were empirically tested using structural equation modeling (SEM) to determine the strength, direction, and significance of the proposed paths.\u003c/p\u003e\n\u003cp\u003eFigure 1. Conceptual model illustrating the hypothesized direct and indirect relationships among health anxiety, psychological resilience, and burnout among healthcare professionals. Health anxiety is hypothesized to have a direct positive effect on burnout and a negative effect on psychological resilience, whereas psychological resilience is expected to negatively predict burnout, indicating a partial mediating effect between health anxiety and burnout.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1: Conceptual Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptual model illustrating the hypothesized direct and indirect relationships among health anxiety, psychological resilience, and burnout. Health anxiety is proposed to have a direct positive effect on burnout and a negative effect on psychological resilience, whereas psychological resilience is expected to negatively predict burnout.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResearch hypotheses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u0026nbsp;\u003c/strong\u003eHealth anxiety significantly and positively predicts burnout levels of healthcare professionals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u0026nbsp;\u003c/strong\u003eHealth anxiety significantly and negatively predicts the psychological resilience levels of healthcare workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u0026nbsp;\u003c/strong\u003ePsychological resilience significantly and negatively predicts burnout levels of healthcare professionals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u0026nbsp;\u003c/strong\u003ePsychological resilience plays a partial mediator role in the relationship between health anxiety and burnout.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThis section presents the results of confirmatory factor analysis (CFA) and structural equation modeling (SEM), including tests of the measurement model, structural relationships, and mediation effects.\u003c/p\u003e\n\u003cp\u003eThe results of the analyses provide empirical evidence for the hypothesized relationships among health anxiety, psychological resilience, and burnout in healthcare professionals working in confined environments. Before testing the structural model, the measurement model was evaluated to confirm the reliability and validity of the constructs. All scales demonstrated satisfactory internal consistency, and confirmatory factor analysis (CFA) results indicated acceptable model fit, supporting the distinctiveness of the three latent variables. Descriptive statistics and correlation analyses were conducted to examine the general tendencies and interrelations among the study variables. Subsequently, the hypothesized structural equation model (SEM) was tested to assess both direct and indirect effects, including the mediating role of psychological resilience in the relationship between health anxiety and burnout.\u003c/p\u003e\n\u003cp\u003eThe measurement model demonstrated acceptable fit: \u0026chi;\u0026sup2;(238)\u0026thinsp;=\u0026thinsp;546.21, \u0026chi;\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.30, RMSEA\u0026thinsp;=\u0026thinsp;.061 (90% CI [.055, .068]), CFI\u0026thinsp;=\u0026thinsp;.911, TLI\u0026thinsp;=\u0026thinsp;.903, SRMR\u0026thinsp;=\u0026thinsp;.041. All standardized factor loadings were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ranged from .41 to .92, indicating convergent validity. The structural model also exhibited an adequate fit: \u0026chi;\u0026sup2;(238)\u0026thinsp;=\u0026thinsp;912.87, \u0026chi;\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.83, RMSEA\u0026thinsp;=\u0026thinsp;.090 (90% CI [.082, .097]), CFI\u0026thinsp;=\u0026thinsp;.89, TLI\u0026thinsp;=\u0026thinsp;.86, SRMR\u0026thinsp;=\u0026thinsp;.066. These indices collectively support the acceptability of the final model for hypothesis testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 348 healthcare workers participated in the study, all of whom were employed at a university hospital in T\u0026uuml;rkiye. The mean age of the participants was 33.10 years (SD\u0026thinsp;=\u0026thinsp;7.85), and their average professional experience was 10.61 years. Of the total sample, 67.5% were women and 32.5% were men. In terms of marital status, 63.5% were married and 36.5% were single.\u003c/p\u003e\n\u003cp\u003eRegarding educational status, 48.3% of the participants held a bachelor\u0026rsquo;s degree, 28.2% had an associate degree, 13.8% were high school graduates, and 9.8% held a postgraduate degree. The majority of participants were nurses or midwives (45.4%), followed by health technicians/technologists (23.6%), healthcare support staff (17.0%), physicians (6.6%), healthcare license holders (5.7%), and healthcare administrators (1.7%).