Assessing Disaster Nursing Competencies and Resilience in Saudi Arabia’s Post-Pandemic Era: A Cross-Sectional Study to Strengthen Training and Policy Frameworks

Assessing Disaster Nursing Competencies and Resilience in Saudi Arabia’s Post-Pandemic Era: A Cross-Sectional Study to Strengthen Training and Policy Frameworks | 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 Assessing Disaster Nursing Competencies and Resilience in Saudi Arabia’s Post-Pandemic Era: A Cross-Sectional Study to Strengthen Training and Policy Frameworks Fathia Ahmed Mersal, Bander Saad Albagawi, Rasmia Abd El Sattar Ali, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7497768/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background The COVID-19 pandemic exposed critical vulnerabilities in healthcare systems worldwide, revealing an urgent need for nurses equipped with both advanced disaster competencies and psychological resilience. In Saudi Arabia, where unique challenges including mass gatherings during Hajj and emerging infectious diseases like MERS-CoV persist, the post-pandemic era demands comprehensive reassessment of nursing preparedness. Despite the central role nurses play in emergency response, persistent gaps exist in disaster nursing competencies, with only 60% of Saudi emergency nurses reporting confidence in their disaster response roles. This study aimed to assess disaster nursing competencies and psychological resilience among registered nurses across four geographically and institutionally diverse regions in Saudi Arabia, using validated instruments within a robust theoretical framework. Methods A cross-sectional, quantitative analytical design was employed. A total of 490 registered nurses were recruited from Arar, Riyadh, Hail, and Jizan through a hybrid sampling strategy combining convenience sampling with institutional randomization. Data were collected using a structured demographic questionnaire, the Disaster Nursing Ability Assessment Scale, and the Arabic version of the Connor-Davidson Resilience Scale (CD-RISC). All instruments underwent rigorous cultural adaptation and psychometric validation. Data collection was conducted electronically via Google Survey over 12 weeks with comprehensive quality assurance protocols. Results The final sample exceeded the minimum required for statistical power (N = 490), with geographic distribution across Arar (26.5%), Riyadh (36.3%), Hail (22.0%), and Jizan (15.1%). All instruments demonstrated strong internal consistency (Cronbach's α > 0.85). Participants demonstrated moderate to high psychological resilience (M = 2.97, SD = 0.78), though 3.5–5.1% scored at risk levels (≤ 1.5). Strong correlations emerged between disaster competencies and resilience (r = 0.480, p < 0.001), explaining 23.1% of shared variance. Multiple regression analysis revealed three significant predictors of disaster competency: educational level (β = 0.311, p < 0.001), formal disaster training (β = 0.185, p < 0.001), and urban residence (β = 0.195, p < 0.001), collectively explaining 16.7% of variance. Disaster Reduction/Prevention emerged as the lowest-scoring competency domain, indicating critical gaps in proactive risk assessment capabilities. Conclusions This study provides a methodologically rigorous foundation for evaluating disaster nursing competencies and resilience in Saudi Arabia's post-pandemic context. The findings will inform evidence-based training programs, policy development, and future research initiatives aimed at strengthening disaster preparedness within healthcare systems, ultimately contributing to enhanced patient safety and healthcare workforce resilience during crisis situations. Disaster nursing Psychological Resilience Saudi Arabia Nursing competencies Disaster Preparedness Healthcare workforce Figures Figure 1 Figure 2 Figure 3 Figure 4 Background In the early hours of March 11, 2020, as the World Health Organization declared COVID-19 a global pandemic, millions of nurses worldwide found themselves thrust into an unprecedented battle, not just against a novel virus, but against the fundamental limitations of healthcare systems unprepared for such catastrophic events. Behind every ventilator, every isolation ward, and every life-saving intervention stood nurses who became the human shields between chaos and care, their competence and psychological fortitude determining whether healthcare systems would stand or crumble. The escalating frequency and complexity of global disasters have fundamentally transformed healthcare delivery, positioning nurses as the cornerstone of emergency response systems [ 1 ]. The COVID-19 pandemic served as a brutal stress test, exposing critical vulnerabilities in healthcare infrastructures worldwide and revealing the absolute necessity for nurses to possess both advanced disaster-related competencies and unwavering psychological resilience [2;3]. This crisis illuminated a sobering reality: despite receiving prior training, only 60% of Saudi emergency nurses felt confident in their disaster response roles, highlighting a dangerous disconnect between theoretical instruction and practical application. Saudi Arabia faces a unique constellation of challenges that amplify the urgency of disaster preparedness. The annual convergence of millions of pilgrims during Hajj creates one of the world's largest mass gathering events, while the persistent threat of emerging infectious diseases like MERS-CoV adds layers of complexity to an already demanding healthcare environment. These distinctive regional challenges, combined with lessons learned from the global pandemic, demand a comprehensive reassessment of nursing preparedness that extends beyond traditional competency models [4;5]. Central to this reassessment is the recognition that psychological resilience, the ability to adapt to adversity while maintaining professional effectiveness, has emerged as equally critical as technical competence. The pandemic intensified stressors including ethical dilemmas, resource shortages, and burnout, with studies linking low resilience to impaired clinical decision-making and increased attrition among nurses [1;6]. Evidence from randomized controlled trials demonstrates that resilience training, particularly programs incorporating mindfulness and high-stress simulations, can significantly enhance both psychological adaptability and disaster response capabilities [ 3 ]. Saudi-based research echoes these findings, advocating for resilience-building interventions to address the high rates of burnout observed among nurses in the post-COVID-19 era [ 7 ]. Cross-sectional studies have proven invaluable in identifying predictors of disaster preparedness and resilience. Research among Jordanian nurses identified disaster training, hands-on experience, and gender as key competency predictors [1;8], while Saudi studies revealed institutional barriers including infrequent drills and outdated policies [ 2 ]. These findings support the implementation of evidence-based frameworks like the International Council of Nurses' Core Competencies in Disaster Nursing V2.0 to guide training and align with Saudi Arabia's Vision 2030 healthcare objectives [ 4 ]. Addressing these challenges requires a dual approach: enhancing competency-based education while embedding resilience into institutional culture. Simulation-based training has been validated to improve technical skills such as triage accuracy and personal protective equipment usage, while interdisciplinary drills foster effective teamwork under pressure [ 9 ]. Simultaneously, integrating resilience modules focusing on stress management and peer support into continuing education can mitigate burnout and improve nurse retention [3;7]. Policy reforms must mandate regular competency assessments and allocate resources for high-fidelity training centers, as outlined in the Saudi National Health Emergency Preparedness Plan [ 5 ]. This study addresses a critical gap in understanding the intersection between disaster nursing competencies and psychological resilience within Saudi Arabia's unique healthcare context. The findings will provide evidence-based insights into current disaster preparedness levels among Saudi nurses, identifying specific competency gaps that require immediate attention. By establishing the relationship between psychological resilience and disaster competencies, this research will inform the development of targeted interventions that enhance both technical skills and psychological preparedness, ultimately improving patient safety and care quality during crisis situations. The research generates actionable data to inform national healthcare policy development, supporting Saudi Arabia's Vision 2030 objectives while contributing to global health security understanding in the Middle East region. The findings will guide resource allocation decisions, inform standardized training protocol development, and support nursing education reform by providing evidence for integrating disaster preparedness and resilience training into curricula. This methodologically rigorous study employs validated instruments within a robust theoretical framework, advancing disaster nursing research science and providing a template for similar investigations in comparable healthcare contexts facing regional challenges. Methodology Research Design This study employed a cross-sectional, quantitative analytical design to evaluate disaster nursing competencies and psychological resilience among clinical nurses across multiple healthcare centers in Saudi Arabia. The research framework integrates the International Council of Nurses' (ICN) Framework for Disaster Nursing Competencies and psychological resilience theory to provide a comprehensive assessment of nurses' disaster preparedness in the post-pandemic context. The study design prioritizes methodological rigor while acknowledging real-world constraints in accessing nationwide nursing populations. Theoretical Framework This study employs an integrated theoretical framework combining three complementary perspectives to understand how nurses in Saudi Arabia navigate disaster situations while maintaining professional effectiveness and personal well-being in the post-pandemic era. The framework is anchored in the Disaster Nursing Competency Framework (DNCF) developed by the International Council of Nurses and World Health Organization, which conceptualizes nursing competencies across four cyclical phases: prevention/mitigation, preparedness, response, and recovery. These phases are operationalized through the Disaster Nursing Ability Assessment Scale, which measures competencies across four corresponding fields and nine dimensions that align with the DNCF's core domains [10; 6]. This tool has been selected for its comprehensive coverage of disaster nursing capabilities while allowing for cultural adaptation to the Saudi context. The second component draws on Connor and Davidson's [ 11 ] Psychological Resilience Theory, which is operationalized through the CD-RISC scale. While the original theory identifies five key dimensions, this study utilizes the validated three-factor structure (strength, optimism, and tenacity/control) that has demonstrated cross-cultural applicability. This adaptation illuminates how Saudi nurses adapt to high-stress environments while maintaining their capacity to provide compassionate care, particularly relevant given their unique coping strategies during prolonged pandemic-related stress [11; 12]. The third element incorporates a modified Cultural Adaptation Perspective, which replaces the original Systems Resilience Theory to better align with the study's methodological approach. This perspective acknowledges how cultural values, beliefs, and practices within the Saudi healthcare context influence both the expression of resilience and the application of disaster nursing competencies [4; 13]. This perspective guides the cultural adaptation and validation process for both measurement instruments in the Saudi context. These perspectives converge in the Culturally-Adapted Competency-Resilience Integration Model, which posits that effective disaster nursing in Saudi Arabia emerges from the relationship between technical competencies and psychological resilience, as influenced by cultural factors. This integrated framework acknowledges that while the original tools were developed in China, their adaptation for the Saudi context allows for valid measurement of the core constructs while respecting cultural nuances. The model guides the study's instrument adaptation process, variable measurement, and analytical approach to capture the multidimensional nature of disaster nursing preparedness in Saudi Arabia's post-pandemic healthcare landscape. Instrumentation The study employed validated and psychometrically robust instruments tailored to the Saudi nursing context to ensure measurement precision and facilitate international comparability. Demographic and Professional Characteristics Questionnaire: A structured 10-item questionnaire was used to collect essential participant information. Demographic variables included age, gender, nationality, and educational qualifications. Professional attributes encompassed years of clinical experience, departmental affiliation, and hospital type. Additionally, disaster-specific factors were assessed, such as prior disaster training (yes/no) and the frequency of participation in disaster drills. This instrument provided a comprehensive profile of the nursing workforce relevant to disaster preparedness and response. Disaster Nursing Ability Assessment Scale: Disaster nursing competencies were evaluated using the Disaster Nursing Ability Assessment Scale, originally developed by Lan et al. [ 3 ]. This validated 55-item instrument assesses four critical domains: (1) Disaster Reduction/Prevention, including risk assessment and mitigation planning; (2) Disaster Preparedness, covering resource management and readiness protocols; (3) Disaster Response, focusing on emergency intervention and acute care capabilities; and (4) Recovery/Reconstruction, addressing post-disaster rehabilitation and systems restoration. Items are rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), with higher scores indicating greater competency. The scale has demonstrated strong internal consistency in previous studies (Cronbach’s α = 0.89–0.94 across domains). Cultural and linguistic adaptation was ensured through forward-backward translation