Digital Literacy and Psychological Resilience Alleviate Nurses’ Work Stress: Examining the Moderating Role of Ward Environment

Digital Literacy and Psychological Resilience Alleviate Nurses’ Work Stress: Examining the Moderating Role of Ward Environment | 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 Digital Literacy and Psychological Resilience Alleviate Nurses’ Work Stress: Examining the Moderating Role of Ward Environment Wu Yan, She Wanbin, Peng Qiyan, Li Jile, She Chenghong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7418214/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 Objective Nurses' work stress significantly impacts the quality of nursing care. While digital literacy and psychological resilience serve as potential mitigating factors, their mechanisms of action remain unclear. The moderating effect of ward environments, in particular, requires urgent validation. Methods We employed snowball sampling and a cross-sectional design to recruit 305 in-service nurses from 12 hospitals in Western China. Participants completed standardized questionnaires. Descriptive statistics and Pearson correlation analyses were performed using SPSS version 30.0. Path analysis was conducted using AMOS version 30.0. Additionally, PROCESS macro (version 4.3) was used to test the moderating role of ward environment (specifically, Emergency Room vs. ICU) in the pathway from digital literacy to work stress, assess the mediating effect of psychological resilience, and evaluate the conditional effect of ward environment on this mediation pathway. Results Psychological resilience fully mediated the relationship between digital literacy and nurses' work stress (β = −0.064). Hospital ward context accounted for 48.8% of the variance in nurses' work stress and moderated the pathways involving both digital literacy and psychological resilience. In ICU settings, the protective effect of psychological resilience was attenuated (index value = 0.108), and these units exhibited the highest levels of work stress (F = 126.213, P < 0.001). Conversely, the Emergency Room showed a dual-pathway stress reduction mechanism: a direct effect of digital literacy (β = −0.185) and an indirect effect through psychological resilience (β = −0.131), collectively reducing work stress by 21%. Conclusion The stress-reducing effects of digital literacy and psychological resilience are contingent upon specific ward types. For clinical practice, tailored interventions are recommended: integrate digital technology with resilience training programs in Emergency Rooms; prioritize systemic workflow redesign in ICUs; and enhance resilience-building initiatives in general wards. Digital literacy Psychological resilience Nurses’ work stress Ward environment Moderating effect Figures Figure 1 Figure 2 1. Introduction The WHO (2025) regards occupational stress and burnout among nurses as a public health crisis with global, persistent, and systemic implications. A meta-analysis by Tiina Ilola et al. ( 2024 , p. 98) indicates that mental health disorders (e.g., burnout, anxiety) have become prevalent among nurses. During the COVID-19 pandemic, 36% of clinical nurses in Germany experienced moderate or severe depressive symptoms, with a turnover intention rate of 21.4% (Leif Boß et al., 2025 , p. 105076). In Nepalese ICUs, depressive and anxiety symptoms were reported in 21.2% and 36.5% of nurses, respectively (Parishma Tamrakar, 2023 , p. 1). In the post-pandemic phase, burnout and depressive symptoms affected 48.2% and 64.1% of Chinese nurses (Zhang et al., 2024 ), severely threatening healthcare quality and patient safety. Notably, the proliferation of digital technologies (e.g., electronic health records, remote monitoring) has introduced new challenges, including a "digital overlay" of nursing occupational stress and even overload. As critical personal capabilities, digital literacy and psychological resilience are recognized as vital factors in mitigating nursing occupational stress, which constitutes a core research focus in contemporary nursing science. 2. Theoretical basis and research issues 2.1 Complex Effects of Digital Literacy The connotation of nurses' digital literacy has evolved from basic skills to a multifaceted competency framework encompassing technological application, information critique, and ethical decision-making (Liu et al., 2024 , p. 1139). Recent studies further reveal its complex effects on stress mitigation. A systematic review by Maryam Abbasalizadeh (2024) confirmed that mobile-based digital interventions significantly reduce stress and anxiety among ICU nurses. Similarly, Wu et al. ( 2021 ) found that nurses with high digital literacy access social support more efficiently through digital tools. However, a randomized controlled trial by Lotte Bock (2025) indicated that digital technology overload may introduce additional stressors. This paradox underscores the intricate relationship between nurses' digital literacy and job stress. However, current research remains confined to individual capabilities, inadequately examining how ward contexts influence the boundaries of utility for digital literacy.. 2.2 Ward-Specificity of Psychological Resilience Teng et al. ( 2021 ) demonstrated that psychological resilience partially mediates the relationship between work stress and generalized anxiety. Maryam Abbasalizadeh (2024), using empirical data from Iranian ICU nurses, observed that prolonged exposure among COVID-19 patients enhanced experiential learning and resilience levels; however, high-stress environments had attenuated those protective mechanisms, making targeted resilience training necessary to mitigate long-term psychological impacts. Yet, existing studies have not investigated interaction effects between digital literacy and psychological resilience, limiting explanations of work stress mechanisms in digital healthcare contexts. 2.3 Moderating Role of Ward Environment As a physical-social-psychological milieu, the ward environment functions not merely as a workplace but as a sociotechnical system shaping stress responses. Maryam Abbasalizadeh (2024, p. 442) revealed that structural variations across wards influence nurses' work stress. Li et al. ( 2023 , pp. 385–396) demonstrated that digital competency, as an individual resource, facilitates the conversion of self-efficacy into workplace well-being. Consequently, disparities in digital resource allocation may result in unequal capacities for translating digital literacy into stress alleviation across different wards. A meta-analysis by Qamaria et al. ( 2025 ) further suggested cultivating digital resilience to address emerging risks, highlighting the complexity of contextual factors within wards. Nevertheless, no study has systematically examined how ward environments moderate the digital literacy–psychological resilience–work stress pathway. In summary, despite substantial evidence supporting the independent roles of digital literacy and psychological resilience, most studies examine single variables in isolation. Key gaps include: absence of integrated modeling for "ward environment moderating mediation pathways" (Tiina Ilola et al., 2024 , pp. 97–109); unclear interaction mechanisms between digital literacy and psychological resilience; insufficient reflection of post-pandemic digital transformation in nursing practices (Natalie L. Webster BMus et al., 2020 , pp. 109–120); and disproportionate focus on hospital-level infrastructure over ward-specific contextual factors (Yang et al., 2024 , pp. 120–125). 2.4 Research Questions and Hypotheses Guided by the Conservation of Resources Theory (Hobfoll, 1989 , pp. 513–524) and the Job Demands-Resources Model (Bakker & Demerouti, 2017 , pp. 273–285), this study addresses the aforementioned research gaps using integrated multimodal analyses investigating four questions: (1) Does psychological resilience mediate the relationship between digital literacy and nurses’ work stress? (2) How does ward type (the Emergency Room/ICU) influence the relationship between digital literacy and nurses’ work stress? (3) Does the ICU environment suppress the stress buffering effect of nurses’ psychological resilience? (4) Do direct and indirect stress reduction pathways of digital literacy coexist between nurses in the Emergency Room? Based on these questions, the following hypotheses are proposed: H1: Digital literacy reduces nurses’ work stress through the complete mediation of psychological resilience. H2: Ward type significantly moderates the relationship between digital literacy and nurses’ work stress. H3: ICU wards suppress the protective effect of nurses’ psychological resilience. H4: Both direct and indirect pathways of digital literacy exist among nurses in the Emergency Room (ER). 3. Methods This study employed a cross-sectional questionnaire design with anonymous online data collection to balance convenience and data integrity, recruiting nurses from 12 hospitals in Western China. 3.1. Sample and Sampling Participants comprised registered in-service nurses. Inclusion criteria were: (a) ≥ 1 year of employment, (b) ≥ 30 clinical work hours weekly, and (c) provision of informed consent. Exclusions included interns, trainees, rehired retirees, and nurses on maternity–medical leave or external training during the survey period. To ensure structural equation modeling (SEM) accuracy, a minimum sample size of 200 was required (Kumar & Natarajan, 2020 , p. 21; Werner Reinartz, 2009, p. 333). This threshold was further validated against the rule of exceeding tenfold the total number of scale items (Ghanbar & Rezvani, 2023 , pp. 79–108). Based on these guidelines, we determined that a target sample size of 280 would satisfy both the absolute minimum for SEM and the item-to-participant ratio. Using snowball sampling, 305 nurses were recruited. After excluding invalid responses, 302 valid questionnaires were retained (valid response rate of 99.02%). 3.2. Data Collection Data will be collected from June to July 2025. Researchers designed an online questionnaire via the WeChat mini-program ‘WPS Form’, and generated a QR code for distribution. After explaining the study’s purpose and significance, researchers contacted nurses individually to distribute the QR codes upon obtaining their consent. Researchers required participants to complete all questions prior to submitting the questionnaire through the platform. 3.3. Ethical Considerations The study was reviewed and approved by the Academic Ethics Committee of Leshan Vocational and Technical College (Approval No.: ). At the beginning of the document, a clearly stated informed consent statement highlighted voluntary participation, anonymity, and the right to withdraw without any adverse effects. 