\u003c/p\u003e\n\u003cp\u003eAnalysis of working conditions showed that 94% of participants reported working in closed environments, and 76% stated that they had limited or no exposure to natural daylight during working hours. Descriptive findings further revealed that the mean score of health anxiety was 38.83 (SD\u0026thinsp;=\u0026thinsp;10.03), the mean burnout score was 71.47 (SD\u0026thinsp;=\u0026thinsp;10.77), and the mean psychological resilience score was 103.02 (SD\u0026thinsp;=\u0026thinsp;8.72), indicating moderate levels of burnout and psychological resilience among the participants, along with a relatively elevated level of health anxiety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings Regarding the Measurement Tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore testing the structural relationships, the measurement properties of the study variables were examined through reliability and validity analyses. Internal consistency reliability was assessed using Cronbach\u0026rsquo;s alpha coefficients, and the results indicated that all measurement instruments demonstrated acceptable to high levels of reliability. Specifically, Cronbach\u0026rsquo;s alpha was .91 for the Health Anxiety Inventory, .80 for the Psychological Resilience Scale, and ranged between .84 and .88 for the subdimensions of the Maslach Burnout Inventory, indicating strong internal consistency.\u003c/p\u003e\n\u003cp\u003eConvergent validity was evaluated using standardized factor loadings, Composite Reliability (CR), and Average Variance Extracted (AVE). All standardized loadings were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ranged from .57 to .86, exceeding the recommended threshold of .50. CR values ranged between .84 and .92, surpassing the acceptable level of .70, while AVE values were between .52 and .58, meeting the minimum criterion of .50. These findings demonstrated that the observed variables adequately represented their corresponding latent constructs.\u003c/p\u003e\n\u003cp\u003eDiscriminant validity was also confirmed based on the Fornell\u0026ndash;Larcker criterion. The square root of the AVE values for each construct was greater than the inter-construct correlation coefficients, indicating that each latent variable was empirically distinct from the others. Taken together, the results confirmed that the measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity, supporting its adequacy for further structural equation modeling.\u003c/p\u003e\n\u003cp\u003eThe measurement model consists of three latent factors (psychological resilience, burnout, and health anxiety) and eleven observed variables (structural style, perception of the future, family cohesion, self-perception, social competence, social resources, emotional exhaustion, depersonalization, personal accomplishment, hypersensitivity anxiety, and negative consequences). The path diagram of the measurement model is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConfirmatory factor analysis (CFA) was conducted to assess the factorial validity of the measurement model. All standardized factor loadings were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that the observed indicators adequately reflected their respective latent constructs. The standardized factor loadings for the Health Anxiety construct ranged from .78 to .90 across its dimensions (e.g., \u003cem\u003eNegative Consequences\u003c/em\u003e, \u003cem\u003eHypersensitivity-Anxiety\u003c/em\u003e). For Psychological Resilience, factor loadings varied between .41 and .78 across dimensions such as \u003cem\u003ePersonal Competence\u003c/em\u003e, \u003cem\u003eFamily Cohesion\u003c/em\u003e, \u003cem\u003eSelf-Perception\u003c/em\u003e, and \u003cem\u003eSocial Resources\u003c/em\u003e. The factor loadings for the three subdimensions of Burnout ranged between .72 and .92 for \u003cem\u003eEmotional Exhaustion\u003c/em\u003e, .59 and .81 for \u003cem\u003eDepersonalization\u003c/em\u003e, and \u0026ndash;.28 to .62 for \u003cem\u003ePersonal Accomplishment\u003c/em\u003e (reverse scored). All corresponding t-values exceeded the critical threshold of 1.96 and were statistically significant, supporting the measurement quality of the model. These findings confirm acceptable convergent validity.\u003c/p\u003e\n\u003cp\u003eThe analyses revealed that the standardized path coefficients ranged from \u0026minus;\u0026thinsp;0.277 to 0.925. Except for the personal accomplishment sub-dimension, the T value of all dimensions was greater than 1.96, and the standardized path coefficients in the measurement model were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eConfirmatory factor analysis (CFA) was performed to evaluate the factorial validity of the measurement model, which consisted of three latent constructs: health anxiety, psychological resilience, and burnout (with three subdimensions: emotional exhaustion, depersonalization, and personal accomplishment). All observed variables were loaded on their respective latent factors, and all standardized loadings were 708 statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\n\u003cp\u003eThe standardized factor loadings for the Health Anxiety Inventory ranged between .60 and .86, indicating strong item representation of the latent construct. The Psychological Resilience Scale showed item loadings between .57 and .79, while the Maslach Burnout Inventory subdimensions demonstrated loading ranges of .61\u0026ndash;.85 for emotional exhaustion, .59\u0026ndash;.81 for depersonalization, and .61\u0026ndash;.82 for personal accomplishment. All t-values exceeded 1.96, confirming statistical significance at the 0.05 level.\u003c/p\u003e\n\u003cp\u003eConvergent validity was further supported by high standardized factor loadings and AVE values greater than .50, indicating that the items sufficiently explained their respective constructs. Discriminant validity was established as the square roots of AVE values were greater than the inter-construct correlations.