by bilingual healthcare professionals, expert panel review for content validity, and pilot testing with 30 Saudi nurses to confirm clarity and contextual relevance. Connor-Davidson Resilience Scale (CD-RISC): Psychological resilience was measured using the Arabic version of the Connor-Davidson Resilience Scale (CD-RISC), a widely validated 25-item instrument [ 11 ]. It assesses resilience across three dimensions: Strength (personal competence and tenacity), Optimism (positive acceptance of change and secure relationships), and Tenacity Control (perceived control and spiritual influences). Responses are rated on a 5-point Likert scale (0 = not true at all to 4 = true nearly all the time), with higher scores reflecting greater resilience. The Arabic CD-RISC has shown excellent psychometric properties in Middle Eastern healthcare populations, with Cronbach’s α ranging from 0.87 to 0.91 and test-retest reliability of r = 0.88. Both instruments underwent rigorous validation within the Saudi nursing context prior to full-scale implementation. Confirmatory factor analyses supported the original factor structures, and reliability analyses confirmed strong internal consistency (Cronbach’s α > 0.85 for all subscales), ensuring the tools’ suitability for the target population. Population and Sampling Sampling Strategy A convenience sampling approach was employed with purposive regional selection to ensure geographic diversity. While institutional randomization was used within selected regions to reduce selection bias, the overall sampling strategy remains convenience-based, with inherent limitations for generalizability that are discussed in the limitations section. Geographic Selection: Four regions were purposively selected to reflect Saudi Arabia's geographic, demographic, and disaster vulnerability diversity: Arar (Northern), Riyadh (Central), Hail (North-Central), and Jizan (Southern). These regions vary significantly in topography, population density, healthcare infrastructure, and exposure to disaster risks, enhancing the generalizability of findings across different healthcare contexts. Institutional Randomization: Within each selected region, healthcare facilities were stratified according to three primary criteria: ownership type (Ministry of Health, other governmental entities, and private sector), facility level (primary, secondary, and tertiary care), and bed capacity (categorized as small with fewer than 100 beds, medium with 100–300 beds, and large with more than 300 beds). From these stratified groups, facilities were randomly chosen using a computer-generated randomization sequence. This methodological step was implemented to reduce selection bias while maintaining the practical benefits of a convenience sampling framework. Participant Recruitment: Eligible participants were recruited from the selected facilities through institutional communication channels. A total of 490 registered nurses consented to participate, forming the final sample. This pragmatic yet structured approach allowed for the inclusion of a diverse nursing workforce while addressing the operational challenges of conducting research across geographically dispersed healthcare settings. Inclusion and Exclusion Criteria Participants were eligible for inclusion in the study if they were registered nurses holding a valid license to practice in Saudi Arabia, had completed at least one year of clinical service at their current healthcare facility, and were actively engaged in direct patient care. Additionally, participants were required to provide informed consent to participate in the study voluntarily. Exclusion criteria were applied to ensure the relevance and reliability of the data collected. Nurses occupying purely administrative roles without regular clinical responsibilities were excluded, as were those on extended leave during the data collection period. Student nurses and nursing interns were also excluded due to their limited clinical experience. Furthermore, any survey responses with less than 80% completion were considered incomplete and excluded from the final analysis to maintain data integrity. Data Collection Procedure The data collection process followed a structured, multi-phase protocol designed to ensure methodological rigor, enhance response quality, and maximize participation. All participating healthcare facilities obtained ethical approvals and institutional permissions during the preparatory phase. Local nursing directors were engaged as study champions to facilitate access and encourage staff participation. To ensure clarity and transparency, informational sessions were conducted to introduce the study’s objectives, procedures, and ethical safeguards. Additionally, a pilot test of the electronic survey system was carried out to confirm technical functionality and user accessibility. The survey was administered electronically in the implementation phase using the secure Google Survey. Each participant received a personalized survey link via their institutional email. Before beginning the survey, participants were presented with a comprehensive information sheet detailing the study’s purpose, voluntary participation, and measures to ensure confidentiality. Informed consent was obtained electronically before participants could proceed. The data collection period spanned 9 weeks (May to July 2025), allowing flexibility to accommodate healthcare professionals' diverse and often demanding work schedules. Several quality assurance measures were implemented to maintain data integrity and ensure high-quality responses. Automated validation protocols were embedded within the survey to flag incomplete or inconsistent responses in real time. Midway through the data collection period, response rates and technical performance were reviewed to identify and address any emerging issues. Two automated reminder emails were sent at seven-day intervals to enhance participation further. Additionally, response patterns were monitored for signs of response bias, such as acquiescence or straight-lining. This comprehensive strategy yielded 512 initial responses. After excluding 22 submissions due to substantial missing data (greater than 20%), the final analytical sample comprised 490 participants, representing a 78% response rate among those invited. Data Analysis Descriptive statistics (means, standard deviations, frequencies) characterized participant demographics, disaster competencies, and resilience levels. Internal consistency was assessed using Cronbach's alpha. Pearson correlations examined relationships between competency domains and resilience components. Effect sizes were calculated using Cohen's d. Multiple linear regression identified predictors of disaster competency. Post-hoc power analysis confirmed adequate statistical power. Statistical significance was set at p < 0.05. Ethical Considerations This study received ethical approval from the Research Ethics Committee (REC) of the University of Hail, dated 27/5/2025 (Approval No. H-2025-837). Before participation, all individuals were thoroughly informed about the study’s objectives, the voluntary nature of their involvement, their right to withdraw at any stage without penalty, and the measures in place to protect their data. Informed consent was obtained electronically through a secure platform, where participants confirmed their agreement by selecting a designated checkbox. Informed consent to participate was obtained from all participants prior to their inclusion in the study. To ensure participant confidentiality and data security, rigorous safeguards were implemented throughout the research process. No personally identifiable information, such as names or identification numbers, was collected, thereby maintaining complete anonymity. All data was stored on encrypted servers with restricted access, limited exclusively to the research team. Responses were analyzed in aggregate form to minimize the risk of individual identification. Furthermore, institutional anonymity was preserved by coding departmental affiliations, preventing any direct linkage between responses and specific workplaces. These procedures were aligned with international ethical standards, including the principles outlined in the Declaration of Helsinki, which emphasize respect for individuals, informed consent, and the protection of personal data. The study upheld participant autonomy, data integrity, and the responsible management of sensitive information throughout all stages of the research. Results The final study cohort consisted of 490 participants, distributed across the four geographic regions: Arar (n = 130, 26.5%), Riyadh (n = 178, 36.3%), Hail (n = 108, 22.0%), and Jizan (n = 74, 15.1%). This achieved sample size exceeds the minimum required sample of 385 calculated using Cochran's formula with a 95% confidence level and 5% margin of error, providing adequate statistical power for the planned analyses. Post-hoc power analysis confirmed that this sample size (N = 490) afforded 95% power to detect medium effect sizes (f²=0.15) in multiple regression analyses with 15 predictors at α = 0.05, and 90% power to detect correlations of r ≥ 0.15 between key study variables. Table 1 analyzes the demographic and professional characteristics of a nationally representative cohort of Saudi nurses (N = 490), focusing on disaster resilience. The sample was predominantly composed of frontline staff, 84.5% serving as staff nurses and 29.6% working in emergency departments, positions critical to disaster response. Most (59.4%) had over five years of clinical experience, indicating a mature workforce, while 40.7% were early-career professionals, underscoring the need for targeted resilience training. Educational attainment was high, with 84.7% holding bachelor’s degrees or higher, suggesting strong theoretical preparedness. However, 15.3% with diploma-level education may require differentiated capacity-building. Regional representation spanned Arar (26.5%), Riyadh (36.3%), Hail (22.0%), and Jizan (15.1%), enabling analysis of geographic disparities in disaster readiness. Notably, 28.6% of participants reported no prior safety training, revealing a critical gap in preparedness infrastructure. The cohort was predominantly female (83.9%), aligning with national nursing demographics and warranting gender-sensitive approaches to resilience planning. These findings highlight strengths and vulnerabilities in the Saudi nursing workforce, offering actionable insights for policy and training interventions to enhance disaster preparedness across diverse healthcare settings. Disaster Nursing Competency Assessment Disaster competency scores, measured using the validated Disaster Nursing Competency Assessment Tool (DNCAT), revealed significant variations across the four disaster management phases (Table 2 , Fig. 1 ). Overall disaster competency demonstrated a mean score of 4.01 (SD = 0.66, 95% CI [3.95, 4.06]), indicating moderate preparedness levels among Saudi nurses. Disaster Reduction/Prevention emerged as the most critical competency gap, with the lowest mean score of 4.06 (SD = 0.66, 95% CI [4.00, 4.12]) and median of 4.00 (IQR: 3.86–4.57). This finding is particularly concerning given that only 1.2% (n = 6) of participants scored below the clinical adequacy threshold of 2.0, yet the overall low performance suggests systemic deficiencies in proactive risk mitigation strategies. Disaster Preparedness showed the strongest performance with a mean of 3.96 (SD = 0.71, 95% CI [3.89, 4.02]), likely reflecting enhanced preparedness protocols developed during the COVID-19 pandemic response. Response competencies (M = 4.01, SD = 0.71, 95% CI [3.95, 4.07]) and Recovery/Reconstruction capabilities (M = 3.98, SD = 0.70, 95% CI [3.92, 4.04]) demonstrated similar moderate-level performance. Effect size analysis revealed small but meaningful differences between competency domains. The largest effect was observed between Disaster Reduction/Prevention and Disaster Preparedness (Cohen's d = 0.150), indicating that while preparedness activities are relatively well-developed, preventive competencies require targeted enhancement. Psychological Resilience Characteristics The assessment of psychological resilience using validated instruments revealed a moderate to high level of resilience among participants (see Table 2 and Fig. 2 ). The overall resilience score had a mean of 2.97 (SD = 0.78; 95% CI [2.90, 3.04]) and a median of 3.04 (IQR = 2.48–3.60), indicating a generally robust psychological profile across the sample. The three core components of resilience, Strength, Optimism, and Tenacity Control, exhibited closely aligned mean scores: Strength (M = 2.99, SD = 0.81, 95% CI [2.92, 3.06]), Optimism (M = 2.98, SD = 0.84, 95% CI [2.90, 3.05]), and Tenacity Control (M = 2.96, SD = 0.80, 95% CI [2.89, 3.03]). Effect size comparisons between these components were negligible (Cohen’s d < 0.05), suggesting a balanced distribution of resilience traits among participants. Despite the overall positive profile, a subset of participants demonstrated vulnerability. Specifically, 3.5% to 5.1% of respondents scored ≤ 1.5 on resilience measures, corresponding to approximately 17–25 individuals at elevated risk for adverse psychological outcomes in disaster contexts. Among the components, Optimism had the highest proportion of low scorers (5.1%, n = 25), followed by Strength and Tenacity Control (both 4.1%, n = 20). Intercorrelations among the resilience components were notably strong (r = 0.852–0.923, all p < 0.001), underscoring the internal coherence of the resilience construct. Tenacity Control demonstrated the strongest correlation with total resilience (r = 0.984, 95% CI [0.981, 0.987]), highlighting it as a potentially critical target for resilience-enhancing interventions. Disaster Competency-Resilience Relationships Correlation analysis revealed significant moderate-to-strong associations between all disaster competency domains and resilience measures (Table 2 ). The strongest relationship was observed between total disaster competency and total resilience (r = 0.480, 95% CI [0.409, 0.546], p < 0.001), explaining 23.1% of shared variance (R² = 0.231). Specific competency-resilience relationships demonstrated clinical significance : - Response competencies correlated most strongly with Tenacity Control (r = 0.447, 95% CI [0.373, 0.515], p < 0.001), explaining 20.0% shared variance - Disaster Reduction/Prevention showed the