3.4. Instruments Hospital Ward Type (HWT) . A researcher-designed demographic questionnaire collected data on gender, education level, age, hospital type, institutional tier, ownership structure, and patient care unit classification. Three categories were identified: Emergency Room, Intensive Care Unit (ICU), and General Medical Ward. Nurses’ Work Stress (NWS) . This construct was operationalized using Yu’s Nurse Stressors Scale (2007), which evaluates stress across five domains: (a) professional role conflicts, (b) temporal workload distribution, (c) environmental resource adequacy, (d) complex patient management demands, and (e) administrative communication efficacy. The 35-item instrument utilizes a 4-point Likert format anchored by Strongly Disagree (1) and Strongly Agree (4), with higher scores indicating greater stress. Cronbach’s α was 0.94 in the original study (Yu, 2007 , pp. 2090–2093) and 0.911 in this study. Nurses’ Digital Literacy (NDL). Assessment employed Liu et al.’s Digital Competence Assessment Tool (2025), encompassing five facets: (a) technological awareness, (b) information synthesis capacity, (c) virtual collaboration proficiency, (d) innovative application skills, and (e) cybersecurity stewardship. Respondents rated item relevance on a 5-point Likert scale from Strongly Disagree (1) to Strongly Agree (5), where higher total scores signify superior self-perceived digital competence. Cronbach’s α was 0.941 in the original study (Liu et al., 2025 , pp. 112–115) and 0.825 in this study. Nurses’ Psychological Resilience (NPR) . Lin et al.’s Psychological Resilience Inventory for Nurses (2020) served as the measurement basis, capturing three subdomains: (a) contextual adaptation to clinical environments, (b) individual coping mechanisms, and (c) collaborative system rebuilding after adversity. This 34-item survey uses a 5-point response ladder from Strongly Disagree (1) to Strongly Agree (5), with cumulative scores reflecting enhanced psychological flexibility. Cronbach’s α was 0.928 in the original study (Lin et al., 2020 , pp. 44–47) and 0.909 in this study. 3.5 Data Analysis IBM SPSS Statistics 30.0 was utilized for conducting all statistical analyses. Descriptive statistics, including t-tests and one-way ANOVA, were employed to summarize the demographic characteristics of participants, while Pearson correlation analysis investigated associations between digital literacy, psychological resilience, and work stress levels among nurses across different ward types. To rigorously validate the theoretical framework under study using a methodological triangulation approach, structural equation modeling (SEM) executed via IBM SPSS AMOS 30.0 tested path relationships among latent variables—specifically, ward type, digital literacy, psychological resilience, and work stress. Concurrently, the PROCESS macro (version 4.3) with 10,000 bootstrap samples explored nuanced effects: Model 1 assessed the moderating role of ward type on the relationship between digital literacy and work stress; Model 4 examined the mediating effect of psychological resilience in this context; Model 7 investigated a dual-path mechanism specific to Emergency Rooms (ERs); and Model 58 evaluated the moderating impact of Intensive Care Unit (ICU) settings on the protective function of psychological resilience. 3.6 Data Bias Verification and Analytical Reliability 3.6.1 Data Preprocessing and Quality Control Z-score standardization was applied to address scale differences across variables (work stress measured on a 4-point Likert scale; resilience and digital literacy assessed using a 5-point Likert scale) (Collins et al., 2015 , p. 735). Winsorization at the 1st and 99th percentiles bilaterally reduced extreme outliers, resulting in improved distributional properties: for digital literacy, kurtosis decreased from − 0.24 to − 0.878 (standard deviation changed from 0.50 to 0.67); for work stress, skewness shifted from − 0.09 to − 0.14 (standard deviation increased from 0.63 to 0.71). All variables satisfied normality assumptions based on established thresholds: absolute skewness values were below 0.44 (standard error = 0.14), absolute kurtosis values were below 0.88 (standard error = 0.28), and the ratio of absolute skewness to absolute kurtosis remained below 2 (David L.Streiner, 2024 , p. 466). 3.6.2 Model Fit Assessment and Reliability Verification Structural equation modeling (SEM) was performed using AMOS version 26.0 with maximum likelihood estimation. Fit indices (Table 1 ) indicated good model-data alignment: χ²/df = 1.973 (less than 3), RMSEA = 0.057 (90%CI [0.044, 0.070]), GFI = 0.940 (exceeding the benchmark of 0.90). CFI = 0.977, NFI = 0.954, IFI = 0.977 (all surpassed 0.95) confirmed superiority over the null model. PNFI = 0.709 and PCFI = 0.726 (both exceeded 0.50) supported parsimony. Overall fit statistics met stringent criteria outlined by Kline ( 2018 , p. 188), thereby justifying subsequent path analysis. Multiple linear regression modeled work stress (expressed as a z-score) as a function of resilience, digital literacy (each converted to z-scores), and ward type. With a sample size of N = 302—sufficient to meet the rule of thumb requiring at least 50 participants plus eight times the number of predictors (here, three predictors)—statistical power exceeded 99% for detecting an effect size corresponding to R²=0.29 at α = 0.05. The final model explained 29.4% of variance in work stress (R² = 0.294), with an adjusted R² of 0.287 accounting for model complexity. The F-statistic for the overall regression was significant at F(3, 298) = 41.464, p < 0.001, indicating that 28.7% of variability in work stress could be explained by the included predictors. Table 1 Structural Equation Modeling Fit Indices Fit Indices Evaluation Criteria Model Values Goodness-of-Fit Assessment χ²/df < 3 1.973 Good RMSEA 0.90 0.940 Good CFI > 0.95 0.977 Excellent NFI > 0.90 0.954 Good IFI > 0.95 0.977 Excellent TLI > 0.95 0.969 Good PNFI > 0.50 0.709 Good PCFI > 0.50 0.726 Good AIC < Saturated Model 237.922 (Saturated Model: 240.000) Excellent Note : Reference standards based on (Hu & Bentler, 1999 ) and Kline ( 2018 ). 3.6.3 Collinearity and Residual Diagnostics All variance inflation factors (VIF) were < 1.32 (ward type = 1.14, digital literacy = 1.24, psychological resilience = 1.32), well below the threshold of 5. The maximum condition index was 5.37, which is less than 30, and no single dimension accounted for multiple variables (ward type explained 97% of the variance in dimension 4), indicating that there was no multicollinearity issue. Standardized residuals (SRs) had a mean of 0 (SD = 0.995), with 95% of the values falling within the range of [− 1.95, 1.95]. Three cases had |SR| >2.5, (max |SR| = 2.705), but Cook’s distance values (max D = 0.12 < 1) indicated no influential cases. The residual-predicted value scatterplot demonstrated random scattering (Fig. 1 ), and no heteroscedasticity was observed. 4. Results 4.1 Descriptive Analysis of Variables Participants were predominantly aged 31–40 years (44.04%) and female (96.69%). Nurses’ Digital Literacy (NDL) scores differed significantly by age and ward type (Table 2 ): nurses aged 31–40 years had higher NDL scores (F = 6.08, p < 0.01), while those working in general wards also exhibited elevated levels (F = 9.04, p < 0.001). Both work stress and psychological resilience varied markedly across ward types, with ICU nurses reporting significantly greater work stress (F = 126.21, p < 0.001) and enhanced psychological resilience (F = 31.03, p < 0.001). Table 2 Differences in Digital Literacy Across Nurse Demographics (N = 302) Characteristics Categories N (%) NDL NWS NPR Mean ± SD t or F Mean ± SD t or F Mean ± SD t or F Age (years) ≤ 30 94(31.13) 4.24 ± 0.48 6.079** 2.55 ± 0.66 2.837 3.97 ± 0.63 2.655 31–40 133(44.04) 4.40 ± 0.48 2.71 ± 0.65 4.04 ± 0.56 ≥ 41 75(24.83) 4.18 ± 0.52 2.53 ± 0.55 3.84 ± 0.52 Gender Male 10(3.31) 4.32 ± 0.45 −0.153 2.83 ± 0.69 −1.118 4.05 ± 0.61 −0.474 Female 292(96.69) 4.30 ± 0.50 2.61 ± 0.63 3.97 ± 0.58 Hospital Ward Type ER 112(37.09) 4.18 ± 0.46 9.036*** 2.83 ± 0.43 126.213*** 3.66 ± 0.43 31.029*** ICU 110(36.42) 4.28 ± 0.52 2.91 ± 0.53 4.18 ± 0.58 GW 80(26.49) 4.48 ± 0.47 1.90 ± 0.42 4.11 ± 0.57 Note : ER = Emergency Room; ICU = Intensive Care Unit; GW = General Ward;***p < 0.001;**p < 0.01༛*p < 0.05༛ 4.2 Structural Relationships and Direct Effects 4.2.1 Intervariable Correlations Table 3 showed strong correlation coefficients between Hospital Ward Type (HWT) and Nurses’ Work Stress (NWS) (η = 0.668, p < .001). This finding was further supported by a significant negative path from HWT to NWS (β = −0.488) in the structural equation modeling (SEM) results presented in Table 4 , indicating significantly higher stress levels among nurses working in high-risk wards (such as ICU and Emergency Room) compared to those in general wards. Additionally, Table 3 revealed significant negative correlations between Nurses’ Psychological Resilience (NPR) and NWS (r = − 0.214, p < .001), as well as between NPR and HWT (η = 0.407, p < .001). However, the direct path from NPR to NWS in the SEM model was nonsignificant (β = −0.117, p = .211; Table 4 ), suggesting that the relationship might be mediated or suppressed by HWT. Nurses’ Digital Literacy (NDL) demonstrated weak negative correlations with both NWS (r = − 0.201, p < .001) and HWT (η = 0.236, p < .001) according to Table 3 . Despite these bivariate associations, no significant direct effect of NDL on NWS emerged in the SEM analysis (β = 0.065, p = .601; Table 4 ), implying potential indirect effects through other variables like resilience. Notably, NDL and NPR were positively correlated (r = 0.431, p < .001; Table 3 ), which aligned with the significant direct path from NPR to NDL observed in the SEM results (β = 0.061, p < .001; Table 4 ). These findings collectively support a bidirectional relationship between NDL and NPR. Table 3 Correlations Among Four Variables (N = 302) 1 2 3 4 NDL 1 NPR 0.431*** 1 NWS −0.201*** −0.214*** 1 HWT 0.236*** 0.407*** 0.668*** 1 Note : ***p < 0.001;**p < 0.01༛*p < 0.05. 4.2.2 Direct Effect Analysis Table 4 demonstrated a significant negative direct effect of Hospital Ward Type (HWT) on Nurses’ Work Stress (NWS) (β = -0.488, p = 0.006), indicating that ward environments maintain independent predictive validity for stress levels after adjusting for Nurses’ Psychological Resilience (NPR) and Nurses’ Digital Literacy (NDL). Notably, specific high-acuity settings such as intensive care units (ICUs) were associated with elevated work stress burden. Neither Nurses’ Psychological Resilience (NPR→NWS: β = -0.117, p = 0.211) nor Nurses’ Digital Literacy (NDL→NWS: β = 0.065, p = 0.601) exhibited statistically significant direct effects, suggesting no immediate buffering impact on stress. Instead, their influence appears to operate indirectly via mediating variables such as ward characteristics. A significant positive association emerged between HWT and NPR (β = 0.077, p < 0.001), identifying HWT as a critical contextual factor shaping resilience capacity. Furthermore, NDL and NPR showed moderate positive correlation (r = 0.061), implying that enhanced digital competence may promote perceived control thereby reinforcing adaptive coping mechanisms (see Fig. 2 ). Table 4 Standardized Estimates for SEM Paths and Intervariable Relationships Path Β / Cov S.E. C.R. p Sig. NWS <--- NPR −.117 .094 −1.250 .211 ns NWS <--- NDL .065 .125 .523 .601 ns NWS <--- HWT −.488 .178 −2.740 .006 ** NDL HWT .015 .006 2.483 .013 * NPR HWT .077 .017 4.465 < 0.001 *** NDL NPR .061 .015 4.223 < 0.001 *** Note: All estimates are standardized; ***p* < .001; **p* < .01; *p* < .05; ns = nonsignificant; NWS = work stress; NPR = psychological resilience; NDL = digital literacy; HWT = Hospital Ward Type. 4.3 Mediation and Moderation Effects in Variable Pathways 4.3.1 Psychological Resilience Mediation Mechanism (H1) The results of Model 4 (as presented in Table 5 ) showed that Nurses’ Digital Literacy (NDL) significantly enhanced Nurses’ Psychological Resilience (NPR) (β = 0.420, p < .001), accounting for 30.55% of the variance (F(3, 298) = 43.69, p < .001). Furthermore, NPR significantly reduced Nurses’ Work Stress (NWS) (β = −0.152, p = .002), with a significant indirect effect observed (β = −0.064, 95% CI [− 0.109, − 0.022]); importantly, the 95% bootstrap confidence interval did not include zero. The direct effect of NDL on NWS was nonsignificant (β = −0.005, p = .922). These findings confirm that NDL indirectly reduces NWS through its effect on enhancing NPR. Thus, Hypothesis 1 was supported. Table 5 Decomposition of Nurses’ Psychological Resilience (NPR) Mediation Effects Path β SE t p 95% CI/BootCI NDL→NPR 0.420 0.054 7.741 < 0.001 [0.313, 0.527] NPR→NWS -0.152 0.049 -3.111 0.002 [-0.249, -0.056] Indirect Effect -0.064 0.022 - - [-0.109, -0.022]* Direct Effect -0.005 0.050 -0.098 0.922 [-0.104, 0.094] Note: Based on 10,000 bootstrap samples; effects are significant if 95% CI excludes zero. Model fit: NPR model: R² = 0.306, F(3, 298) = 43.69, p < 0.001; NWS model: R² = 0.467, F(4, 297) = 65.08, p < 0.001. 