\u003c/p\u003e\n\u003cp\u003eOverall, the CFA results confirmed that the three-factor model provided a satisfactory fit, validating the measurement structure for subsequent structural equation modeling (SEM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Model Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e direct and indirect relationships between variables included in the structural model were analyzed. Health anxiety was considered a predictor of burnout, and psychological resilience was considered a mediating variable in this relationship. Data on the path coefficients for structural equation modeling are shown below.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, the findings suggested that healthcare professionals experiencing higher levels of health anxiety are at increased risk of burnout. However, psychological resilience serves as a protective factor that weakens this relationship. These results highlight the importance of resilience-enhancing interventions to mitigate burnout in high-stress healthcare environments.\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, health anxiety positively predicted burnout (\u0026beta;\u0026thinsp;=\u0026thinsp;.522, SE\u0026thinsp;=\u0026thinsp;.062, t\u0026thinsp;=\u0026thinsp;8.401, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; 95% BC CI [.401, .643]), and negatively predicted psychological resilience (\u0026beta; = \u0026ndash;.308, SE\u0026thinsp;=\u0026thinsp;.027, t = \u0026minus;\u0026thinsp;4.039, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; 95% BC CI [\u0026ndash;.362, \u0026ndash;.241]). Psychological resilience negatively predicted burnout (\u0026beta; = \u0026ndash;.210, SE\u0026thinsp;=\u0026thinsp;.058, t = \u0026minus;\u0026thinsp;3.208, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; 95% BC CI [\u0026ndash;.310, \u0026ndash;.119]). The indirect effect of health anxiety on burnout via resilience was significant (\u0026beta;indirect\u0026thinsp;=\u0026thinsp;.065, 95% BC CI [.032, .118]), confirming partial mediation. The final model explained 38% of the variance in burnout and 9% in resilience.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePath Coefficients Table in the Structural Model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePath\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta; (Standardized)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% Bootstrap CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect Type\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealth Anxiety \u0026rarr; Burnout\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.062\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.401\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[.401, .643]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealth Anxiety \u0026rarr; Psychological Resilience\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;4.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u0026ndash;.362, \u0026ndash;.241]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePsychological Resilience \u0026rarr; Burnout\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;3.208\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[\u0026ndash;.310, \u0026ndash;.119]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDirect\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect Effect (HA \u0026rarr; PR \u0026rarr; BO)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.065\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.021\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.01\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[.032, .118]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndirect\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal Effect (HA \u0026rarr; BO)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e.587\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[.473, .708]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003cstrong\u003eR\u0026sup2; (Burnout)\u0026thinsp;=\u0026thinsp;.38 \u0026mdash; R\u0026sup2; (Psychological Resilience)\u0026thinsp;=\u0026thinsp;.09\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote. Standardized beta coefficients are reported. Bootstrap confidence intervals were calculated using 5,000 resamples (bias-corrected). HA = Health Anxiety; PR = Psychological Resilience; BO = Burnout.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe structural model explained 38% of the variance in burnout and 9% of the variance in psychological resilience. Health anxiety had a significant direct positive effect on burnout and a negative effect on psychological resilience. Psychological resilience partially mediated the relationship between health anxiety and burnout.