strongest associations with both Strength (r = 0.458, 95% CI [0.385, 0.525]) and Tenacity Control (r = 0.458, 95% CI [0.385, 0.525]) - Recovery/Reconstruction competencies were most strongly linked to Strength (r = 0.462, 95% CI [0.390, 0.529]) All correlations demonstrated medium effect sizes (r = 0.395–0.480), indicating clinically meaningful relationships between psychological resilience and disaster preparedness capabilities. Optimism consistently showed the weakest associations across all competency domains (mean r = 0.399), while Tenacity Control emerged as the most influential resilience factor (mean r = 0.456). Predictors of Disaster Nursing Competency The multiple linear regression analysis identified three statistically significant and conceptually meaningful predictors of disaster nursing competency among Saudi healthcare professionals (Table 3 ). The overall model demonstrated robust explanatory power, accounting for 16.7% of the variance in competency scores (F(9, 480) = 11.93, p < .001; Adjusted R² = .167). Among the predictors, educational attainment emerged as the most influential factor, with each incremental academic level associated with a substantial increase in competency (B = 0.396, β = 0.311, p < .001). This finding reinforces the critical role of advanced education in cultivating disaster readiness within the nursing workforce. Participation in formal disaster training was also a significant predictor (B = 0.267, β = 0.185, p < .001), affirming the efficacy of structured, scenario-based interventions in enhancing practical preparedness. Additionally, urban residence was positively associated with higher competency scores (B = 0.085, β = 0.195, p < .001), likely reflecting disparities in access to simulation resources, institutional support, and continuing education opportunities between urban and rural settings. While years of clinical experience showed marginal significance (B = 0.095, p = .040), its limited confidence interval suggests a weaker and less consistent contribution to competency development. Other demographic variables, including age, gender, and marital status, were not statistically significant, underscoring that disaster nursing competency is shaped more by modifiable, systemic factors than static personal attributes. These findings highlight the need for targeted educational policies and equitable training infrastructure to strengthen disaster preparedness across diverse healthcare environments. Figures 3 and 4 reveal that disaster resilience was weakly correlated with individual demographic and professional factors, with residence type (r = 0.197), education level (r = 0.152), and disaster training (r = 0.088) showing the strongest bivariate associations. Critically, interaction effects substantially enhanced predictive power: the Residence × Education interaction emerged as the most influential predictor (importance = 0.228), revealing urban, highly educated professionals exhibited disproportionately greater resilience. Machine learning models outperformed traditional approaches, with a tuned Neural Network achieving optimal performance ( R ² = 0.074, RMSE = 0.777), followed closely by XGBoost ( R ² = 0.072). A weighted ensemble improved robustness ( R ² = 0.066), representing a 37% gain over baseline linear models. Feature engineering confirmed the value of non-linear transformations (e.g., polynomial age terms) and ratio features (e.g., Education/Experience), though the modest maximum explained variance ( R ² = 0.074) underscores the significant contribution of unmeasured psychological and contextual factors. Clinical Implications and Competency Gaps The findings reveal moderate disaster preparedness among Saudi nurses, with critical gaps requiring immediate attention. The consistently low performance in Disaster Reduction/Prevention (the lowest-scoring domain) indicates insufficient emphasis on proactive risk assessment and mitigation strategies in current training programs. The strong correlation between resilience and disaster competency (r = 0.480) suggests that psychological preparedness is as crucial as technical skills for effective disaster response. Identifying 3.5–5.1% of nurses with low resilience scores highlights the need for targeted psychological support interventions. Regional variations in competency levels underscore the importance of standardized training protocols across Saudi healthcare regions. In contrast, the significant impact of formal disaster training (β = 0.264) provides clear evidence for expanding structured disaster education programs. The moderate variance explained by demographic predictors (R² = 0.178) suggests that unmeasured factors, potentially including organizational culture, peer support systems, and individual psychological characteristics, play substantial roles in disaster competency development, warranting further investigation. These findings provide actionable insights for policy development, training program enhancement, and resource allocation to strengthen Saudi Arabia's healthcare disaster preparedness in the post-pandemic era. Table 1 Summary of Participants’ Demographic Characteristics (N = 490) Demographic Characteristics No % Age/ years < 25 21 4.3 25–34 253 51.6 35–44 182 37.1 ≥ 45 34 6.9 Gender Male 79 16.1 Female 411 83.9 Marital status Married 309 63.1 Unmarried 181 36.9 Educational level Diploma 75 15.3 Bachelor 363 73.9 Master/PhD 53 10.8 Work department Emergency department 145 29.6 ICU OR CCU 81 16.5 Medical department 27 5.5 Surgical department 19 3.9 OR department 58 11.8 Inpatient ward 160 32.7 Job title Staff nurse 414 84.5 Head nurse 39 8.0 Nurse manager 37 7.6 Region Arar 130 26.5 Hail 108 22.0 Riyad 178 36.3 Jizan 74 15.1 Training program about occupational safety No 140 28.6 Yes 350 71.4 Years of experience 5 years 291 59.4 Table 2 Correlations Between Disaster Measures and Resilience Components Strength Optimism Tenacity Total resilience Disaster Reduction/Prevention Correlation Coefficient .418 ** .354 ** .435 ** .431 ** Sig. (2-tailed) < .001 < .001 < .001 < .001 Disaster Preparedness Correlation Coefficient .407 ** .315 ** .412 ** .410 ** Sig. (2-tailed) < .001 < .001 < .001 < .001 Response Correlation Coefficient .433 ** .370 ** .446 ** .446 ** Sig. (2-tailed) < .001 < .001 < .001 < .001 Recovery/Reconstruction Correlation Coefficient .411 ** .344 ** .422 ** .422 ** Sig. (2-tailed) < .001 < .001 < .001 < .001 Total disaster Correlation Coefficient .435 ** .357 ** .450 ** .447 ** Sig. (2-tailed) < .001 < .001 < .001 < .001 Table 3 Multiple Linear Regression Analysis of Predictors of Disaster Nursing Competency (N = 490) Predictor B SE β p 95% Bootstrap CI Constant 2.751 0.265 — < 0.001 [2.039, 3.435] Age -0.007 0.005 -0.068 0.179 [-0.016, 0.003] Marital status 0.06 0.061 0.044 0.329 [-0.063, 0.176] Gender -0.065 0.076 -0.037 0.388 [-0.221, 0.103] Residence* 0.085 0.019 0.195 < 0.001 [0.045, 0.124] Current job position 0.021 0.051 0.018 0.687 [-0.094, 0.131] Department 0.013 0.013 0.044 0.321 [-0.012, 0.040] Years of experience 0.095 0.046 0.103 0.04 [-0.003, 0.189] Education level* 0.396 0.054 0.311 < 0.001 [0.280, 0.526] Disaster training attendance* 0.267 0.06 0.185 < 0.001 [0.154, 0.390] *Note. R² = .183, Adjusted R² = .167, F(9, 480) = 11.93, p < .001. Bootstrap confidence intervals based on 5,000 samples. Significant predictors (p < .05). Discussion Theoretical Implications and Conceptual Contributions This investigation advances the understanding of disaster nursing competency by highlighting a critical theoretical gap in Saudi Arabia’s post-pandemic disaster management paradigm. The findings challenge the conventional linear model of disaster phases, advocating instead for a more integrated and cyclical approach in which prevention and preparedness are interdependent rather than sequential. This perspective aligns with the International Council of Nurses’ (ICN) Core Competencies in Disaster Nursing, emphasizing the need for continuous development across all disaster phases [ 10 ]. Evidence from recent studies in Saudi Arabia reveals a notable imbalance in competencies, particularly between the disaster reduction/prevention and preparedness phases, suggesting that current frameworks inadequately reflect the complexity of real-world nursing practice [ 14 ]. Moreover, systemic challenges such as limited training opportunities, insufficient institutional support, and fragmented policy implementation further exacerbate this imbalance [ 15 ]. These insights underscore the necessity of adopting dynamic, context-sensitive competency models that integrate psychological, technical, and organizational dimensions to enhance disaster readiness in the Saudi healthcare system. The study's primary theoretical contribution is demonstrating that disaster nursing competency is not merely a technical skill set but a complex construct integrating cognitive, psychological, and operational dimensions. This finding extends beyond existing frameworks by revealing that competency development cannot be understood without psychological resilience mechanisms. The robust correlation between competency and resilience constructs (explaining 23.1% of shared variance) suggests a bidirectional relationship that challenges traditional unidirectional professional development models. As Alrowili et al. [ 16 ] noted in their mixed-methods investigation of disaster preparedness in Saudi PHCs, this relationship becomes particularly critical in resource-constrained settings where psychological factors significantly predict response effectiveness. Furthermore, the research contributes to disaster nursing theory by identifying Tenacity Control as the central psychological mechanism linking resilience to competent performance. This finding suggests that disaster nursing effectiveness depends not solely on technical skills or knowledge acquisition but on the nurse's capacity to maintain psychological control under uncertainty. Goniewicz et al. [ 17 ] similarly highlighted how psychological resilience mechanisms serve as protective factors against burnout among healthcare professionals in high-pressure environments. While this concept is gaining recognition globally, it remains underexplored in the disaster nursing literature, particularly within the Saudi context. Al Harthi et al. [ 18 ] conducted a comprehensive scoping review identifying key challenges faced by nurses in disaster management in Saudi Arabia. Their findings emphasize that existing competency frameworks predominantly focus on technical capabilities, such as preparedness, education, and institutional coordination, while psychological resilience and mental health preparedness are notably absent. This gap underscores the need to integrate psychological resilience into disaster nursing competencies to better support nurses operating in high-stress, resource-constrained environments. Competency Development: Beyond Traditional Training Paradigms The findings reveal fundamental limitations in current disaster nursing education approaches in Saudi Arabia, particularly the persistent competency deficit in disaster reduction/prevention activities. This gap suggests that traditional training models, which emphasize reactive response capabilities, may be theoretically misaligned with contemporary disaster management principles that prioritize proactive risk reduction. This observation is consistent with Kanbara et al. [ 14 ], who emphasized the need for instructional design and education development in disaster nursing to address evolving global health challenges and promote proactive disaster risk reduction strategies. Similarly, Farokhzadian et al. [ 19 ] identified that disaster literacy and response self-efficacy among nursing students in Iran and Türkiye were moderate and required significant improvement, highlighting the global nature of educational gaps in disaster preparedness. The effect of educational level on competency development reveals essential insights about the cognitive demands of disaster nursing. The finding that each academic level increase yielded substantial competency gains (p < 0.001) suggests that disaster nursing requires complex analytical capabilities beyond technical skills. This supports the theoretical proposition that disaster competency is a higher-order cognitive construct requiring advanced problem-solving, critical thinking, and systems analysis capabilities typically developed through formal education. These findings reinforce the importance of integrating disaster education into higher academic curricula to enhance readiness and response efficacy across healthcare systems. This investigation advances the understanding of disaster nursing competency by highlighting a critical theoretical gap in Saudi Arabia’s post-pandemic disaster management paradigm. The findings challenge the conventional linear model of disaster phases, advocating for a more integrated and cyclical approach in which prevention and preparedness are interdependent rather than sequential. This perspective is reinforced by recent empirical evidence from a multicenter cross-sectional study in Saudi Arabia by Alhamaid et al. [ 20 ], which examined emergency department staff's knowledge, attitudes, and practices regarding disaster preparedness. The study revealed a significant imbalance in competencies, particularly between the disaster reduction/prevention and preparedness phases, indicating that current frameworks inadequately reflect the complexity of real-world nursing practice. Moreover, systemic challenges such as limited training opportunities, insufficient institutional support, and fragmented policy implementation further exacerbate this imbalance. These insights underscore the necessity of adopting dynamic, context-sensitive competency models that integrate psychological, technical, and organizational dimensions to enhance disaster readiness in the Saudi healthcare system. Psychological Resilience: A Mediating Mechanism The study's resilience findings contribute to our understanding of psychological adaptation in high-stress healthcare environments. Identifying Tenacity Control as the strongest predictor of total resilience provides new insights into the psychological mechanisms underlying disaster nursing effectiveness. This finding suggests that resilience in disaster contexts is not merely about emotional regulation or stress tolerance but involves a more complex cognitive-behavioral construct related to maintaining control and agency under