4.3.2 Moderating Effect of Ward Type on Digital Literacy–Work Stress Relationship (H2) Model 1 analysis (Table 6 ) demonstrated that for nurses in emergency rooms, every 1-unit increase in Nurses’ Digital Literacy (NDL) reduced Nurses’ Work Stress (NWS) by 0.185 units (β = −0.185, p = 0.025). However, no significant association was observed in non-emergency wards (β = −0.015, p = 0.787). This indicates that NDL alleviates NWS among emergency room nurses. Additionally, ICU nurses had 1.094 units higher work stress than non-ICU nurses (β = 1.094, p < 0.001), supporting H2. Table 6 Hospital Ward Type (HWT) Moderation Analysis Results Path Conditional β SE t p 95% CI NDL→NWS Non-ER -0.015 0.056 -0.271 0.786 [-0.125, 0.095] NDL→NWS ER -0.185 0.083 -2.246 0.025 [-0.348, -0.023]* Dept_ICU→NWS - 1.094 0.078 13.979 < 0.001 [0.940, 1.248] Note: Based on 10,000 bootstrap samples; effects are significant if 95% CI excludes zero. Model fit: R²=0.455, F(4,297) = 62.00,p < 0.001;ER = Emergency Room; Non-ER = Not Emergency Room. 4.3.3 Moderating Effect of ICU on Resilience’s Protective Role (H3) Model 58 analysis (Table 7 ) revealed differential effects through simple slope examination: Nurses’ Psychological Resilience (NPR) significantly reduced occupational stress in non-ICU wards (β = −0.274, p < 0.001), yet this protective effect was completely nullified in ICU settings (β = 0.018, p = 0.796). Moderation analysis confirmed a significant positive moderating effect of ICU work environments on the NPR–NWS relationship (interaction term: β = 0.293, p = 0.001), with a statistically significant Moderated Mediation Index (Index = 0.108, 95% CI [0.006, 0.221]). These findings demonstrate that ICU settings suppress the stress-buffering effect of NPR, thereby providing empirical support for Hypothesis 3. Table 7 ICU Moderation Analysis Results Effect Type Non-ICU ICU Moderating Effect NPR→NWS β=-0.274 β = 0.0183 Interaction Term β = 0.293** Conditional Indirect Effect -0.098 0.009 Moderation Index = 0.108 95%BootCI [-0.159, -0.050]* [-0.078, 0.110] [0.006, 0.221]* Note:* Bootstrap = 10,000 samples; effects significant if 95% CI excludes zero; NPR×Dept_ICU interaction: β = 0.293, SE = 0.0889, t = 3.290, p = 0.001; Model fit: NPR model: R²=0.309, F(4,297) = 33.27, p < 0.001; NWS model: R²=0.486, F(5,296) = 55.95, p < 0.001. 4.3.4 Dual-Pathway Stress Reduction Mechanism of Digital Literacy in Emergency Room Nurses (H4) Integrated results from Models 1 and 7 (Table 8 ) revealed that in emergency rooms: Nurses’ Digital Literacy (NDL) directly reduced Nurses’ Work Stress (NWS) (β = −0.185, p = 0.025). A significant indirect pathway through Nurses’ Psychological Resilience (NPR) emerged (β = −0.131, 95% BOOT CI [− 0.200, − 0.071]). These dual pathways jointly accounted for 52.1% of the total effect on stress among the emergency nurses. The total effect (β = −0.316, p < 0.001) corresponded to a 21% reduction in stress levels attributable to NDL. This finding demonstrates dual mechanisms underlying stress reduction—direct effects and resilience-mediated pathways—thereby supporting hypothesis H4. Table 8 Emergency Room Dual-Pathway Effect Decomposition Path Effect Type β SE p 95% CI/BootCI Source of data Direct Path: NDL→NWS -0.185 0.083 0.025 [-0.348, -0.023] Model 1 Indirect Path: NDL→NPR→NWS -0.131 0.033 - [-0.200, -0.071]* Model 7 Total Effect -0.316 - - - Calculated value Note : * Bootstrap = 10,000 samples, effects significant if 95% CI excludes zero; Indirect effect calculation: NDL→NPR|ER: β = 0.405 (Model 7), NPR→NWS|ER: β=-0.289 (Model 7), Product: 0.405 × (-0.289) = -0.131. 5. Discussion 5.1 Digital Literacy Reduces Stress via Full Mediation of Psychological Resilience This study revealed no direct effect of Nurses’ Digital Literacy (NDL) on Nurses’ Work Stress (NWS) but demonstrated its complete indirect reduction of stress through enhanced Nurses’ Psychological Resilience (NPR), with a 100% mediation effect. This supports the "technological competence–psychosocial resources" transformation framework proposed by Fornés-Vives et al. ( 2016 , pp. 318–323), wherein digital tools must be translated into psychological capital to alleviate occupational stress. Consistent with Ou Yang ( 2020 , pp. 98–99), nurses' digital literacy requires self-efficacy mediation to influence work adaptation. It also aligns with findings by Lotte Bock (2025) and Li et al. ( 2023 , pp. 385–396), showing that digital training can yield measurable emotional relief over brief periods, thereby enhancing workplace well-being. Critically, our results confirm psychological resilience as the primary pathway for digital literacy's stress reduction in nursing. This indicates that integrating resilience-building interventions into digital technology training offers a viable practical measure to mitigate nurse stress. 5.2 Ward Type Moderates the Digital Literacy–Stress Relationship Our study identified ward type as a core contextual factor, accounting for 48.8% of the variance in Nurses’ Work Stress (NWS) while moderating the relationships between Nurses’ Digital Literacy (NDL) and Nurses’ Psychological Resilience (NPR). In emergency rooms, each 1-unit increase in NDL was associated with a reduction in NWS by 0.185 units; however, no significant direct association was observed in non-emergency wards. The ICU environment was associated with significantly higher stress levels—nurses in ICU settings reported stress levels that were 1.094 units higher than those in non-ICU settings. These findings support prior research: For instance, the direct stress-reducing effect of emergency rooms aligns with findings from Dee McGonigle and Mastrian (2022, pp. 201–230) and Maryam Montazeri (2021, p. 26402), where digital systems (e.g., e-triage tools) optimize patient flow, which directly reduces time pressure. Furthermore, the main effect of elevated stress in ICUs is consistent with Daniel R. Schweitzer’s work (2023, pp. 1076–1080), who attributed this to the demanding nature of ICU care depleting personal resources such as self-control and decision-making capacity, leading to decision fatigue. The moderating role of organizational contexts echoes Xu and Zhao’s observations (2024, p. 1392811). Crucially, the ward environment determines whether the instrumental utility of NDL can be leveraged: Specifically, in emergency settings, digital tools enable real-time dynamic clinical decisions; in general wards, realizing digital value requires transformation through nurse resilience; whereas in ICUs, complex humanistic care demands resist being replaced by digital tools, necessitating systematic policy interventions. 5.3 ICU Environment Suppresses Resilience’s Protective Effect Resilience significantly reduced Nurses’ Work Stress (NWS) in non-ICU wards but was fully offset in ICUs, with a significantly moderated mediating effect (Index = 0.108). These globally relevant findings indicate that chronic exposure to ICUs' high-pressure, traumatic environments may erode Nurses’ Psychological Resilience (NPR). When demands exceed critical thresholds, NPR loses its buffering capacity (Angela M Kunzler, 2022, p. 104312; Du, 2019 , p. 4059; Mealer et al., 2012 , pp. 292–299), supporting Hobfoll’s ( 1989 , pp. 513–524) Conservation of Resources Theory. Notably, our results contrast with those reported by Salluh et al. ( 2022 , p. 37), who observed persistent resilience effects in ICUs. This divergence underscores that the high-intensity and high-stress context of ICUs—characterized by frequent emergencies and complexity—therefore requires structured multilevel support systems rather than relying solely on individual resilience. 5.4 Dual-Pathway Stress Reduction Mechanism in Emergency Rooms Our results showed that the direct path (Nurses’ Digital Literacy → Nurses’ Work Stress) contributes to 12.3% stress reduction, while the indirect path (NDL → Nurses’ Psychological Resilience (NPR) → NWS) accounts for 8.7%. The combined dual-pathway effects explain 52.1% of the variance in stress levels, resulting in 21% overall stress reduction. This confirms that digital literacy serves as a contextually adaptive resource, yielding dual effects through both direct (technological empowerment) and indirect (psychological empowerment) pathways. The dual-pathway mechanism validates the Job Demands-Resources Model (Bakker & Demerouti, 2017 , pp. 273–285), extending Erkan Boğa ( 2024 , p. e38933) on how emergency experience enhances digital and health literacy. It also aligns with Wilson’s advocacy (2024, pp. 110–111) for implementing digital solutions—such as mobile apps—to improve nurses’ mental health. Notably, integrating digital training with resilience-building interventions offers a practical stress-reduction strategy for emergency rooms. 6. Conclusion This study demonstrates that while digital literacy and psychological resilience significantly reduce nurses’ work stress, ward environments exert complex moderating effects: In emergency rooms, stress reduction occurs via dual pathways (direct and indirect); in non-ICU wards, resilience primarily mediates this relationship; whereas in ICU settings, the protective role of resilience becomes entirely suppressed. As a key contextual factor, ward type independently accounts for 48.8% of the variance in work stress. Clinical recommendations for differentiated interventions include: Low-risk wards (e.g., general units): Enhance resilience through mindfulness training and structured peer support groups. Medium-risk wards (e.g., emergency rooms): Deploy intelligent decision terminals, optimize electronic health record systems, implement micro-learning modules for digital literacy, such as Nursing Digital Badges (Alberto Lana et al., 2024, p. 71), and facilitate dual-pathway stress reduction through targeted resilience training. High-risk wards (e.g., ICUs): Undertake systemic redesign encompassing workflow reorganization, patient assignment optimization, and critical patient tiering protocols, supplemented by post-traumatic growth training for staff members and crisis simulation exercises to restore depleted resilience capacity. 