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the direct effects estimated in the structural equation model. The findings revealed that health anxiety had a significant positive effect on burnout (\u0026beta;\u0026thinsp;=\u0026thinsp;.522, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that healthcare professionals with higher levels of health-related anxiety were more likely to experience burnout. In addition, health anxiety had a significant negative effect on psychological resilience (\u0026beta; = \u0026ndash;.308, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), demonstrating that increasing levels of anxiety reduced individuals' psychological coping capacity. Psychological resilience, in turn, had a significant negative effect on burnout (\u0026beta; = \u0026ndash;.210, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that resilience served as a protective factor by mitigating burnout levels among healthcare workers. All regression paths were statistically significant, and the bootstrap confidence intervals did not include zero, supporting the robustness of these relationships. These results support the hypothesized model and provide empirical evidence for the mediating role of psychological resilience in the relationship between health anxiety and burnout.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMeans, standard deviations, and correlations among study variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1. Health Anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2. Psychological Resilience\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e103.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.308***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3. Emotional Exhaustion (Burnout-EE)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.481***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.226***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e4. Depersonalization (Burnout-DP)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.462***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.204***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.612***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e5. Personal Accomplishment (Burnout-PA)*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.405***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.233***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.388***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ndash;.374***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Higher scores in PA indicate \u003cstrong\u003elower burnout\u003c/strong\u003e, as the subscale is reverse interpreted relative to EE and DP.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e the correlation analysis revealed significant relationships among the study variables. Health anxiety was positively correlated with emotional exhaustion (r\u0026thinsp;=\u0026thinsp;.481, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and depersonalization (r\u0026thinsp;=\u0026thinsp;.462, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and negatively correlated with personal accomplishment (r = \u0026ndash;.405, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that higher levels of health anxiety were associated with greater burnout among healthcare workers. Psychological resilience showed a negative correlation with emotional exhaustion (r = \u0026ndash;.226, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and depersonalization (r = \u0026ndash;.204, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and a positive correlation with personal accomplishment (r\u0026thinsp;=\u0026thinsp;.233, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that resilience served as a protective factor against burnout. The correlations among the subdimensions of burnout were also moderate to strong; emotional exhaustion and depersonalization were positively correlated (r\u0026thinsp;=\u0026thinsp;.612, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), while both were negatively associated with personal accomplishment. These findings support the hypothesized relationships among variables and justify further structural equation modeling to test direct and indirect effects.\u003c/p\u003e\n\u003cp\u003eAccording to the findings obtained as a result of the analysis, the acceptance and rejection status of the hypotheses are as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e, which proposed that health anxiety would positively predict burnout levels, was supported. Health anxiety significantly and positively predicted burnout among healthcare professionals (\u0026beta;\u0026thinsp;=\u0026thinsp;.522, SE\u0026thinsp;=\u0026thinsp;.062, t\u0026thinsp;=\u0026thinsp;8.401, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; 95% BC CI [.401, .643]). This finding indicates that as health anxiety increases, burnout levels also rise, reflecting a direct risk association between psychological stress and emotional exhaustion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e predicted that health anxiety would negatively predict psychological resilience. The results confirmed this hypothesis (\u0026beta; = \u0026ndash;.308, SE\u0026thinsp;=\u0026thinsp;.027, t = \u0026minus;\u0026thinsp;4.039, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; 95% BC CI [\u0026ndash;.362, \u0026ndash;.241]). Thus, higher levels of health anxiety were associated with lower levels of resilience, implying that persistent health-related worries reduce individuals\u0026rsquo; capacity for adaptive coping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e, which posited that psychological resilience negatively predicts burnout, was also supported (\u0026beta; = \u0026ndash;.210, SE\u0026thinsp;=\u0026thinsp;.058, t = \u0026minus;\u0026thinsp;3.208, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; 95% BC CI [\u0026ndash;.310, \u0026ndash;.119]). These results suggest that resilience functions