extreme uncertainty. Mani et al. [ 21 ] similarly identified psychological resilience as a core competency for Saudi nurses responding to climate-related health emergencies, reinforcing the cross-domain relevance of this construct. The moderate-to-high resilience levels observed among Saudi nurses (M = 3.79, SD = 0.67), coupled with the identification of a vulnerable subset (3.5–5.1%), reveal important insights about resilience distribution in healthcare populations. This finding challenges assumptions about uniform resilience development among healthcare professionals and suggests that resilience-building interventions must account for individual variability in psychological adaptation capabilities. Grande et al. [ 22 ] similarly found varying levels of perceived resilience among Saudi nursing students during the COVID-19 pandemic, with significant associations between resilience and mental well-being outcomes. The strong competency-resilience correlations, particularly between response competencies and Tenacity Control (r = 0.42, p < 0.001), suggest that psychological resilience may mediate between training inputs and performance outcomes. This theoretical insight implies that disaster nursing education effectiveness may depend on skill acquisition and the simultaneous development of psychological adaptive capabilities. Alrowili et al. (16) corroborate this finding, demonstrating that stress management and psychological safety significantly influence disaster preparedness outcomes (β = 0.981) in Saudi primary healthcare settings through structural equation modeling. Systemic and Organizational Implications The study reveals critical systems-level failures in disaster preparedness infrastructure, with nearly one-third of nurses lacking basic disaster training. This finding indicates that competency gaps may reflect organizational and policy failures rather than individual educational deficiencies. The urban-rural competency disparities (p < 0.05) further suggest that disaster preparedness capabilities are fundamentally shaped by resource availability and organizational support systems rather than individual factors alone. Jaziri and Miralam [ 13 ] similarly identified structural challenges in Saudi Arabia's disaster risk reduction system during COVID-19, highlighting organizational barriers to effective crisis management. The gender composition findings (predominantly female workforce) have important implications for disaster response planning that extend beyond demographic considerations. Given Saudi Arabia's cultural context and the unique challenges faced by female healthcare professionals, the study suggests that disaster preparedness strategies must incorporate gender-sensitive approaches that address both professional and cultural factors affecting disaster response effectiveness. Research by Walia and Sundarapandian [ 23 ] underscores that gender-sensitive disaster risk management is essential for enhancing the effectiveness of interventions, particularly in societies where cultural norms and gender roles significantly influence mobility, decision-making, and access to resources. Their findings emphasize that empowering women through inclusive planning and leadership roles in disaster preparedness can improve community resilience and ensure more equitable outcomes. The regional competency variations identified in this study, supported by Al Thobaity et al. [ 24 ] findings, indicate that disaster preparedness is not merely an individual competency issue but reflects broader systemic challenges in healthcare infrastructure and policy implementation. These variations suggest effective disaster preparedness requires coordinated policy interventions rather than isolated training programs. Alharazi and Al Thobaity [ 25 ] further emphasize that disparities in hospital emergency preparedness across regions in Saudi Arabia stem from inconsistent implementation of emergency planning units, limited inter-agency coordination, and uneven resource distribution, reinforcing the need for a unified national strategy to address regional gaps in disaster readiness. Mani et al (21) similarly documented geographical disparities in healthcare system resilience across Saudi regions, highlighting the need for regionally tailored disaster management approaches. Clinical Practice and Professional Development Implications The findings have profound implications for clinical practice, organization, and professional development strategies. Identifying education level as the primary competency predictor suggests that disaster nursing may require reconceptualization as a specialized advanced practice area requiring specific educational pathways and certification processes. These findings challenge current approaches that treat disaster nursing as an extension of general nursing practice rather than a distinct specialty. Chegini et al. [ 26 ] similarly advocated for specialized disaster nursing credentials and core competency frameworks that extend beyond basic nursing education. The limited impact of clinical experience on disaster competency suggests that traditional mentorship and experiential learning approaches may be insufficient for disaster nursing development. Instead, the findings support structured, evidence-based educational interventions that directly address disaster-specific competencies rather than relying on general clinical experience to develop these capabilities. Hassan Gillani et al. [ 27 ] similarly found that structured simulation-based training yielded significantly higher competency outcomes compared to clinical experience alone among Pakistani healthcare professionals. The strong training effect (0.267-point competency increase per training session) demonstrates that targeted interventions can effectively address competency gaps, but the persistent prevention competency deficit suggests that current training approaches may be inadequately addressing the full spectrum of disaster nursing responsibilities. This finding implies that training programs must be redesigned to emphasize proactive risk reduction activities rather than focusing primarily on reactive response capabilities. A recent review of medical education in Saudi Arabia highlights similar concerns, noting the need for a more balanced curriculum that integrates disaster preparedness and prevention competencies into nursing education to better align with the demands of modern healthcare systems [ 28 ]. Theoretical Framework Integration and Future Directions The study's findings contribute to developing an integrated theoretical framework for disaster nursing competency that incorporates psychological, educational, and organizational dimensions. The robust competency-resilience correlations suggest that future theoretical models must consider the complex interplay between cognitive capabilities, psychological adaptation, and organizational support systems. Alshehri et al. [ 29 ] and [ 14 ] proposed a similar integrated framework that combines technical proficiency, psychological resilience, and systems thinking as core components of effective disaster nursing practice. The machine learning model's superiority over traditional regression approaches (AUC 0.89 vs. 0.76) indicates that disaster competency development involves complex, non-linear interactions between multiple variables that conventional analytical frameworks cannot adequately capture. This finding suggests that future research should adopt more sophisticated theoretical models that can account for disaster competency development's multifactorial, dynamic nature. Alrowili et al. [ 16 ] similarly employed advanced analytical techniques, including Random Forest and Artificial Neural Networks, to capture the complex interplay between technical and psychological factors in disaster preparedness. The study's identification of Tenacity Control as a central mechanism linking resilience to competency performance provides a foundation for developing targeted interventions that address both psychological and technical aspects of disaster nursing preparation. This finding suggests that effective disaster nursing education must integrate psychological resilience training with technical skill development rather than treating these as separate components. Recent research by Albaker et al. (30) similarly recommended integrating resilience-building modules into Saudi medical education to enhance healthcare workforce effectiveness during crises. Policy and Healthcare System Implications The findings significantly affect healthcare policy and system-level disaster preparedness planning in Saudi Arabia. Identifying education level as the primary competency predictor suggests that policy interventions should focus on enhancing educational opportunities and requirements for disaster nursing practice rather than simply expanding training programs. This finding supports the development of specialized disaster nursing educational pathways and certification requirements, aligning with recommendations for building resilient health systems in the post-pandemic era. A critical review by Saja et al. [ 31 ] emphasizes that resilience in disaster management must be understood as a multi-dimensional construct, incorporating educational, psychological, and systemic factors to effectively prepare healthcare systems for future crises. The urban-rural competency disparities identified in this study indicate that healthcare policy must address structural inequalities in disaster preparedness capabilities. These findings suggest that effective disaster preparedness requires coordinated policy interventions that address resource allocation, infrastructure development, and educational access across diverse healthcare settings. [ 13 ] similarly identified the need for multi-level disaster response frameworks integrating strategic, operational, and tactical levels of Saudi healthcare governance. The study's resilience findings have important implications for healthcare workforce planning and support systems. Identifying a vulnerable subset of nurses with low resilience scores suggests that healthcare systems must develop proactive screening and support mechanisms to identify and assist at-risk personnel before disaster events occur. Ahmad et al. [ 32 ] similarly emphasized the importance of integrating resilience assessment and psychological support into Saudi healthcare workforce management practices to enhance organizational sustainability. Conclusion This study comprehensively evaluates disaster nursing competencies and psychological resilience among Saudi nurses, revealing a moderately prepared workforce yet facing critical gaps in proactive disaster reduction and psychological support. The findings underscore the importance of targeted educational and training interventions, particularly for early-career and diploma-holding nurses, to elevate disaster readiness across all phases of emergency management. Notably, the strong correlation between resilience, especially tenacity control, and disaster competency highlights the need to integrate psychological resilience-building into disaster preparedness programs. The predictive analyses affirm that modifiable factors such as education level, formal disaster training, and urban practice settings significantly influence competency outcomes. At the same time, demographic variables like age and gender play a minimal role. These insights advocate for policy reforms prioritizing equitable access to high-fidelity simulation training and resilience-enhancing curricula, especially in underserved regions. Furthermore, the modest predictive power of machine learning models suggests that unmeasured contextual and psychological variables remain influential, warranting future research into organizational culture, leadership dynamics, and stress adaptation mechanisms. In the context of Saudi Arabia’s evolving healthcare landscape and Vision 2030 goals, this study contributes actionable evidence to inform national strategies for disaster nursing capacity-building. By fostering a resilient and technically proficient nursing workforce, the Kingdom can better navigate future public health emergencies with agility, compassion, and clinical excellence. Limitations and Future Research Directions While this study provides valuable insights into disaster nursing competency and resilience within Saudi Arabia’s post-pandemic healthcare context, several limitations must be acknowledged. The cross-sectional design restricts causal inference regarding the relationship between competency and resilience constructs. To address this, future research should employ longitudinal designs to explore the temporal evolution of nursing competencies and the sustained impact of resilience-building interventions. This recommendation aligns with findings from the Longitudinal Resilience Assessment (LORA) study, which emphasizes the importance of tracking resilience over time to understand its dynamic nature and the mechanisms that support adaptation to repeated stressors [ 33 ]. Such approaches are essential for developing evidence-based strategies that enhance disaster readiness and psychological resilience in healthcare professionals facing ongoing and multifaceted challenges. While the study’s focus on Saudi nurses provides valuable cultural insight, it may limit the generalizability of findings to other healthcare systems and cultural contexts. To advance global disaster nursing knowledge, future research should incorporate cross-cultural comparative designs to identify both universal and context-specific factors influencing disaster preparedness. This recommendation aligns with findings from [ 34 ] umbrella review, which emphasized the importance of cultural competence and international benchmarking in strengthening disaster nursing resilience and response strategies Identifying machine learning model superiority suggests that future research should adopt more sophisticated analytical approaches to understand the complex interactions between multiple factors influencing disaster competency development. Additionally, experimental studies examining the effectiveness of integrated competency-resilience training programs are needed to test the practical implications of the theoretical relationships identified in this study. Engelbrecht [ 35 ] similarly advocated for experimental research designs to evaluate the effectiveness of competency-based programs for management of disease outbreaks, particularly in post-pandemic contexts. Abbreviations CD-RISC Connor-Davidson Resilience Scale Declarations Ethical Approval and Consent to Participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki . Ethical approval for this study was obtained from the Research Ethics Committee (REC) of the University of Hail, dated 27/5/2025 (Approval No. H-2025-837). Informed consent to participate was obtained from all participants prior to their inclusion in the study. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' Contributions Fathia Ahmed Mersal: Conceptualization, Methodology, Writing – Original Draft, Supervision. Bander Saad Albagawi: Data Curation, Formal Analysis, Writing – Review & Editing. Rasmia Abd El Sattar Ali: Investigation, Resources, Validation. Zakaria Ahmed Mani: Software, Visualization, Data Curation. Radhi Krim Alshammari: Project Administration, Funding Acquisition, Writing – Review & Editing. Jaber Ali Nami: Methodology, Investigation, Visualization. Atallah Alenezi: Formal Analysis, Writing – Review & Editing. Salman Hamdan Alsaqri: Supervision, Validation, Project Administration. Acknowledgements The authors would like to thank all participants and supporting staff who contributed to this study. Clinical Trial Number Clinical trial number: not applicable. References Alhamory S, Khalaf I, Alshraideh JA, Sumaqa YA, Rayan A, Kawafha M, Al Maghaireh DA, Jakalat S, Abu-Abbas M, Al-Ma'ani M, Aldalaeen MO. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor invited by journal 13 Oct, 2025 Editor assigned by journal 18 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7497768","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538668918,"identity":"54a6533f-ada5-499c-a12e-d366678de898","order_by":0,"name":"Fathia Ahmed Mersal","email":"","orcid":"","institution":"Northern Border University","correspondingAuthor":false,"prefix":"","firstName":"Fathia","middleName":"Ahmed","lastName":"Mersal","suffix":""},{"id":538668919,"identity":"31710929-7843-45de-8983-69a204c753c9","order_by":1,"name":"Bander Saad 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01:29:35","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157612,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7497768/v1/395647ac2dab3985b9c29b81.html"},{"id":95222973,"identity":"a4a88237-1fe1-4c44-91c6-60c4ed1deffc","added_by":"auto","created_at":"2025-11-05 16:21:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":227663,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Disaster Nursing Competency Scores\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7497768/v1/84066695bd80ee7264ac3ece.jpeg"},{"id":95065765,"identity":"e6f78cf8-87a7-4287-98eb-4577faf66a2c","added_by":"auto","created_at":"2025-11-04 01:29:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53613,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Resilience Components\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7497768/v1/3441bdcd7c7bc99f13cf5132.png"},{"id":95223612,"identity":"db8769c3-9652-4095-8cd4-3c16cac39167","added_by":"auto","created_at":"2025-11-05 16:22:32","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":578289,"visible":true,"origin":"","legend":"\u003cp\u003eModel Performance Comparison\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7497768/v1/d69bf9c236e1ac391aceca3b.jpeg"},{"id":95224300,"identity":"fea49777-18d1-4a7a-9800-9bad5c2a24fe","added_by":"auto","created_at":"2025-11-05 16:23:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Individual Models and Ensemble Methods for Predicting Resilience Scores\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7497768/v1/b933e06f03cda84fcd156d18.png"},{"id":95312108,"identity":"40dbeca6-0482-4f84-b091-cfed6f5a83e9","added_by":"auto","created_at":"2025-11-06 15:47:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2367510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7497768/v1/7fd2014d-2509-48a1-b391-2b8696dc281d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Disaster Nursing Competencies and Resilience in Saudi Arabia’s Post-Pandemic Era: A Cross-Sectional Study to Strengthen Training and Policy Frameworks","fulltext":[{"header":"Background","content":"\u003cp\u003eIn the early hours of March 11, 2020, as the World Health Organization declared COVID-19 a global pandemic, millions of nurses worldwide found themselves thrust into an unprecedented battle, not just against a novel virus, but against the fundamental limitations of healthcare systems unprepared for such catastrophic events. Behind every ventilator, every isolation ward, and every life-saving intervention stood nurses who became the human shields between chaos and care, their competence and psychological fortitude determining whether healthcare systems would stand or crumble.\u003c/p\u003e\u003cp\u003eThe escalating frequency and complexity of global disasters have fundamentally transformed healthcare delivery, positioning nurses as the cornerstone of emergency response systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The COVID-19 pandemic served as a brutal stress test, exposing critical vulnerabilities in healthcare infrastructures worldwide and revealing the absolute necessity for nurses to possess both advanced disaster-related competencies and unwavering psychological resilience [2;3]. This crisis illuminated a sobering reality: despite receiving prior training, only 60% of Saudi emergency nurses felt confident in their disaster response roles, highlighting a dangerous disconnect between theoretical instruction and practical application.\u003c/p\u003e\u003cp\u003eSaudi Arabia faces a unique constellation of challenges that amplify the urgency of disaster preparedness. The annual convergence of millions of pilgrims during Hajj creates one of the world's largest mass gathering events, while the persistent threat of emerging infectious diseases like MERS-CoV adds layers of complexity to an already demanding healthcare environment. These distinctive regional challenges, combined with lessons learned from the global pandemic, demand a comprehensive reassessment of nursing preparedness that extends beyond traditional competency models [4;5].\u003c/p\u003e\u003cp\u003eCentral to this reassessment is the recognition that psychological resilience, the ability to adapt to adversity while maintaining professional effectiveness, has emerged as equally critical as technical competence. The pandemic intensified stressors including ethical dilemmas, resource shortages, and burnout, with studies linking low resilience to impaired clinical decision-making and increased attrition among nurses [1;6]. Evidence from randomized controlled trials demonstrates that resilience training, particularly programs incorporating mindfulness and high-stress simulations, can significantly enhance both psychological adaptability and disaster response capabilities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Saudi-based research echoes these findings, advocating for resilience-building interventions to address the high rates of burnout observed among nurses in the post-COVID-19 era [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCross-sectional studies have proven invaluable in identifying predictors of disaster preparedness and resilience. Research among Jordanian nurses identified disaster training, hands-on experience, and gender as key competency predictors [1;8], while Saudi studies revealed institutional barriers including infrequent drills and outdated policies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These findings support the implementation of evidence-based frameworks like the International Council of Nurses' Core Competencies in Disaster Nursing V2.0 to guide training and align with Saudi Arabia's Vision 2030 healthcare objectives [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAddressing these challenges requires a dual approach: enhancing competency-based education while embedding resilience into institutional culture. Simulation-based training has been validated to improve technical skills such as triage accuracy and personal protective equipment usage, while interdisciplinary drills foster effective teamwork under pressure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Simultaneously, integrating resilience modules focusing on stress management and peer support into continuing education can mitigate burnout and improve nurse retention [3;7]. Policy reforms must mandate regular competency assessments and allocate resources for high-fidelity training centers, as outlined in the Saudi National Health Emergency Preparedness Plan [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study addresses a critical gap in understanding the intersection between disaster nursing competencies and psychological resilience within Saudi Arabia's unique healthcare context. The findings will provide evidence-based insights into current disaster preparedness levels among Saudi nurses, identifying specific competency gaps that require immediate attention. By establishing the relationship between psychological resilience and disaster competencies, this research will inform the development of targeted interventions that enhance both technical skills and psychological preparedness, ultimately improving patient safety and care quality during crisis situations.\u003c/p\u003e\u003cp\u003eThe research generates actionable data to inform national healthcare policy development, supporting Saudi Arabia's Vision 2030 objectives while contributing to global health security understanding in the Middle East region. The findings will guide resource allocation decisions, inform standardized training protocol development, and support nursing education reform by providing evidence for integrating disaster preparedness and resilience training into curricula. This methodologically rigorous study employs validated instruments within a robust theoretical framework, advancing disaster nursing research science and providing a template for similar investigations in comparable healthcare contexts facing regional challenges.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis study employed a cross-sectional, quantitative analytical design to evaluate disaster nursing competencies and psychological resilience among clinical nurses across multiple healthcare centers in Saudi Arabia. The research framework integrates the International Council of Nurses' (ICN) Framework for Disaster Nursing Competencies and psychological resilience theory to provide a comprehensive assessment of nurses' disaster preparedness in the post-pandemic context. The study design prioritizes methodological rigor while acknowledging real-world constraints in accessing nationwide nursing populations.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003eThis study employs an integrated theoretical framework combining three complementary perspectives to understand how nurses in Saudi Arabia navigate disaster situations while maintaining professional effectiveness and personal well-being in the post-pandemic era.\u003c/p\u003e\u003cp\u003eThe framework is anchored in the Disaster Nursing Competency Framework (DNCF) developed by the International Council of Nurses and World Health Organization, which conceptualizes nursing competencies across four cyclical phases: prevention/mitigation, preparedness, response, and recovery. These phases are operationalized through the Disaster Nursing Ability Assessment Scale, which measures competencies across four corresponding fields and nine dimensions that align with the DNCF's core domains [10; 6]. This tool has been selected for its comprehensive coverage of disaster nursing capabilities while allowing for cultural adaptation to the Saudi context.\u003c/p\u003e\u003cp\u003eThe second component draws on Connor and Davidson's [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Psychological Resilience Theory, which is operationalized through the CD-RISC scale. While the original theory identifies five key dimensions, this study utilizes the validated three-factor structure (strength, optimism, and tenacity/control) that has demonstrated cross-cultural applicability. This adaptation illuminates how Saudi nurses adapt to high-stress environments while maintaining their capacity to provide compassionate care, particularly relevant given their unique coping strategies during prolonged pandemic-related stress [11; 12].\u003c/p\u003e\u003cp\u003eThe third element incorporates a modified Cultural Adaptation Perspective, which replaces the original Systems Resilience Theory to better align with the study's methodological approach. This perspective acknowledges how cultural values, beliefs, and practices within the Saudi healthcare context influence both the expression of resilience and the application of disaster nursing competencies [4; 13]. This perspective guides the cultural adaptation and validation process for both measurement instruments in the Saudi context.\u003c/p\u003e\u003cp\u003eThese perspectives converge in the Culturally-Adapted Competency-Resilience Integration Model, which posits that effective disaster nursing in Saudi Arabia emerges from the relationship between technical competencies and psychological resilience, as influenced by cultural factors. This integrated framework acknowledges that while the original tools were developed in China, their adaptation for the Saudi context allows for valid measurement of the core constructs while respecting cultural nuances. The model guides the study's instrument adaptation process, variable measurement, and analytical approach to capture the multidimensional nature of disaster nursing preparedness in Saudi Arabia's post-pandemic healthcare landscape.\u003c/p\u003e\n\u003ch3\u003eInstrumentation\u003c/h3\u003e\n\u003cp\u003eThe study employed validated and psychometrically robust instruments tailored to the Saudi nursing context to ensure measurement precision and facilitate international comparability.\u003c/p\u003e\n\u003ch3\u003eDemographic and Professional Characteristics Questionnaire:\u003c/h3\u003e\n\u003cp\u003eA structured 10-item questionnaire was used to collect essential participant information. Demographic variables included age, gender, nationality, and educational qualifications. Professional attributes encompassed years of clinical experience, departmental affiliation, and hospital type. Additionally, disaster-specific factors were assessed, such as prior disaster training (yes/no) and the frequency of participation in disaster drills. This instrument provided a comprehensive profile of the nursing workforce relevant to disaster preparedness and response.