7. Limitations and Future Research The cross-sectional design limits causal inference, and self-reported data are susceptible to common-method bias. Therefore, future studies should employ: longitudinal tracking; objective physiological measures; randomized controlled trials—to validate interventions and explore long-term dynamics across ward contexts, digital literacy, resilience, and work stress. Abbreviations HWT Hospital Ward Type NWS Nurses’ Work Stress NDL Nurses’ Digital Literacy NPR Nurses’ Psychological Resilience ICU Intensive Care Unit ER Emergency Room GW General Ward SEM Structural Equation Modeling RMSEA Root Mean Square Error of Approximation GFI Goodness-of-Fit Index CFI Comparative Fit Index NFI Normed Fit Index IFI Incremental Fit Index TLI Tucker-Lewis Index PNFI Parsimonious Normed Fit Index PCFI Parsimonious Comparative Fit Index AIC Akaike Information Criterion VIF Variance Inflation Factor SR Standardized Residual SD Standard Deviation SE Standard Estimate Declarations Acknowledgments This work was supported by the Sichuan Province High-level Social Science Research Team Development Program of Leshan Normal University, China. We would like to thank nurses who participated in the study. Authors' contributions This manuscript was jointly completed by Yan Wu (Y.W.), Wanbin She (W.S.), Qiyan Peng (Q.P.), Jile Li (J.L.), and Chenghong She (C.S.). The authors confirm that: a) The order of co-first authorship reflects relative leadership in research design, conceptualization, and data collection (Yan Wu), while methodology, manuscript writing, and theoretical innovation were led by Wanbin She. b) Q.P., J.L.and C.S. Conceptualization, methodology, interpretation of data, writing - review & editing. c) All authors have read and approved the final version of the manuscript. Funding This research has been funded by the Sichuan Provincial Education and Teaching Reform Project (JG2024-1036) and the Leshan Normal University Scientific Research Start-up Project for Introducing High-level Talents (RC2025041). Availability of data All data generated and analyzed are presented in the article. Upon request, the corresponding author will provide access to the datasets supporting these findings; however, they cannot be made publicly available due to privacy and ethical constraints. Ethics approval and consent to participate The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Academic Ethics Committee of the Leshan Vocational and Technical College (Approval No.: LVTCERC202502; date of approval: 25 May 2025). Consent for publication Not applicable. Informed consent Written consent has been obtained from the participants. Competing Interests The authors declare that there are no competing interests in the publication of this research. References Alberto Lana, Beatriz Sánchez-García, María González-García, Ana Fernández-Feito, & David González-Pando. (2024). Impact of Nursing Professional Values on Depression, Stress, and Anxiety among Nurses during the COVID-19 Pandemic. Journal of Nursing Management , Vol.2024 (No.1), 1-144. https://doi.org/10.1155/2024/5199508 Angela M Kunzler, A. C., Nikolaus Röthke, Marlene Staginnus, Sarah K Schäfer, Jutta Stoffers-Winterling, Klaus Lieb. (2022). 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(2024). The effect of resilience training with mHealth application based on micro-learning method on the stress and anxiety of nurses working in intensive care units: a randomized controlled trial. BMC medical education , Vol.24 (No.1), 442. https://doi.org/10.1186/s12909-024-05427-w Maryam Montazeri, J. M., Claire Novorol, Shubhanan Upadhyay, Paul Wicks, Stephen Gilbert. (2021). Optimization of Patient Flow in Urgent Care Centers Using a Digital Tool for Recording Patient Symptoms and History: Simulation Study. JMIR formative research , Vol.5 (No.5), e26402. https://doi.org/10.2196/26402 Mealer, M. D. o. P. S., Critical Care Medicine, D. o. M., University of Colorado School of Medicine, Aurora, CO, US, [email protected] , Jones, J. C. o. N., University of Colorado, Aurora, CO, US, Newman, J. D. o. P. S., Critical Care Medicine, D. o. M., University of Colorado School of Medicine, Aurora, CO, US, McFann, K. K. D. o. P. M., Biometrics, U. o. C. D., Aurora, CO, US, Rothbaum, B. D. o. P., Emory University School of Medicine, Atlanta, GA, US, Moss, M. D. o. P. S., & Critical Care Medicine, D. o. M., University of Colorado School of Medicine, Aurora, CO, US. (2012). The presence of resilience is associated with a healthier psychological profile in intensive care unit (ICU) nurses: Results of a national survey. International Journal of Nursing Studies , Vol.49 (No.3), 292-299. https://doi.org/10.1016/j.ijnurstu.2011.09.015 Natalie L. Webster BMus , P., MSc, Jan R. Oyebode BA , M. P., PhD, Catharine Jenkins BA , M., RMN, MSc, S. B., & Analisa Smythe MPhil, M., RMN. (2020). Using technology to support the emotional and social well?being of nurses: A scoping review. Journal of Advanced Nursing , Vol.76 (No.1), 109-120. https://doi.org/10.1111/jan.14232 Ou Yang, J. (2020). The mediating role of self-efficacy between team trust support and work adjustment among ICU nurses. Electronic Journal of Practical ynecological Endocrinology , Vol.7 (35), 98-99. https://doi.org/10.16484/j.cnki.issn2095-8803.2020.35.070 Parishma Tamrakar, S. B. P., Subhash Prasad Acharya. (2023). Anxiety and depression among nurses in COVID and non-COVID intensive care units. Nursing in critical care , Vol.28 (No.2), 272-280. https://doi.org/10.1111/nicc.12685 Qamaria, R. S., Kuswandi, D., Setiyowati, N., & Bahodirovna, A. M. (2025). Digital resilience in adolescence: A systematic review of models, methods and theoretical perspectives. Multidisciplinary Reviews , Vol.8 (No.9). https://doi.org/10.31893/multirev.2025287 Salluh, J. I. F., Kurtz, P., Bastos, L. S. L., Quintairos, A., Zampieri, F. G., & Bozza, F. A. (2022). The resilient intensive care unit. Annals of Intensive Care , 12 (1), 37. https://doi.org/10.1186/s13613-022-01011-x Teng, Y., Li, S., Shang, M., Liu, D., & Wang, F. (2021). The mediating effect of psychological resilience between work stress and generalized anxiety disorder in nursing staff. Journal of Jining Medical University , Vol.44 (6), 403-406. https://doi.org/10.3969/j.issn.1000-9760.2021.06.006 Tiina Ilola, Mikael Malmisalo, Elina Laukka, Heli Lehtiniemi, Tarja Pölkki, Maria Kääriäinen, Hong-Gu He, & Outi Kanste. (2024). The effectiveness of digital solutions in improving nurses' and healthcare professionals' mental well-being: a systematic review and meta-analysis. Journal of research in nursing : JRN , Vol.29 (No.2), 97-109. https://doi.org/10.1177/17449871241226914 Werner Reinartz, M. H., Jörg Henseler. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing , Vol.26 (No.4), 332-344. https://doi.org/10.1016/j.ijresmar.2009.08.001 WHO, W. H. O. (2025). State of the world's nursing report 2025: investing in education, jobs, leadership and service delivery. 1-166. https://www.who.int/publications/i/item/9789240110236 Wilson, J. (2024). Commentary: The effectiveness of digital solutions in improving nurses' and healthcare professionals' mental well-being: a systematic review and meta-analysis. Journal of research in nursing : JRN , Vol.29 (No.2), 110-111. https://doi.org/10.1177/17449871241232966 Wu, C., Du, Y., & He, S. (2021). Study on the Relationship Model among Self-Efficacy,Social Support and Information Literacy of Clinical Nurses. Chinese Health Quality Management , Vol.28 (11), 58-63. https://doi.org/10.13912/j.cnki.chqm.2021.28.11.14 Xu, H., & Zhao, X. (2024). Organizational support enhances nurses' work-family enrichment: a person-context interactionist perspective. Frontiers in psychiatry , Vol.15 , 1392811. https://doi.org/10.3389/fpsyt.2024.1392811 Yang, M., Cheng, S., Liu, Y., Zhu, Y., Weng, F., & Tian, J. (2024). Construction and Analysis of Large-Scale Image Equipment Resource Allocation Model Based on Real World Data. China Medical Devices , Vol.39 (10), 120-125. Yu, H. (2007). Analysis on reliability and validity of Chinese nurses stressor scale. Chinese Nursing Research , Vol.21 (No.8), 2090-2093. Zhang, Y., Wu, C., Ma, J., Liu, F., Shen, C., Sun, J., Ma, Z., Hu, W., & Lang, H. (2024). Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis. BMC nursing , Vol.23 (No.1), 224. https://doi.org/10.1186/s12912-024-01867-3 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.xlsx Supplementarymaterial2.amosoutput Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7418214","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507720885,"identity":"5ac60490-2a19-4786-924f-f291cf5b165b","order_by":0,"name":"Wu Yan","email":"","orcid":"","institution":"Leshan Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Yan","suffix":""},{"id":507720886,"identity":"96f8b20f-35f6-4fe2-a72e-2923e13bc4ba","order_by":1,"name":"She 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Introduction","content":"\u003cp\u003eThe WHO (2025) regards occupational stress and burnout among nurses as a public health crisis with global, persistent, and systemic implications. A meta-analysis by Tiina Ilola et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p. 98) indicates that mental health disorders (e.g., burnout, anxiety) have become prevalent among nurses. During the COVID-19 pandemic, 36% of clinical nurses in Germany experienced moderate or severe depressive symptoms, with a turnover intention rate of 21.4% (Leif Bo\u0026szlig; et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, p. 105076). In Nepalese ICUs, depressive and anxiety symptoms were reported in 21.2% and 36.5% of nurses, respectively (Parishma Tamrakar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, p. 1). In the post-pandemic phase, burnout and depressive symptoms affected 48.2% and 64.1% of Chinese nurses (Zhang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), severely threatening healthcare quality and patient safety. Notably, the proliferation of digital technologies (e.g., electronic health records, remote monitoring) has introduced new challenges, including a \"digital overlay\" of nursing occupational stress and even overload. As critical personal capabilities, digital literacy and psychological resilience are recognized as vital factors in mitigating nursing occupational stress, which constitutes a core research focus in contemporary nursing science.\u003c/p\u003e"},{"header":"2. Theoretical basis and research issues","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Complex Effects of Digital Literacy\u003c/h2\u003e\u003cp\u003eThe connotation of nurses' digital literacy has evolved from basic skills to a multifaceted competency framework encompassing technological application, information critique, and ethical decision-making (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p. 1139). Recent studies further reveal its complex effects on stress mitigation. A systematic review by Maryam Abbasalizadeh (2024) confirmed that mobile-based digital interventions significantly reduce stress and anxiety among ICU nurses. Similarly, Wu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that nurses with high digital literacy access social support more efficiently through digital tools. However, a randomized controlled trial by Lotte Bock (2025) indicated that digital technology overload may introduce additional stressors. This paradox underscores the intricate relationship between nurses' digital literacy and job stress. However, current research remains confined to individual capabilities, inadequately examining how ward contexts influence the boundaries of utility for digital literacy..\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ward-Specificity of Psychological Resilience\u003c/h2\u003e\u003cp\u003eTeng et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that psychological resilience partially mediates the relationship between work stress and generalized anxiety. Maryam Abbasalizadeh (2024), using empirical data from Iranian ICU nurses, observed that prolonged exposure among COVID-19 patients enhanced experiential learning and resilience levels; however, high-stress environments had attenuated those protective mechanisms, making targeted resilience training necessary to mitigate long-term psychological impacts. Yet, existing studies have not investigated interaction effects between digital literacy and psychological resilience, limiting explanations of work stress mechanisms in digital healthcare contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Moderating Role of Ward Environment\u003c/h2\u003e\u003cp\u003eAs a physical-social-psychological milieu, the ward environment functions not merely as a workplace but as a sociotechnical system shaping stress responses. Maryam Abbasalizadeh (2024, p. 442) revealed that structural variations across wards influence nurses' work stress. Li et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, pp. 385\u0026ndash;396) demonstrated that digital competency, as an individual resource, facilitates the conversion of self-efficacy into workplace well-being. Consequently, disparities in digital resource allocation may result in unequal capacities for translating digital literacy into stress alleviation across different wards. A meta-analysis by Qamaria et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) further suggested cultivating digital resilience to address emerging risks, highlighting the complexity of contextual factors within wards. Nevertheless, no study has systematically examined how ward environments moderate the digital literacy\u0026ndash;psychological resilience\u0026ndash;work stress pathway.