as a protective psychological resource that reduces the likelihood of burnout symptoms among healthcare employees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e examined the mediating role of psychological resilience in the relationship between health anxiety and burnout. The bootstrap analysis revealed a significant indirect effect of health anxiety on burnout via resilience (\u0026beta;indirect\u0026thinsp;=\u0026thinsp;.065, 95% BC CI [.032, .118]). Because the direct effect of health anxiety on burnout remained significant (\u0026beta;direct\u0026thinsp;=\u0026thinsp;.522, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), \u003cstrong\u003epartial mediation\u003c/strong\u003e was confirmed. The indirect pathway accounted for approximately 11% of the total effect (\u0026beta;total\u0026thinsp;=\u0026thinsp;.587), indicating that resilience partially buffers the detrimental impact of health anxiety on burnout.\u003c/p\u003e\n\u003cp\u003eOverall, all four hypotheses (H1\u0026ndash;H4) were statistically supported, confirming the proposed conceptual model. The final model explained 38% of the variance in burnout and 9% of the variance in psychological resilience, demonstrating satisfactory explanatory power for psychological and occupational outcomes among healthcare workers.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study provides empirical support for the hypothesized relationships between health anxiety, psychological resilience, and burnout among healthcare professionals working in confined clinical settings. All four hypotheses were supported, indicating that resilience plays a protective yet partial mediating role in the link between health anxiety and burnout. The results provide robust empirical evidence supporting all proposed hypotheses. Consistent with \u003cb\u003eH1\u003c/b\u003e, health anxiety was found to be a significant positive predictor of burnout. This result aligns with prior studies showing that sustained anxiety related to health threats or infection risk can heighten emotional exhaustion and depersonalization in healthcare settings (33, 34, 35, 36). In environments with high uncertainty and continuous exposure to patient suffering, anxiety may escalate job stress and resource depletion, ultimately leading to burnout.\u003c/p\u003e\u003cp\u003eSupporting \u003cb\u003eH2\u003c/b\u003e, health anxiety was negatively associated with psychological resilience. This finding is congruent with previous literature suggesting that anxiety reduces self-efficacy and adaptive coping capacities, thereby undermining resilience (37, 38, 39). When healthcare workers perceive themselves as vulnerable to illness or incapable of controlling health-related risks, their psychological flexibility tends to decline.\u003c/p\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e further confirmed the protective role of resilience against burnout. Individuals with higher levels of psychological resilience experience less emotional exhaustion and depersonalization and maintain a greater sense of personal accomplishment (40, 41, 42). This finding supports the stress-buffering perspective, suggesting that resilient healthcare professionals can manage occupational challenges more effectively.\u003c/p\u003e\u003cp\u003eFinally, \u003cb\u003eH4\u003c/b\u003e demonstrated that psychological resilience partially mediates the relationship between health anxiety and burnout. The mediation pattern indicates that while health anxiety exerts a direct detrimental impact on burnout, part of this relationship operates indirectly through reduced resilience. In other words, resilience serves as a partial buffer that mitigates\u0026mdash;but does not fully eliminate\u0026mdash;the adverse effects of health anxiety on burnout. (43, 44, 45). This partial mediation highlights the dual role of resilience: as both an individual protective factor and a mechanism through which anxiety influences occupational outcomes.\u003c/p\u003e\u003cp\u003eThe findings suggest that healthcare organizations should prioritize resilience-oriented interventions\u0026mdash;such as cognitive-behavioral training, mindfulness-based stress reduction, and structured peer support programs\u0026mdash;to mitigate the psychological burden of health anxiety and prevent burnout. Future research should employ longitudinal designs and cross-cultural samples to validate the temporal and contextual stability of these findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study provides empirical evidence that health anxiety is a critical psychological factor influencing burnout among healthcare professionals, and that psychological resilience serves as a partial mediator in this relationship. Consistent with theoretical perspectives on stress and coping, the findings indicate that elevated levels of health anxiety heighten emotional exhaustion and depersonalization, whereas resilience mitigates these adverse effects by enabling adaptive coping and emotional regulation. In other words, resilience functions as a protective psychological resource that buffers, but does not completely eliminate, the detrimental influence of anxiety on burnout.