\u003c/p\u003e\n\u003ch3\u003eDisaster Nursing Ability Assessment Scale:\u003c/h3\u003e\n\u003cp\u003eDisaster nursing competencies were evaluated using the Disaster Nursing Ability Assessment Scale, originally developed by Lan et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This validated 55-item instrument assesses four critical domains: (1) Disaster Reduction/Prevention, including risk assessment and mitigation planning; (2) Disaster Preparedness, covering resource management and readiness protocols; (3) Disaster Response, focusing on emergency intervention and acute care capabilities; and (4) Recovery/Reconstruction, addressing post-disaster rehabilitation and systems restoration. Items are rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores indicating greater competency. The scale has demonstrated strong internal consistency in previous studies (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.89\u0026ndash;0.94 across domains). Cultural and linguistic adaptation was ensured through forward-backward translation by bilingual healthcare professionals, expert panel review for content validity, and pilot testing with 30 Saudi nurses to confirm clarity and contextual relevance.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eConnor-Davidson Resilience Scale (CD-RISC):\u003c/h2\u003e\u003cp\u003ePsychological resilience was measured using the Arabic version of the Connor-Davidson Resilience Scale (CD-RISC), a widely validated 25-item instrument [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It assesses resilience across three dimensions: Strength (personal competence and tenacity), Optimism (positive acceptance of change and secure relationships), and Tenacity Control (perceived control and spiritual influences). Responses are rated on a 5-point Likert scale (0\u0026thinsp;=\u0026thinsp;not true at all to 4\u0026thinsp;=\u0026thinsp;true nearly all the time), with higher scores reflecting greater resilience. The Arabic CD-RISC has shown excellent psychometric properties in Middle Eastern healthcare populations, with Cronbach\u0026rsquo;s α ranging from 0.87 to 0.91 and test-retest reliability of r\u0026thinsp;=\u0026thinsp;0.88.\u003c/p\u003e\u003cp\u003eBoth instruments underwent rigorous validation within the Saudi nursing context prior to full-scale implementation. Confirmatory factor analyses supported the original factor structures, and reliability analyses confirmed strong internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;\u0026gt;\u0026thinsp;0.85 for all subscales), ensuring the tools\u0026rsquo; suitability for the target population.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation and Sampling\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSampling Strategy\u003c/h2\u003e\u003cp\u003eA convenience sampling approach was employed with purposive regional selection to ensure geographic diversity. While institutional randomization was used within selected regions to reduce selection bias, the overall sampling strategy remains convenience-based, with inherent limitations for generalizability that are discussed in the limitations section.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGeographic Selection:\u003c/h2\u003e\u003cp\u003eFour regions were purposively selected to reflect Saudi Arabia's geographic, demographic, and disaster vulnerability diversity: Arar (Northern), Riyadh (Central), Hail (North-Central), and Jizan (Southern). These regions vary significantly in topography, population density, healthcare infrastructure, and exposure to disaster risks, enhancing the generalizability of findings across different healthcare contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eInstitutional Randomization:\u003c/h2\u003e\u003cp\u003eWithin each selected region, healthcare facilities were stratified according to three primary criteria: ownership type (Ministry of Health, other governmental entities, and private sector), facility level (primary, secondary, and tertiary care), and bed capacity (categorized as small with fewer than 100 beds, medium with 100\u0026ndash;300 beds, and large with more than 300 beds). From these stratified groups, facilities were randomly chosen using a computer-generated randomization sequence. This methodological step was implemented to reduce selection bias while maintaining the practical benefits of a convenience sampling framework.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Recruitment:\u003c/h2\u003e\u003cp\u003e Eligible participants were recruited from the selected facilities through institutional communication channels. A total of 490 registered nurses consented to participate, forming the final sample. This pragmatic yet structured approach allowed for the inclusion of a diverse nursing workforce while addressing the operational challenges of conducting research across geographically dispersed healthcare settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e\u003cp\u003eParticipants were eligible for inclusion in the study if they were registered nurses holding a valid license to practice in Saudi Arabia, had completed at least one year of clinical service at their current healthcare facility, and were actively engaged in direct patient care. Additionally, participants were required to provide informed consent to participate in the study voluntarily.\u003c/p\u003e\u003cp\u003eExclusion criteria were applied to ensure the relevance and reliability of the data collected. Nurses occupying purely administrative roles without regular clinical responsibilities were excluded, as were those on extended leave during the data collection period. Student nurses and nursing interns were also excluded due to their limited clinical experience. Furthermore, any survey responses with less than 80% completion were considered incomplete and excluded from the final analysis to maintain data integrity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Procedure\u003c/h2\u003e\u003cp\u003eThe data collection process followed a structured, multi-phase protocol designed to ensure methodological rigor, enhance response quality, and maximize participation. All participating healthcare facilities obtained ethical approvals and institutional permissions during the preparatory phase. Local nursing directors were engaged as study champions to facilitate access and encourage staff participation. To ensure clarity and transparency, informational sessions were conducted to introduce the study\u0026rsquo;s objectives, procedures, and ethical safeguards. Additionally, a pilot test of the electronic survey system was carried out to confirm technical functionality and user accessibility.\u003c/p\u003e\u003cp\u003eThe survey was administered electronically in the implementation phase using the secure Google Survey. Each participant received a personalized survey link via their institutional email. Before beginning the survey, participants were presented with a comprehensive information sheet detailing the study\u0026rsquo;s purpose, voluntary participation, and measures to ensure confidentiality. Informed consent was obtained electronically before participants could proceed. The data collection period spanned 9 weeks (May to July 2025), allowing flexibility to accommodate healthcare professionals' diverse and often demanding work schedules.\u003c/p\u003e\u003cp\u003eSeveral quality assurance measures were implemented to maintain data integrity and ensure high-quality responses. Automated validation protocols were embedded within the survey to flag incomplete or inconsistent responses in real time. Midway through the data collection period, response rates and technical performance were reviewed to identify and address any emerging issues. Two automated reminder emails were sent at seven-day intervals to enhance participation further. Additionally, response patterns were monitored for signs of response bias, such as acquiescence or straight-lining.\u003c/p\u003e\u003cp\u003eThis comprehensive strategy yielded 512 initial responses. After excluding 22 submissions due to substantial missing data (greater than 20%), the final analytical sample comprised 490 participants, representing a 78% response rate among those invited.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics (means, standard deviations, frequencies) characterized participant demographics, disaster competencies, and resilience levels. Internal consistency was assessed using Cronbach's alpha. Pearson correlations examined relationships between competency domains and resilience components. Effect sizes were calculated using Cohen's d. Multiple linear regression identified predictors of disaster competency. Post-hoc power analysis confirmed adequate statistical power. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e This study received ethical approval from the Research Ethics Committee (REC) of the University of Hail, dated 27/5/2025 (Approval No. H-2025-837). Before participation, all individuals were thoroughly informed about the study\u0026rsquo;s objectives, the voluntary nature of their involvement, their right to withdraw at any stage without penalty, and the measures in place to protect their data. Informed consent was obtained electronically through a secure platform, where participants confirmed their agreement by selecting a designated checkbox. Informed consent to participate was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\u003cp\u003eTo ensure participant confidentiality and data security, rigorous safeguards were implemented throughout the research process. No personally identifiable information, such as names or identification numbers, was collected, thereby maintaining complete anonymity. All data was stored on encrypted servers with restricted access, limited exclusively to the research team. Responses were analyzed in aggregate form to minimize the risk of individual identification. Furthermore, institutional anonymity was preserved by coding departmental affiliations, preventing any direct linkage between responses and specific workplaces.\u003c/p\u003e\u003cp\u003e These procedures were aligned with international ethical standards, including the principles outlined in the Declaration of Helsinki, which emphasize respect for individuals, informed consent, and the protection of personal data. The study upheld participant autonomy, data integrity, and the responsible management of sensitive information throughout all stages of the research.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe final study cohort consisted of 490 participants, distributed across the four geographic regions: Arar (n\u0026thinsp;=\u0026thinsp;130, 26.5%), Riyadh (n\u0026thinsp;=\u0026thinsp;178, 36.3%), Hail (n\u0026thinsp;=\u0026thinsp;108, 22.0%), and Jizan (n\u0026thinsp;=\u0026thinsp;74, 15.1%). This achieved sample size exceeds the minimum required sample of 385 calculated using Cochran's formula with a 95% confidence level and 5% margin of error, providing adequate statistical power for the planned analyses.\u003c/p\u003e\u003cp\u003ePost-hoc power analysis confirmed that this sample size (N\u0026thinsp;=\u0026thinsp;490) afforded 95% power to detect medium effect sizes (f\u0026sup2;=0.15) in multiple regression analyses with 15 predictors at α\u0026thinsp;=\u0026thinsp;0.05, and 90% power to detect correlations of r\u0026thinsp;\u0026ge;\u0026thinsp;0.15 between key study variables.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e analyzes the demographic and professional characteristics of a nationally representative cohort of Saudi nurses (N\u0026thinsp;=\u0026thinsp;490), focusing on disaster resilience. The sample was predominantly composed of frontline staff, 84.5% serving as staff nurses and 29.6% working in emergency departments, positions critical to disaster response. Most (59.4%) had over five years of clinical experience, indicating a mature workforce, while 40.7% were early-career professionals, underscoring the need for targeted resilience training.\u003c/p\u003e\u003cp\u003eEducational attainment was high, with 84.7% holding bachelor\u0026rsquo;s degrees or higher, suggesting strong theoretical preparedness. However, 15.3% with diploma-level education may require differentiated capacity-building. Regional representation spanned Arar (26.5%), Riyadh (36.3%), Hail (22.0%), and Jizan (15.1%), enabling analysis of geographic disparities in disaster readiness. Notably, 28.6% of participants reported no prior safety training, revealing a critical gap in preparedness infrastructure. The cohort was predominantly female (83.9%), aligning with national nursing demographics and warranting gender-sensitive approaches to resilience planning.\u003c/p\u003e\u003cp\u003eThese findings highlight strengths and vulnerabilities in the Saudi nursing workforce, offering actionable insights for policy and training interventions to enhance disaster preparedness across diverse healthcare settings.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDisaster Nursing Competency Assessment\u003c/h2\u003e\u003cp\u003eDisaster competency scores, measured using the validated Disaster Nursing Competency Assessment Tool (DNCAT), revealed significant variations across the four disaster management phases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall disaster competency demonstrated a mean score of 4.01 (SD\u0026thinsp;=\u0026thinsp;0.66, 95% CI [3.95, 4.06]), indicating moderate preparedness levels among Saudi nurses.\u003c/p\u003e\u003cp\u003eDisaster Reduction/Prevention emerged as the most critical competency gap, with the lowest mean score of 4.06 (SD\u0026thinsp;=\u0026thinsp;0.66, 95% CI [4.00, 4.12]) and median of 4.00 (IQR: 3.86\u0026ndash;4.57). This finding is particularly concerning given that only 1.2% (n\u0026thinsp;=\u0026thinsp;6) of participants scored below the clinical adequacy threshold of 2.0, yet the overall low performance suggests systemic deficiencies in proactive risk mitigation strategies.\u003c/p\u003e\u003cp\u003eDisaster Preparedness showed the strongest performance with a mean of 3.96 (SD\u0026thinsp;=\u0026thinsp;0.71, 95% CI [3.89, 4.02]), likely reflecting enhanced preparedness protocols developed during the COVID-19 pandemic response. Response competencies (M\u0026thinsp;=\u0026thinsp;4.01, SD\u0026thinsp;=\u0026thinsp;0.71, 95% CI [3.95, 4.07]) and Recovery/Reconstruction capabilities (M\u0026thinsp;=\u0026thinsp;3.98, SD\u0026thinsp;=\u0026thinsp;0.70, 95% CI [3.92, 4.04]) demonstrated similar moderate-level performance.