\u003c/p\u003e\u003cp\u003eIn summary, despite substantial evidence supporting the independent roles of digital literacy and psychological resilience, most studies examine single variables in isolation. Key gaps include: absence of integrated modeling for \"ward environment moderating mediation pathways\" (Tiina Ilola et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, pp. 97\u0026ndash;109); unclear interaction mechanisms between digital literacy and psychological resilience; insufficient reflection of post-pandemic digital transformation in nursing practices (Natalie L. Webster BMus et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, pp. 109\u0026ndash;120); and disproportionate focus on hospital-level infrastructure over ward-specific contextual factors (Yang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, pp. 120\u0026ndash;125).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Research Questions and Hypotheses\u003c/h2\u003e\u003cp\u003eGuided by the Conservation of Resources Theory (Hobfoll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, pp. 513\u0026ndash;524) and the Job Demands-Resources Model (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, pp. 273\u0026ndash;285), this study addresses the aforementioned research gaps using integrated multimodal analyses investigating four questions:\u003c/p\u003e\u003cp\u003e(1) Does psychological resilience mediate the relationship between digital literacy and nurses\u0026rsquo; work stress?\u003c/p\u003e\u003cp\u003e(2) How does ward type (the Emergency Room/ICU) influence the relationship between digital literacy and nurses\u0026rsquo; work stress?\u003c/p\u003e\u003cp\u003e(3) Does the ICU environment suppress the stress buffering effect of nurses\u0026rsquo; psychological resilience?\u003c/p\u003e\u003cp\u003e(4) Do direct and indirect stress reduction pathways of digital literacy coexist between nurses in the Emergency Room?\u003c/p\u003e\u003cp\u003eBased on these questions, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eH1: Digital literacy reduces nurses\u0026rsquo; work stress through the complete mediation of psychological resilience.\u003c/p\u003e\u003cp\u003eH2: Ward type significantly moderates the relationship between digital literacy and nurses\u0026rsquo; work stress.\u003c/p\u003e\u003cp\u003eH3: ICU wards suppress the protective effect of nurses\u0026rsquo; psychological resilience.\u003c/p\u003e\u003cp\u003eH4: Both direct and indirect pathways of digital literacy exist among nurses in the Emergency Room (ER).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cp\u003eThis study employed a cross-sectional questionnaire design with anonymous online data collection to balance convenience and data integrity, recruiting nurses from 12 hospitals in Western China.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Sample and Sampling\u003c/h2\u003e\u003cp\u003eParticipants comprised registered in-service nurses. Inclusion criteria were: (a)\u0026thinsp;\u0026ge;\u0026thinsp;1 year of employment, (b)\u0026thinsp;\u0026ge;\u0026thinsp;30 clinical work hours weekly, and (c) provision of informed consent. Exclusions included interns, trainees, rehired retirees, and nurses on maternity\u0026ndash;medical leave or external training during the survey period. To ensure structural equation modeling (SEM) accuracy, a minimum sample size of 200 was required (Kumar \u0026amp; Natarajan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, p. 21; Werner Reinartz, 2009, p. 333). This threshold was further validated against the rule of exceeding tenfold the total number of scale items (Ghanbar \u0026amp; Rezvani, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, pp. 79\u0026ndash;108). Based on these guidelines, we determined that a target sample size of 280 would satisfy both the absolute minimum for SEM and the item-to-participant ratio. Using snowball sampling, 305 nurses were recruited. After excluding invalid responses, 302 valid questionnaires were retained (valid response rate of 99.02%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data Collection\u003c/h2\u003e\u003cp\u003eData will be collected from June to July 2025. Researchers designed an online questionnaire via the WeChat mini-program \u0026lsquo;WPS Form\u0026rsquo;, and generated a QR code for distribution. After explaining the study\u0026rsquo;s purpose and significance, researchers contacted nurses individually to distribute the QR codes upon obtaining their consent. Researchers required participants to complete all questions prior to submitting the questionnaire through the platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Ethical Considerations\u003c/h2\u003e\u003cp\u003eThe study was reviewed and approved by the Academic Ethics Committee of Leshan Vocational and Technical College (Approval No.: ). At the beginning of the document, a clearly stated informed consent statement highlighted voluntary participation, anonymity, and the right to withdraw without any adverse effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Instruments\u003c/h2\u003e\u003cp\u003e\u003cb\u003eHospital Ward Type (HWT)\u003c/b\u003e. A researcher-designed demographic questionnaire collected data on gender, education level, age, hospital type, institutional tier, ownership structure, and patient care unit classification. Three categories were identified: Emergency Room, Intensive Care Unit (ICU), and General Medical Ward.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNurses\u0026rsquo; Work Stress (NWS)\u003c/b\u003e. This construct was operationalized using Yu\u0026rsquo;s Nurse Stressors Scale (2007), which evaluates stress across five domains: (a) professional role conflicts, (b) temporal workload distribution, (c) environmental resource adequacy, (d) complex patient management demands, and (e) administrative communication efficacy. The 35-item instrument utilizes a 4-point Likert format anchored by Strongly Disagree (1) and Strongly Agree (4), with higher scores indicating greater stress. Cronbach\u0026rsquo;s α was 0.94 in the original study (Yu, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, pp. 2090\u0026ndash;2093) and 0.911 in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNurses\u0026rsquo; Digital Literacy (NDL).\u003c/b\u003e Assessment employed Liu et al.\u0026rsquo;s Digital Competence Assessment Tool (2025), encompassing five facets: (a) technological awareness, (b) information synthesis capacity, (c) virtual collaboration proficiency, (d) innovative application skills, and (e) cybersecurity stewardship. Respondents rated item relevance on a 5-point Likert scale from Strongly Disagree (1) to Strongly Agree (5), where higher total scores signify superior self-perceived digital competence. Cronbach\u0026rsquo;s α was 0.941 in the original study (Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, pp. 112\u0026ndash;115) and 0.825 in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNurses\u0026rsquo; Psychological Resilience (NPR)\u003c/b\u003e. Lin et al.\u0026rsquo;s Psychological Resilience Inventory for Nurses (2020) served as the measurement basis, capturing three subdomains: (a) contextual adaptation to clinical environments, (b) individual coping mechanisms, and (c) collaborative system rebuilding after adversity. This 34-item survey uses a 5-point response ladder from Strongly Disagree (1) to Strongly Agree (5), with cumulative scores reflecting enhanced psychological flexibility. Cronbach\u0026rsquo;s α was 0.928 in the original study (Lin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, pp. 44\u0026ndash;47) and 0.909 in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Data Analysis\u003c/h2\u003e\u003cp\u003eIBM SPSS Statistics 30.0 was utilized for conducting all statistical analyses. Descriptive statistics, including t-tests and one-way ANOVA, were employed to summarize the demographic characteristics of participants, while Pearson correlation analysis investigated associations between digital literacy, psychological resilience, and work stress levels among nurses across different ward types. To rigorously validate the theoretical framework under study using a methodological triangulation approach, structural equation modeling (SEM) executed via IBM SPSS AMOS 30.0 tested path relationships among latent variables\u0026mdash;specifically, ward type, digital literacy, psychological resilience, and work stress. Concurrently, the PROCESS macro (version 4.3) with 10,000 bootstrap samples explored nuanced effects: Model 1 assessed the moderating role of ward type on the relationship between digital literacy and work stress; Model 4 examined the mediating effect of psychological resilience in this context; Model 7 investigated a dual-path mechanism specific to Emergency Rooms (ERs); and Model 58 evaluated the moderating impact of Intensive Care Unit (ICU) settings on the protective function of psychological resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Data Bias Verification and Analytical Reliability\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1 Data Preprocessing and Quality Control\u003c/h2\u003e\u003cp\u003eZ-score standardization was applied to address scale differences across variables (work stress measured on a 4-point Likert scale; resilience and digital literacy assessed using a 5-point Likert scale) (Collins et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, p. 735). Winsorization at the 1st and 99th percentiles bilaterally reduced extreme outliers, resulting in improved distributional properties: for digital literacy, kurtosis decreased from \u0026minus;\u0026thinsp;0.24 to \u0026minus;\u0026thinsp;0.878 (standard deviation changed from 0.50 to 0.67); for work stress, skewness shifted from \u0026minus;\u0026thinsp;0.09 to \u0026minus;\u0026thinsp;0.14 (standard deviation increased from 0.63 to 0.71). All variables satisfied normality assumptions based on established thresholds: absolute skewness values were below 0.44 (standard error\u0026thinsp;=\u0026thinsp;0.14), absolute kurtosis values were below 0.88 (standard error\u0026thinsp;=\u0026thinsp;0.28), and the ratio of absolute skewness to absolute kurtosis remained below 2 (David L.Streiner, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p. 466).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2 Model Fit Assessment and Reliability Verification\u003c/h2\u003e\u003cp\u003eStructural equation modeling (SEM) was performed using AMOS version 26.0 with maximum likelihood estimation. Fit indices (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated good model-data alignment: χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.973 (less than 3), RMSEA\u0026thinsp;=\u0026thinsp;0.057 (90%CI [0.044, 0.070]), GFI\u0026thinsp;=\u0026thinsp;0.940 (exceeding the benchmark of 0.90). CFI\u0026thinsp;=\u0026thinsp;0.977, NFI\u0026thinsp;=\u0026thinsp;0.954, IFI\u0026thinsp;=\u0026thinsp;0.977 (all surpassed 0.95) confirmed superiority over the null model. PNFI\u0026thinsp;=\u0026thinsp;0.709 and PCFI\u0026thinsp;=\u0026thinsp;0.726 (both exceeded 0.50) supported parsimony. Overall fit statistics met stringent criteria outlined by Kline (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, p. 188), thereby justifying subsequent path analysis.