\u003c/p\u003e\u003cp\u003eBy integrating health anxiety, psychological resilience, and burnout into a single structural model, this research contributes to a more comprehensive understanding of psychological mechanisms underlying occupational well-being in healthcare contexts. The results underscore the importance of strengthening resilience-oriented interventions\u0026mdash;such as stress management training, cognitive-behavioral coping programs, and mindfulness-based practices\u0026mdash;to enhance healthcare workers\u0026rsquo; ability to cope with persistent health-related concerns and prevent burnout. From an organizational standpoint, these findings emphasize the need for hospital administrators to monitor health anxiety symptoms, provide supportive supervision, and design workplace environments that promote emotional safety and psychological recovery.\u003c/p\u003e\u003cp\u003eDespite its valuable contributions, this study has certain limitations. The cross-sectional design precludes causal inference; therefore, future research should adopt longitudinal or experimental approaches to validate these relationships over time. Additionally, the data were collected from healthcare professionals working in a single hospital context, which may limit generalizability. Future studies are encouraged to replicate this model in different cultural and organizational settings, explore potential moderating factors such as gender or profession type, and examine intervention-based strategies to enhance resilience and reduce burnout.\u003c/p\u003e\u003cp\u003eIn conclusion, this study highlights psychological resilience as a vital mechanism linking health anxiety and burnout among healthcare workers. Fostering resilience not only supports individual mental well-being but also contributes to the sustainability of healthcare systems facing chronic stress and uncertainty. The findings provide a theoretical and practical foundation for developing preventive strategies and mental health policies aimed at protecting healthcare professionals\u0026rsquo; psychological resources and improving their long-term occupational health.\u003c/p\u003e\u003cp\u003eThis study employed a cross-sectional design, which precludes causal inferences among health anxiety, burnout, and resilience. The observed relationships should therefore be interpreted as associations rather than cause\u0026ndash;effect links. Future research using longitudinal or experimental designs could better clarify the directionality and potential causal mechanisms underlying these relationships.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eHealth Anxiety\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ePsychological Resilience\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBO\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eBurnout\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eEmotional Exhaustion\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eDepersonalization\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003ePersonal Accomplishment\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSEM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eStructural Equation Modeling\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCFA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eConfirmatory Factor Analysis\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAVE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eAverage Variance Extracted\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eComposite Reliability\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eConfidence Interval\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eStandard Deviation\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eStandard Error\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eStandardized Regression Coefficient (Beta)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eχ\u0026sup2;/df\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eChi\u003c/em\u003e\u0026ndash;\u003cem\u003esquare to Degrees of Freedom Ratio\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCFI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eComparative Fit Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTLI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eTucker\u003c/em\u003e\u0026ndash;\u003cem\u003eLewis Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRMSEA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eRoot Mean Square Error of Approximation\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSRMR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eStandardized Root Mean Square Residual\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNFI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eNormed Fit Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIFI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eIncremental Fit Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGFI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eGoodness of Fit Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAGFI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eAdjusted Goodness of Fit Index\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eα\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eCronbach\u0026rsquo;s Alpha (Reliability Coefficient)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ef\u0026sup2;\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eEffect Size (Cohen\u0026rsquo;s f\u003c/em\u003e\u0026ndash;\u003cem\u003esquared)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eCoefficient of Determination\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: AÇ, and İÇ; acquisition of data: AÇ, and İÇ; analysis and interpretation of data: AÇ; drafting of the manuscript: AÇ; critical revision of the manuscript: AÇ, and İÇ; statistical analysis:AÇ; and study supervision: AÇ, and İÇ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\n\u003cp\u003eEthics Approval and Consent to Participate: Ethics committee approval for the study was obtained in accordance with the Declaration of Helsinki, with the decision of the Tokat Gaziosmanpaşa University Graduate Education Institute, dated February 2, 2024, and numbered 394121. Approval was also obtained with the letter of the Tokat Gaziosmanpaşa University Health Research and Application Center, dated February 14, 2024, and numbered 399109.