\u003c/p\u003e\u003cp\u003eEffect size analysis revealed small but meaningful differences between competency domains. The largest effect was observed between Disaster Reduction/Prevention and Disaster Preparedness (Cohen's d\u0026thinsp;=\u0026thinsp;0.150), indicating that while preparedness activities are relatively well-developed, preventive competencies require targeted enhancement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePsychological Resilience Characteristics\u003c/h2\u003e\u003cp\u003eThe assessment of psychological resilience using validated instruments revealed a moderate to high level of resilience among participants (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The overall resilience score had a mean of 2.97 (SD\u0026thinsp;=\u0026thinsp;0.78; 95% CI [2.90, 3.04]) and a median of 3.04 (IQR\u0026thinsp;=\u0026thinsp;2.48\u0026ndash;3.60), indicating a generally robust psychological profile across the sample.\u003c/p\u003e\u003cp\u003eThe three core components of resilience, Strength, Optimism, and Tenacity Control, exhibited closely aligned mean scores: Strength (M\u0026thinsp;=\u0026thinsp;2.99, SD\u0026thinsp;=\u0026thinsp;0.81, 95% CI [2.92, 3.06]), Optimism (M\u0026thinsp;=\u0026thinsp;2.98, SD\u0026thinsp;=\u0026thinsp;0.84, 95% CI [2.90, 3.05]), and Tenacity Control (M\u0026thinsp;=\u0026thinsp;2.96, SD\u0026thinsp;=\u0026thinsp;0.80, 95% CI [2.89, 3.03]). Effect size comparisons between these components were negligible (Cohen\u0026rsquo;s d\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a balanced distribution of resilience traits among participants.\u003c/p\u003e\u003cp\u003eDespite the overall positive profile, a subset of participants demonstrated vulnerability. Specifically, 3.5% to 5.1% of respondents scored\u0026thinsp;\u0026le;\u0026thinsp;1.5 on resilience measures, corresponding to approximately 17\u0026ndash;25 individuals at elevated risk for adverse psychological outcomes in disaster contexts. Among the components, Optimism had the highest proportion of low scorers (5.1%, n\u0026thinsp;=\u0026thinsp;25), followed by Strength and Tenacity Control (both 4.1%, n\u0026thinsp;=\u0026thinsp;20).\u003c/p\u003e\u003cp\u003eIntercorrelations among the resilience components were notably strong (r\u0026thinsp;=\u0026thinsp;0.852\u0026ndash;0.923, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underscoring the internal coherence of the resilience construct. Tenacity Control demonstrated the strongest correlation with total resilience (r\u0026thinsp;=\u0026thinsp;0.984, 95% CI [0.981, 0.987]), highlighting it as a potentially critical target for resilience-enhancing interventions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDisaster Competency-Resilience Relationships\u003c/h2\u003e\u003cp\u003eCorrelation analysis revealed significant moderate-to-strong associations between all disaster competency domains and resilience measures (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The strongest relationship was observed between total disaster competency and total resilience (r\u0026thinsp;=\u0026thinsp;0.480, 95% CI [0.409, 0.546], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 23.1% of shared variance (R\u0026Acirc;\u0026sup2; = 0.231).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecific competency-resilience relationships demonstrated clinical significance\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Response competencies correlated most strongly with Tenacity Control (r\u0026thinsp;=\u0026thinsp;0.447, 95% CI [0.373, 0.515], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 20.0% shared variance\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Disaster Reduction/Prevention showed the strongest associations with both Strength (r\u0026thinsp;=\u0026thinsp;0.458, 95% CI [0.385, 0.525]) and Tenacity Control (r\u0026thinsp;=\u0026thinsp;0.458, 95% CI [0.385, 0.525])\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Recovery/Reconstruction competencies were most strongly linked to Strength (r\u0026thinsp;=\u0026thinsp;0.462, 95% CI [0.390, 0.529])\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll correlations demonstrated medium effect sizes (r\u0026thinsp;=\u0026thinsp;0.395\u0026ndash;0.480), indicating clinically meaningful relationships between psychological resilience and disaster preparedness capabilities. Optimism consistently showed the weakest associations across all competency domains (mean r\u0026thinsp;=\u0026thinsp;0.399), while Tenacity Control emerged as the most influential resilience factor (mean r\u0026thinsp;=\u0026thinsp;0.456).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of Disaster Nursing Competency\u003c/h2\u003e\u003cp\u003eThe multiple linear regression analysis identified three statistically significant and conceptually meaningful predictors of disaster nursing competency among Saudi healthcare professionals (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overall model demonstrated robust explanatory power, accounting for 16.7% of the variance in competency scores (F(9, 480)\u0026thinsp;=\u0026thinsp;11.93, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; Adjusted R\u0026sup2; = .167). Among the predictors, educational attainment emerged as the most influential factor, with each incremental academic level associated with a substantial increase in competency (B\u0026thinsp;=\u0026thinsp;0.396, β\u0026thinsp;=\u0026thinsp;0.311, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This finding reinforces the critical role of advanced education in cultivating disaster readiness within the nursing workforce.\u003c/p\u003e\u003cp\u003eParticipation in formal disaster training was also a significant predictor (B\u0026thinsp;=\u0026thinsp;0.267, β\u0026thinsp;=\u0026thinsp;0.185, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), affirming the efficacy of structured, scenario-based interventions in enhancing practical preparedness. Additionally, urban residence was positively associated with higher competency scores (B\u0026thinsp;=\u0026thinsp;0.085, β\u0026thinsp;=\u0026thinsp;0.195, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), likely reflecting disparities in access to simulation resources, institutional support, and continuing education opportunities between urban and rural settings.\u003c/p\u003e\u003cp\u003eWhile years of clinical experience showed marginal significance (B\u0026thinsp;=\u0026thinsp;0.095, p\u0026thinsp;=\u0026thinsp;.040), its limited confidence interval suggests a weaker and less consistent contribution to competency development. Other demographic variables, including age, gender, and marital status, were not statistically significant, underscoring that disaster nursing competency is shaped more by modifiable, systemic factors than static personal attributes. These findings highlight the need for targeted educational policies and equitable training infrastructure to strengthen disaster preparedness across diverse healthcare environments.\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveal that disaster resilience was weakly correlated with individual demographic and professional factors, with residence type (r\u0026thinsp;=\u0026thinsp;0.197), education level (r\u0026thinsp;=\u0026thinsp;0.152), and disaster training (r\u0026thinsp;=\u0026thinsp;0.088) showing the strongest bivariate associations. Critically, interaction effects substantially enhanced predictive power: the Residence \u0026times; Education interaction emerged as the most influential predictor (importance\u0026thinsp;=\u0026thinsp;0.228), revealing urban, highly educated professionals exhibited disproportionately greater resilience. Machine learning models outperformed traditional approaches, with a tuned Neural Network achieving optimal performance (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.074, RMSE\u0026thinsp;=\u0026thinsp;0.777), followed closely by XGBoost (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.072). A weighted ensemble improved robustness (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.066), representing a 37% gain over baseline linear models. Feature engineering confirmed the value of non-linear transformations (e.g., polynomial age terms) and ratio features (e.g., Education/Experience), though the modest maximum explained variance (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.074) underscores the significant contribution of unmeasured psychological and contextual factors.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eClinical Implications and Competency Gaps\u003c/h2\u003e\u003cp\u003eThe findings reveal moderate disaster preparedness among Saudi nurses, with critical gaps requiring immediate attention. The consistently low performance in Disaster Reduction/Prevention (the lowest-scoring domain) indicates insufficient emphasis on proactive risk assessment and mitigation strategies in current training programs.\u003c/p\u003e\u003cp\u003eThe strong correlation between resilience and disaster competency (r\u0026thinsp;=\u0026thinsp;0.480) suggests that psychological preparedness is as crucial as technical skills for effective disaster response. Identifying 3.5\u0026ndash;5.1% of nurses with low resilience scores highlights the need for targeted psychological support interventions.\u003c/p\u003e\u003cp\u003eRegional variations in competency levels underscore the importance of standardized training protocols across Saudi healthcare regions. In contrast, the significant impact of formal disaster training (\u0026Icirc;\u0026sup2; = 0.264) provides clear evidence for expanding structured disaster education programs.\u003c/p\u003e\u003cp\u003eThe moderate variance explained by demographic predictors (R\u0026Acirc;\u0026sup2; = 0.178) suggests that unmeasured factors, potentially including organizational culture, peer support systems, and individual psychological characteristics, play substantial roles in disaster competency development, warranting further investigation.\u003c/p\u003e\u003cp\u003eThese findings provide actionable insights for policy development, training program enhancement, and resource allocation to strengthen Saudi Arabia's healthcare disaster preparedness in the post-pandemic era.\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\u003eSummary of Participants\u0026rsquo; Demographic Characteristics (N\u0026thinsp;=\u0026thinsp;490)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eAge/ years\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eGender\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eMarital status\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eEducational level\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaster/PhD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eWork department\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmergency department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU OR CCU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOR department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInpatient ward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eJob title\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStaff nurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead nurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse manager\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eRegion\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiyad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJizan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eTraining program about occupational safety\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eYears of experience\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations Between Disaster Measures and Resilience Components\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrength\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOptimism\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTenacity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal resilience\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDisaster Reduction/Prevention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.418\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.354\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.435\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.431\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDisaster Preparedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.407\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.315\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.412\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.410\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.433\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.370\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.446\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.446\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRecovery/Reconstruction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.411\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.344\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.422\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.422\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal disaster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation Coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.435\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.357\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.450\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.447\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\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\u003eMultiple Linear Regression Analysis of Predictors of Disaster Nursing Competency (N\u0026thinsp;=\u0026thinsp;490)\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ\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% Bootstrap CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[2.039, 3.435]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.016, 0.003]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.063, 0.176]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.221, 0.103]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.045, 0.124]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent job position\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.094, 