\u003c/p\u003e\u003cp\u003eMultiple linear regression modeled work stress (expressed as a z-score) as a function of resilience, digital literacy (each converted to z-scores), and ward type. With a sample size of N\u0026thinsp;=\u0026thinsp;302\u0026mdash;sufficient to meet the rule of thumb requiring at least 50 participants plus eight times the number of predictors (here, three predictors)\u0026mdash;statistical power exceeded 99% for detecting an effect size corresponding to R\u0026sup2;=0.29 at α\u0026thinsp;=\u0026thinsp;0.05. The final model explained 29.4% of variance in work stress (R\u0026sup2; = 0.294), with an adjusted R\u0026sup2; of 0.287 accounting for model complexity. The F-statistic for the overall regression was significant at F(3, 298)\u0026thinsp;=\u0026thinsp;41.464, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating that 28.7% of variability in work stress could be explained by the included predictors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural Equation Modeling Fit Indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFit Indices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvaluation Criteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel Values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGoodness-of-Fit Assessment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2;/df\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057 (90%CI:0.044\u0026ndash;0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt; Saturated Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e237.922 (Saturated Model: 240.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: Reference standards based on (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and Kline (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.6.3 Collinearity and Residual Diagnostics\u003c/h2\u003e\u003cp\u003eAll variance inflation factors (VIF) were \u0026lt;\u0026thinsp;1.32 (ward type\u0026thinsp;=\u0026thinsp;1.14, digital literacy\u0026thinsp;=\u0026thinsp;1.24, psychological resilience\u0026thinsp;=\u0026thinsp;1.32), well below the threshold of 5. The maximum condition index was 5.37, which is less than 30, and no single dimension accounted for multiple variables (ward type explained 97% of the variance in dimension 4), indicating that there was no multicollinearity issue. Standardized residuals (SRs) had a mean of 0 (SD\u0026thinsp;=\u0026thinsp;0.995), with 95% of the values falling within the range of [\u0026minus;\u0026thinsp;1.95, 1.95]. Three cases had |SR| \u0026gt;2.5, (max |SR| = 2.705), but Cook\u0026rsquo;s distance values (max D\u0026thinsp;=\u0026thinsp;0.12\u0026thinsp;\u0026lt;\u0026thinsp;1) indicated no influential cases. The residual-predicted value scatterplot demonstrated random scattering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and no heteroscedasticity was observed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Descriptive Analysis of Variables\u003c/h2\u003e\u003cp\u003eParticipants were predominantly aged 31\u0026ndash;40 years (44.04%) and female (96.69%). Nurses\u0026rsquo; Digital Literacy (NDL) scores differed significantly by age and ward type (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): nurses aged 31\u0026ndash;40 years had higher NDL scores (F\u0026thinsp;=\u0026thinsp;6.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while those working in general wards also exhibited elevated levels (F\u0026thinsp;=\u0026thinsp;9.04, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both work stress and psychological resilience varied markedly across ward types, with ICU nurses reporting significantly greater work stress (F\u0026thinsp;=\u0026thinsp;126.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and enhanced psychological resilience (F\u0026thinsp;=\u0026thinsp;31.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in Digital Literacy Across Nurse Demographics (N\u0026thinsp;=\u0026thinsp;302)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eNWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eNPR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e or \u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e or \u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e or \u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94(31.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e6.079**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2.655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133(44.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75(24.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026minus;0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026minus;1.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026minus;0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292(96.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHospital Ward Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112(37.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e9.036***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e126.213***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e31.029***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eICU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110(36.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80(26.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e: ER\u0026thinsp;=\u0026thinsp;Emergency Room; ICU\u0026thinsp;=\u0026thinsp;Intensive Care Unit; GW\u0026thinsp;=\u0026thinsp;General Ward;***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001;**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01༛*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05༛\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Structural Relationships and Direct Effects\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Intervariable Correlations\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed strong correlation coefficients between Hospital Ward Type (HWT) and Nurses\u0026rsquo; Work Stress (NWS) (η\u0026thinsp;=\u0026thinsp;0.668, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This finding was further supported by a significant negative path from HWT to NWS (β = \u0026minus;0.488) in the structural equation modeling (SEM) results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, indicating significantly higher stress levels among nurses working in high-risk wards (such as ICU and Emergency Room) compared to those in general wards.\u003c/p\u003e\u003cp\u003eAdditionally, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed significant negative correlations between Nurses\u0026rsquo; Psychological Resilience (NPR) and NWS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.214, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), as well as between NPR and HWT (η\u0026thinsp;=\u0026thinsp;0.407, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). However, the direct path from NPR to NWS in the SEM model was nonsignificant (β = \u0026minus;0.117, p\u0026thinsp;=\u0026thinsp;.211; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that the relationship might be mediated or suppressed by HWT. Nurses\u0026rsquo; Digital Literacy (NDL) demonstrated weak negative correlations with both NWS (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.201, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and HWT (η\u0026thinsp;=\u0026thinsp;0.236, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) according to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eDespite these bivariate associations, no significant direct effect of NDL on NWS emerged in the SEM analysis (β\u0026thinsp;=\u0026thinsp;0.065, p\u0026thinsp;=\u0026thinsp;.601; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), implying potential indirect effects through other variables like resilience. Notably, NDL and NPR were positively correlated (r\u0026thinsp;=\u0026thinsp;0.431, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which aligned with the significant direct path from NPR to NDL observed in the SEM results (β\u0026thinsp;=\u0026thinsp;0.061, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings collectively support a bidirectional relationship between NDL and NPR.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations Among Four Variables (N\u0026thinsp;=\u0026thinsp;302)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.431***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.201***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;0.214***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHWT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.236***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.407***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.668***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001;**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01༛*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Direct Effect Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrated a significant negative direct effect of Hospital Ward Type (HWT) on Nurses\u0026rsquo; Work Stress (NWS) (β = -0.488, p\u0026thinsp;=\u0026thinsp;0.006), indicating that ward environments maintain independent predictive validity for stress levels after adjusting for Nurses\u0026rsquo; Psychological Resilience (NPR) and Nurses\u0026rsquo; Digital Literacy (NDL). Notably, specific high-acuity settings such as intensive care units (ICUs) were associated with elevated work stress burden.\u003c/p\u003e\u003cp\u003eNeither Nurses\u0026rsquo; Psychological Resilience (NPR\u0026rarr;NWS: β = -0.117, p\u0026thinsp;=\u0026thinsp;0.211) nor Nurses\u0026rsquo; Digital Literacy (NDL\u0026rarr;NWS: β\u0026thinsp;=\u0026thinsp;0.065, p\u0026thinsp;=\u0026thinsp;0.601) exhibited statistically significant direct effects, suggesting no immediate buffering impact on stress. Instead, their influence appears to operate indirectly via mediating variables such as ward characteristics.\u003c/p\u003e\u003cp\u003eA significant positive association emerged between HWT and NPR (β\u0026thinsp;=\u0026thinsp;0.077, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), identifying HWT as a critical contextual factor shaping resilience capacity. Furthermore, NDL and NPR showed moderate positive correlation (r\u0026thinsp;=\u0026thinsp;0.061), implying that enhanced digital competence may promote perceived control thereby reinforcing adaptive coping mechanisms (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardized Estimates for SEM Paths and Intervariable Relationships\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΒ / Cov\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC.R.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNWS \u0026lt;--- NPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;1.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNWS \u0026lt;--- NDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNWS \u0026lt;--- HWT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDL \u0026lt;--\u0026gt;HWT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPR \u0026lt;--\u0026gt;HWT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDL \u0026lt;--\u0026gt;NPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: All estimates are standardized; ***p* \u0026lt; .001; **p* \u0026lt; .01; *p* \u0026lt; .05; ns\u0026thinsp;=\u0026thinsp;nonsignificant; NWS\u0026thinsp;=\u0026thinsp;work stress; NPR\u0026thinsp;=\u0026thinsp;psychological resilience; NDL\u0026thinsp;=\u0026thinsp;digital literacy; HWT\u0026thinsp;=\u0026thinsp;Hospital Ward Type.