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen Q, Shen S, Liang Y, Kong L, Zhuang S, Li C. Analysis of mental health of healthcare workers and its influencing factors in three consecutive years. Work. 2025;80(3):1296\u0026ndash;303. https://doi.org/10.1177/10519815241289827\u003c/li\u003e\n\u003cli\u003eRuini C, Pira GL, Cordella E, Vescovelli F. Positive mental health, depression and burnout in healthcare workers during the second wave of COVID-19 pandemic. 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The relationship between burnout, stress, and resilience among Vietnamese health care workers. National Journal of Community Medicine. 2024;15(3):215\u0026ndash;26. https://doi.org/10.55489/njcm.150320243557\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Health anxiety, Burnout, Psychological resilience, Mediation, Healthcare professionals, Structural equation modeling","lastPublishedDoi":"10.21203/rs.3.rs-8054758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8054758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBurnout is a pervasive problem among healthcare professionals, particularly in high-stress environments where psychological demands and uncertainty are constant. Health anxiety, intensified by occupational stressors and perceived health risks, can increase vulnerability to burnout. However, psychological resilience may serve as a protective mechanism that buffers the impact of anxiety on occupational well-being. This study aimed to examine the mediating role of psychological resilience in the relationship between health anxiety and burnout among healthcare workers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional, questionnaire-based design was employed. Data were collected from 348 healthcare professionals working in a university hospital in T\u0026uuml;rkiye. Standardized instruments were used to measure health anxiety, psychological resilience, and burnout. The hypothesized structural model was tested using structural equation modeling (SEM) with maximum likelihood estimation and bias-corrected bootstrapping (5,000 resamples). Model fit was evaluated using multiple indices, including χ\u0026sup2;/df, RMSEA, CFI, TLI, and SRMR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe final model demonstrated an acceptable fit to the data (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.83, RMSEA\u0026thinsp;=\u0026thinsp;.090, SRMR\u0026thinsp;=\u0026thinsp;.066, CFI\u0026thinsp;=\u0026thinsp;.89, TLI\u0026thinsp;=\u0026thinsp;.86). Health anxiety significantly and positively predicted burnout (β\u0026thinsp;=\u0026thinsp;.522, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and negatively predicted psychological resilience (β = \u0026ndash;.308, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Psychological resilience negatively predicted burnout (β = \u0026ndash;.210, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The indirect effect of health anxiety on burnout via resilience was significant (βindirect\u0026thinsp;=\u0026thinsp;.065, 95% BC bootstrap CI [.032, .118]), confirming partial mediation. The model explained 38% of the variance in burnout and 9% in psychological resilience.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHealth anxiety increases burnout among healthcare professionals, while psychological resilience serves as a partial buffer that mitigates this effect. These findings underscore the importance of enhancing resilience-focused interventions\u0026mdash;such as stress management, coping skills, and mindfulness-based training\u0026mdash;to protect healthcare workers from the psychological consequences of anxiety and emotional exhaustion. Future research should employ longitudinal designs and cross-cultural samples to validate these relationships and inform evidence-based mental health policies in healthcare institutions. This study highlights the importance of resilience-based interventions for sustaining the psychological well-being of healthcare professionals.\u003c/p\u003e","manuscriptTitle":"The Mediating Role of Psychological Resilience in the Relationship Between Health Anxiety and Burnout Among Healthcare Workers Working in Closed Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 04:33:09","doi":"10.21203/rs.3.rs-8054758/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":"ccf18d9c-e8af-4992-8255-fda3af089273","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T04:33:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 04:33:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8054758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8054758","identity":"rs-8054758","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}