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Mediation and Moderation Effects in Variable Pathways\u003c/h2\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Psychological Resilience Mediation Mechanism (H1)\u003c/h2\u003e\u003cp\u003eThe results of Model 4 (as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that Nurses\u0026rsquo; Digital Literacy (NDL) significantly enhanced Nurses\u0026rsquo; Psychological Resilience (NPR) (β\u0026thinsp;=\u0026thinsp;0.420, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), accounting for 30.55% of the variance (F(3, 298)\u0026thinsp;=\u0026thinsp;43.69, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, NPR significantly reduced Nurses\u0026rsquo; Work Stress (NWS) (β = \u0026minus;0.152, p\u0026thinsp;=\u0026thinsp;.002), with a significant indirect effect observed (β = \u0026minus;0.064, 95% CI [\u0026minus;\u0026thinsp;0.109, \u0026minus;\u0026thinsp;0.022]); importantly, the 95% bootstrap confidence interval did not include zero. The direct effect of NDL on NWS was nonsignificant (β = \u0026minus;0.005, p\u0026thinsp;=\u0026thinsp;.922). These findings confirm that NDL indirectly reduces NWS through its effect on enhancing NPR. Thus, Hypothesis 1 was supported.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDecomposition of Nurses\u0026rsquo; Psychological Resilience (NPR) Mediation Effects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI/BootCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDL\u0026rarr;NPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.313, 0.527]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPR\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.249, -0.056]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.109, -0.022]*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.104, 0.094]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Based on 10,000 bootstrap samples; effects are significant if 95% CI excludes zero. Model fit: NPR model: R\u0026sup2; = 0.306, F(3, 298)\u0026thinsp;=\u0026thinsp;43.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; NWS model: R\u0026sup2; = 0.467, F(4, 297)\u0026thinsp;=\u0026thinsp;65.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Moderating Effect of Ward Type on Digital Literacy\u0026ndash;Work Stress Relationship (H2)\u003c/h2\u003e\u003cp\u003eModel 1 analysis (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) demonstrated that for nurses in emergency rooms, every 1-unit increase in Nurses\u0026rsquo; Digital Literacy (NDL) reduced Nurses\u0026rsquo; Work Stress (NWS) by 0.185 units (β = \u0026minus;0.185, p\u0026thinsp;=\u0026thinsp;0.025). However, no significant association was observed in non-emergency wards (β = \u0026minus;0.015, p\u0026thinsp;=\u0026thinsp;0.787). This indicates that NDL alleviates NWS among emergency room nurses. Additionally, ICU nurses had 1.094 units higher work stress than non-ICU nurses (β\u0026thinsp;=\u0026thinsp;1.094, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHospital Ward Type (HWT) Moderation Analysis Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConditional\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDL\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-ER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-0.125, 0.095]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDL\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-0.348, -0.023]*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDept_ICU\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[0.940, 1.248]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Based on 10,000 bootstrap samples; effects are significant if 95% CI excludes zero. Model fit: R\u0026sup2;=0.455, F(4,297)\u0026thinsp;=\u0026thinsp;62.00,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001;ER\u0026thinsp;=\u0026thinsp;Emergency Room; Non-ER\u0026thinsp;=\u0026thinsp;Not Emergency Room.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Moderating Effect of ICU on Resilience\u0026rsquo;s Protective Role (H3)\u003c/h2\u003e\u003cp\u003eModel 58 analysis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) revealed differential effects through simple slope examination: Nurses\u0026rsquo; Psychological Resilience (NPR) significantly reduced occupational stress in non-ICU wards (β = \u0026minus;0.274, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), yet this protective effect was completely nullified in ICU settings (β\u0026thinsp;=\u0026thinsp;0.018, p\u0026thinsp;=\u0026thinsp;0.796). Moderation analysis confirmed a significant positive moderating effect of ICU work environments on the NPR\u0026ndash;NWS relationship (interaction term: β\u0026thinsp;=\u0026thinsp;0.293, p\u0026thinsp;=\u0026thinsp;0.001), with a statistically significant Moderated Mediation Index (Index\u0026thinsp;=\u0026thinsp;0.108, 95% CI [0.006, 0.221]). These findings demonstrate that ICU settings suppress the stress-buffering effect of NPR, thereby providing empirical support for Hypothesis 3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eICU Moderation Analysis Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-ICU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerating Effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPR\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003eβ=-0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.0183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInteraction Term β\u0026thinsp;=\u0026thinsp;0.293**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConditional Indirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModeration Index\u0026thinsp;=\u0026thinsp;0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e95%BootCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e\u003cp\u003e[-0.159, -0.050]*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e[-0.078, 0.110]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[0.006, 0.221]*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:* Bootstrap\u0026thinsp;=\u0026thinsp;10,000 samples; effects significant if 95% CI excludes zero; NPR\u0026times;Dept_ICU interaction: β\u0026thinsp;=\u0026thinsp;0.293, SE\u0026thinsp;=\u0026thinsp;0.0889, t\u0026thinsp;=\u0026thinsp;3.290, p\u0026thinsp;=\u0026thinsp;0.001; Model fit: NPR model: R\u0026sup2;=0.309, F(4,297)\u0026thinsp;=\u0026thinsp;33.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; NWS model: R\u0026sup2;=0.486, F(5,296)\u0026thinsp;=\u0026thinsp;55.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e4.3.4 Dual-Pathway Stress Reduction Mechanism of Digital Literacy in Emergency Room Nurses (H4)\u003c/h2\u003e\u003cp\u003eIntegrated results from Models 1 and 7 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) revealed that in emergency rooms: Nurses\u0026rsquo; Digital Literacy (NDL) directly reduced Nurses\u0026rsquo; Work Stress (NWS) (β = \u0026minus;0.185, p\u0026thinsp;=\u0026thinsp;0.025). A significant indirect pathway through Nurses\u0026rsquo; Psychological Resilience (NPR) emerged (β = \u0026minus;0.131, 95% BOOT CI [\u0026minus;\u0026thinsp;0.200, \u0026minus;\u0026thinsp;0.071]). These dual pathways jointly accounted for 52.1% of the total effect on stress among the emergency nurses. The total effect (β = \u0026minus;0.316, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) corresponded to a 21% reduction in stress levels attributable to NDL. This finding demonstrates dual mechanisms underlying stress reduction\u0026mdash;direct effects and resilience-mediated pathways\u0026mdash;thereby supporting hypothesis H4.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEmergency Room Dual-Pathway Effect Decomposition\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePath Effect Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI/BootCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource of data\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect Path: NDL\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e[-0.348, -0.023]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect Path: NDL\u0026rarr;NPR\u0026rarr;NWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e[-0.200, -0.071]*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCalculated value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: * Bootstrap\u0026thinsp;=\u0026thinsp;10,000 samples, effects significant if 95% CI excludes zero; Indirect effect calculation: NDL\u0026rarr;NPR|ER: β\u0026thinsp;=\u0026thinsp;0.405 (Model 7), NPR\u0026rarr;NWS|ER: β=-0.289 (Model 7), Product: 0.405 \u0026times; (-0.289) = -0.131.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Digital Literacy Reduces Stress via Full Mediation of Psychological Resilience\u003c/h2\u003e\u003cp\u003eThis study revealed no direct effect of Nurses\u0026rsquo; Digital Literacy (NDL) on Nurses\u0026rsquo; Work Stress (NWS) but demonstrated its complete indirect reduction of stress through enhanced Nurses\u0026rsquo; Psychological Resilience (NPR), with a 100% mediation effect. This supports the \"technological competence\u0026ndash;psychosocial resources\" transformation framework proposed by Forn\u0026eacute;s-Vives et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, pp. 318\u0026ndash;323), wherein digital tools must be translated into psychological capital to alleviate occupational stress. Consistent with Ou Yang (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, pp. 98\u0026ndash;99), nurses' digital literacy requires self-efficacy mediation to influence work adaptation. It also aligns with findings by Lotte Bock (2025) and Li et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, pp. 385\u0026ndash;396), showing that digital training can yield measurable emotional relief over brief periods, thereby enhancing workplace well-being. Critically, our results confirm psychological resilience as the primary pathway for digital literacy's stress reduction in nursing. This indicates that integrating resilience-building interventions into digital technology training offers a viable practical measure to mitigate nurse stress.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Ward Type Moderates the Digital Literacy\u0026ndash;Stress Relationship\u003c/h2\u003e\u003cp\u003eOur study identified ward type as a core contextual factor, accounting for 48.8% of the variance in Nurses\u0026rsquo; Work Stress (NWS) while moderating the relationships between Nurses\u0026rsquo; Digital Literacy (NDL) and Nurses\u0026rsquo; Psychological Resilience (NPR). In emergency rooms, each 1-unit increase in NDL was associated with a reduction in NWS by 0.185 units; however, no significant direct association was observed in non-emergency wards. The ICU environment was associated with significantly higher stress levels\u0026mdash;nurses in ICU settings reported stress levels that were 1.094 units higher than those in non-ICU settings. These findings support prior research: For instance, the direct stress-reducing effect of emergency rooms aligns with findings from Dee McGonigle and Mastrian (2022, pp. 201\u0026ndash;230) and Maryam Montazeri (2021, p. 26402), where digital systems (e.g., e-triage tools) optimize patient flow, which directly reduces time pressure. Furthermore, the main effect of elevated stress in ICUs is consistent with Daniel R. Schweitzer\u0026rsquo;s work (2023, pp. 1076\u0026ndash;1080), who attributed this to the demanding nature of ICU care depleting personal resources such as self-control and decision-making capacity, leading to decision fatigue. The moderating role of organizational contexts echoes Xu and Zhao\u0026rsquo;s observations (2024, p. 1392811). Crucially, the ward environment determines whether the instrumental utility of NDL can be leveraged: Specifically, in emergency settings, digital tools enable real-time dynamic clinical decisions; in general wards, realizing digital value requires transformation through nurse resilience; whereas in ICUs, complex humanistic care demands resist being replaced by digital tools, necessitating systematic policy interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e5.3 ICU Environment Suppresses Resilience\u0026rsquo;s Protective Effect\u003c/h2\u003e\u003cp\u003eResilience significantly reduced Nurses\u0026rsquo; Work Stress (NWS) in non-ICU wards but was fully offset in ICUs, with a significantly moderated mediating effect (Index\u0026thinsp;=\u0026thinsp;0.108). These globally relevant findings indicate that chronic exposure to ICUs' high-pressure, traumatic environments may erode Nurses\u0026rsquo; Psychological Resilience (NPR). When demands exceed critical thresholds, NPR loses its buffering capacity (Angela M Kunzler, 2022, p. 104312; Du, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, p. 4059; Mealer et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, pp. 292\u0026ndash;299), supporting Hobfoll\u0026rsquo;s (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, pp. 513\u0026ndash;524) Conservation of Resources Theory. Notably, our results contrast with those reported by Salluh et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, p. 37), who observed persistent resilience effects in ICUs. This divergence underscores that the high-intensity and high-stress context of ICUs\u0026mdash;characterized by frequent emergencies and complexity\u0026mdash;therefore requires structured multilevel support systems rather than relying solely on individual resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Dual-Pathway Stress Reduction Mechanism in Emergency Rooms\u003c/h2\u003e\u003cp\u003eOur results showed that the direct path (Nurses\u0026rsquo; Digital Literacy \u0026rarr; Nurses\u0026rsquo; Work Stress) contributes to 12.3% stress reduction, while the indirect path (NDL \u0026rarr; Nurses\u0026rsquo; Psychological Resilience (NPR) \u0026rarr; NWS) accounts for 8.7%. The combined dual-pathway effects explain 52.1% of the variance in stress levels, resulting in 21% overall stress reduction. This confirms that digital literacy serves as a contextually adaptive resource, yielding dual effects through both direct (technological empowerment) and indirect (psychological empowerment) pathways. The dual-pathway mechanism validates the Job Demands-Resources Model (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, pp. 273\u0026ndash;285), extending Erkan Boğa (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p. e38933) on how emergency experience enhances digital and health literacy. It also aligns with Wilson\u0026rsquo;s advocacy (2024, pp. 110\u0026ndash;111) for implementing digital solutions\u0026mdash;such as mobile apps\u0026mdash;to improve nurses\u0026rsquo; mental health. Notably, integrating digital training with resilience-building interventions offers a practical stress-reduction strategy for emergency rooms.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study demonstrates that while digital literacy and psychological resilience significantly reduce nurses\u0026rsquo; work stress, ward environments exert complex moderating effects: In emergency rooms, stress reduction occurs via dual pathways (direct and indirect); in non-ICU wards, resilience primarily mediates this relationship; whereas in ICU settings, the protective role of resilience becomes entirely suppressed. As a key contextual factor, ward type independently accounts for 48.8% of the variance in work stress.\u003c/p\u003e\u003cp\u003eClinical recommendations for differentiated interventions include:\u003c/p\u003e\u003cp\u003e\u003cb\u003eLow-risk wards\u003c/b\u003e (e.g., general units): Enhance resilience through mindfulness training and structured peer support groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMedium-risk wards\u003c/b\u003e (e.g., emergency rooms): Deploy intelligent decision terminals, optimize electronic health record systems, implement micro-learning modules for digital literacy, such as Nursing Digital Badges (Alberto Lana et al., 2024, p. 71), and facilitate dual-pathway stress reduction through targeted resilience training.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh-risk wards\u003c/b\u003e (e.g., ICUs): Undertake systemic redesign encompassing workflow reorganization, patient assignment optimization, and critical patient tiering protocols, supplemented by post-traumatic growth training for staff members and crisis simulation exercises to restore depleted resilience capacity.\u003c/p\u003e"},{"header":"7. Limitations and Future Research","content":"\u003cp\u003eThe cross-sectional design limits causal inference, and self-reported data are susceptible to common-method bias. Therefore, future studies should employ: longitudinal tracking; objective physiological measures; randomized controlled trials\u0026mdash;to validate interventions and explore long-term dynamics across ward contexts, digital literacy, resilience, and work stress.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eHWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eHospital Ward Type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNWS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eNurses\u0026rsquo;\u0026nbsp;Work Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eNurses\u0026rsquo;\u0026nbsp;Digital Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eNurses\u0026rsquo;\u0026nbsp;Psychological Resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eICU\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eIntensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eEmergency Room\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eGeneral Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eStructural Equation Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eGoodness-of-Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eComparative Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eNormed Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eIncremental Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eTucker-Lewis Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eParsimonious Normed Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003ePCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eParsimonious Comparative Fit Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eVariance Inflation Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eStandardized Residual\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003eStandard Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Sichuan Province High-level Social Science Research Team Development Program of Leshan Normal University, China. We would like to thank nurses who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript was jointly completed by Yan Wu (Y.W.), Wanbin She (W.S.), Qiyan Peng (Q.P.), Jile Li (J.L.), and Chenghong She (C.S.). The authors confirm that: a) The order of co-first authorship reflects relative leadership in research design, conceptualization, and data collection (Yan Wu), while methodology, manuscript writing, and theoretical innovation were led by Wanbin She. b) Q.P., J.L.and C.S. Conceptualization, methodology, interpretation of data, writing - review \u0026amp; editing. c) All authors have read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been funded by the Sichuan Provincial Education and Teaching Reform Project (JG2024-1036) and the Leshan Normal University Scientific Research Start-up Project for Introducing High-level Talents (RC2025041).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed are presented in the article. Upon request, the corresponding author will provide access to the datasets supporting these findings; however, they cannot be made publicly available due to privacy and ethical constraints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Academic Ethics Committee of the Leshan Vocational and Technical College (Approval No.: LVTCERC202502; date of approval: 25 May 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten consent has been obtained from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests in the publication of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlberto Lana, Beatriz S\u0026aacute;nchez-Garc\u0026iacute;a, Mar\u0026iacute;a Gonz\u0026aacute;lez-Garc\u0026iacute;a, Ana Fern\u0026aacute;ndez-Feito, \u0026amp; David Gonz\u0026aacute;lez-Pando. 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Relationship between depression and burnout among nurses in Intensive Care units at the late stage of COVID-19: a network analysis. \u003cem\u003eBMC nursing\u003c/em\u003e,\u003cem\u003e Vol.23\u003c/em\u003e(No.1), 224. https://doi.org/10.1186/s12912-024-01867-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital literacy, Psychological resilience, Nurses’ work stress, Ward environment, Moderating effect","lastPublishedDoi":"10.21203/rs.3.rs-7418214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7418214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eNurses' work stress significantly impacts the quality of nursing care. While digital literacy and psychological resilience serve as potential mitigating factors, their mechanisms of action remain unclear. The moderating effect of ward environments, in particular, requires urgent validation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe employed snowball sampling and a cross-sectional design to recruit 305 in-service nurses from 12 hospitals in Western China. Participants completed standardized questionnaires. Descriptive statistics and Pearson correlation analyses were performed using SPSS version 30.0. Path analysis was conducted using AMOS version 30.0. Additionally, PROCESS macro (version 4.3) was used to test the moderating role of ward environment (specifically, Emergency Room vs. ICU) in the pathway from digital literacy to work stress, assess the mediating effect of psychological resilience, and evaluate the conditional effect of ward environment on this mediation pathway.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePsychological resilience fully mediated the relationship between digital literacy and nurses' work stress (β = \u0026minus;0.064). Hospital ward context accounted for 48.8% of the variance in nurses' work stress and moderated the pathways involving both digital literacy and psychological resilience. In ICU settings, the protective effect of psychological resilience was attenuated (index value\u0026thinsp;=\u0026thinsp;0.108), and these units exhibited the highest levels of work stress (F\u0026thinsp;=\u0026thinsp;126.213, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the Emergency Room showed a dual-pathway stress reduction mechanism: a direct effect of digital literacy (β = \u0026minus;0.185) and an indirect effect through psychological resilience (β = \u0026minus;0.131), collectively reducing work stress by 21%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe stress-reducing effects of digital literacy and psychological resilience are contingent upon specific ward types. For clinical practice, tailored interventions are recommended: integrate digital technology with resilience training programs in Emergency Rooms; prioritize systemic workflow redesign in ICUs; and enhance resilience-building initiatives in general wards.\u003c/p\u003e","manuscriptTitle":"Digital Literacy and Psychological Resilience Alleviate Nurses’ Work Stress: Examining the Moderating Role of Ward Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 06:37:03","doi":"10.21203/rs.3.rs-7418214/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":"02658f34-487e-4dad-a39f-306742ecb761","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T05:39:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 06:37:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7418214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7418214","identity":"rs-7418214","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}