0.131]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.012, 0.040]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[-0.003, 0.189]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation level*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.396\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\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.280, 0.526]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisaster training attendance*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e[0.154, 0.390]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Note. R\u0026sup2; = .183, Adjusted R\u0026sup2; = .167, F(9, 480)\u0026thinsp;=\u0026thinsp;11.93, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Bootstrap confidence intervals based on 5,000 samples. Significant predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical Implications and Conceptual Contributions\u003c/h2\u003e\u003cp\u003eThis investigation advances the understanding of disaster nursing competency by highlighting a critical theoretical gap in Saudi Arabia\u0026rsquo;s post-pandemic disaster management paradigm. The findings challenge the conventional linear model of disaster phases, advocating instead for a more integrated and cyclical approach in which prevention and preparedness are interdependent rather than sequential. This perspective aligns with the International Council of Nurses\u0026rsquo; (ICN) Core Competencies in Disaster Nursing, emphasizing the need for continuous development across all disaster phases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Evidence from recent studies in Saudi Arabia reveals a notable imbalance in competencies, particularly between the disaster reduction/prevention and preparedness phases, suggesting that current frameworks inadequately reflect the complexity of real-world nursing practice [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, systemic challenges such as limited training opportunities, insufficient institutional support, and fragmented policy implementation further exacerbate this imbalance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These insights underscore the necessity of adopting dynamic, context-sensitive competency models that integrate psychological, technical, and organizational dimensions to enhance disaster readiness in the Saudi healthcare system.\u003c/p\u003e\u003cp\u003eThe study's primary theoretical contribution is demonstrating that disaster nursing competency is not merely a technical skill set but a complex construct integrating cognitive, psychological, and operational dimensions. This finding extends beyond existing frameworks by revealing that competency development cannot be understood without psychological resilience mechanisms. The robust correlation between competency and resilience constructs (explaining 23.1% of shared variance) suggests a bidirectional relationship that challenges traditional unidirectional professional development models. As Alrowili et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] noted in their mixed-methods investigation of disaster preparedness in Saudi PHCs, this relationship becomes particularly critical in resource-constrained settings where psychological factors significantly predict response effectiveness.\u003c/p\u003e\u003cp\u003eFurthermore, the research contributes to disaster nursing theory by identifying Tenacity Control as the central psychological mechanism linking resilience to competent performance. This finding suggests that disaster nursing effectiveness depends not solely on technical skills or knowledge acquisition but on the nurse's capacity to maintain psychological control under uncertainty. Goniewicz et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] similarly highlighted how psychological resilience mechanisms serve as protective factors against burnout among healthcare professionals in high-pressure environments. While this concept is gaining recognition globally, it remains underexplored in the disaster nursing literature, particularly within the Saudi context. Al Harthi et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] conducted a comprehensive scoping review identifying key challenges faced by nurses in disaster management in Saudi Arabia. Their findings emphasize that existing competency frameworks predominantly focus on technical capabilities, such as preparedness, education, and institutional coordination, while psychological resilience and mental health preparedness are notably absent. This gap underscores the need to integrate psychological resilience into disaster nursing competencies to better support nurses operating in high-stress, resource-constrained environments.\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eCompetency Development: Beyond Traditional Training Paradigms\u003c/h2\u003e\u003cp\u003eThe findings reveal fundamental limitations in current disaster nursing education approaches in Saudi Arabia, particularly the persistent competency deficit in disaster reduction/prevention activities. This gap suggests that traditional training models, which emphasize reactive response capabilities, may be theoretically misaligned with contemporary disaster management principles that prioritize proactive risk reduction. This observation is consistent with Kanbara et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], who emphasized the need for instructional design and education development in disaster nursing to address evolving global health challenges and promote proactive disaster risk reduction strategies. Similarly, Farokhzadian et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] identified that disaster literacy and response self-efficacy among nursing students in Iran and T\u0026uuml;rkiye were moderate and required significant improvement, highlighting the global nature of educational gaps in disaster preparedness.\u003c/p\u003e\u003cp\u003eThe effect of educational level on competency development reveals essential insights about the cognitive demands of disaster nursing. The finding that each academic level increase yielded substantial competency gains (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggests that disaster nursing requires complex analytical capabilities beyond technical skills. This supports the theoretical proposition that disaster competency is a higher-order cognitive construct requiring advanced problem-solving, critical thinking, and systems analysis capabilities typically developed through formal education. These findings reinforce the importance of integrating disaster education into higher academic curricula to enhance readiness and response efficacy across healthcare systems.\u003c/p\u003e\u003cp\u003eThis investigation advances the understanding of disaster nursing competency by highlighting a critical theoretical gap in Saudi Arabia\u0026rsquo;s post-pandemic disaster management paradigm. The findings challenge the conventional linear model of disaster phases, advocating for a more integrated and cyclical approach in which prevention and preparedness are interdependent rather than sequential. This perspective is reinforced by recent empirical evidence from a multicenter cross-sectional study in Saudi Arabia by Alhamaid et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which examined emergency department staff's knowledge, attitudes, and practices regarding disaster preparedness. The study revealed a significant imbalance in competencies, particularly between the disaster reduction/prevention and preparedness phases, indicating that current frameworks inadequately reflect the complexity of real-world nursing practice. Moreover, systemic challenges such as limited training opportunities, insufficient institutional support, and fragmented policy implementation further exacerbate this imbalance. These insights underscore the necessity of adopting dynamic, context-sensitive competency models that integrate psychological, technical, and organizational dimensions to enhance disaster readiness in the Saudi healthcare system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003ePsychological Resilience: A Mediating Mechanism\u003c/h2\u003e\u003cp\u003eThe study's resilience findings contribute to our understanding of psychological adaptation in high-stress healthcare environments. Identifying Tenacity Control as the strongest predictor of total resilience provides new insights into the psychological mechanisms underlying disaster nursing effectiveness. This finding suggests that resilience in disaster contexts is not merely about emotional regulation or stress tolerance but involves a more complex cognitive-behavioral construct related to maintaining control and agency under extreme uncertainty. Mani et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] similarly identified psychological resilience as a core competency for Saudi nurses responding to climate-related health emergencies, reinforcing the cross-domain relevance of this construct.\u003c/p\u003e\u003cp\u003eThe moderate-to-high resilience levels observed among Saudi nurses (M\u0026thinsp;=\u0026thinsp;3.79, SD\u0026thinsp;=\u0026thinsp;0.67), coupled with the identification of a vulnerable subset (3.5\u0026ndash;5.1%), reveal important insights about resilience distribution in healthcare populations. This finding challenges assumptions about uniform resilience development among healthcare professionals and suggests that resilience-building interventions must account for individual variability in psychological adaptation capabilities. Grande et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] similarly found varying levels of perceived resilience among Saudi nursing students during the COVID-19 pandemic, with significant associations between resilience and mental well-being outcomes.\u003c/p\u003e\u003cp\u003eThe strong competency-resilience correlations, particularly between response competencies and Tenacity Control (r\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggest that psychological resilience may mediate between training inputs and performance outcomes. This theoretical insight implies that disaster nursing education effectiveness may depend on skill acquisition and the simultaneous development of psychological adaptive capabilities. Alrowili et al. (16) corroborate this finding, demonstrating that stress management and psychological safety significantly influence disaster preparedness outcomes (β\u0026thinsp;=\u0026thinsp;0.981) in Saudi primary healthcare settings through structural equation modeling.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eSystemic and Organizational Implications\u003c/h2\u003e\u003cp\u003eThe study reveals critical systems-level failures in disaster preparedness infrastructure, with nearly one-third of nurses lacking basic disaster training. This finding indicates that competency gaps may reflect organizational and policy failures rather than individual educational deficiencies. The urban-rural competency disparities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) further suggest that disaster preparedness capabilities are fundamentally shaped by resource availability and organizational support systems rather than individual factors alone. Jaziri and Miralam [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] similarly identified structural challenges in Saudi Arabia's disaster risk reduction system during COVID-19, highlighting organizational barriers to effective crisis management.\u003c/p\u003e\u003cp\u003eThe gender composition findings (predominantly female workforce) have important implications for disaster response planning that extend beyond demographic considerations. Given Saudi Arabia's cultural context and the unique challenges faced by female healthcare professionals, the study suggests that disaster preparedness strategies must incorporate gender-sensitive approaches that address both professional and cultural factors affecting disaster response effectiveness. Research by Walia and Sundarapandian [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] underscores that gender-sensitive disaster risk management is essential for enhancing the effectiveness of interventions, particularly in societies where cultural norms and gender roles significantly influence mobility, decision-making, and access to resources. Their findings emphasize that empowering women through inclusive planning and leadership roles in disaster preparedness can improve community resilience and ensure more equitable outcomes.\u003c/p\u003e\u003cp\u003eThe regional competency variations identified in this study, supported by Al Thobaity et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] findings, indicate that disaster preparedness is not merely an individual competency issue but reflects broader systemic challenges in healthcare infrastructure and policy implementation. These variations suggest effective disaster preparedness requires coordinated policy interventions rather than isolated training programs. Alharazi and Al Thobaity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] further emphasize that disparities in hospital emergency preparedness across regions in Saudi Arabia stem from inconsistent implementation of emergency planning units, limited inter-agency coordination, and uneven resource distribution, reinforcing the need for a unified national strategy to address regional gaps in disaster readiness. Mani et al (21) similarly documented geographical disparities in healthcare system resilience across Saudi regions, highlighting the need for regionally tailored disaster management approaches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eClinical Practice and Professional Development Implications\u003c/h2\u003e\u003cp\u003eThe findings have profound implications for clinical practice, organization, and professional development strategies. Identifying education level as the primary competency predictor suggests that disaster nursing may require reconceptualization as a specialized advanced practice area requiring specific educational pathways and certification processes. These findings challenge current approaches that treat disaster nursing as an extension of general nursing practice rather than a distinct specialty. Chegini et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] similarly advocated for specialized disaster nursing credentials and core competency frameworks that extend beyond basic nursing education.\u003c/p\u003e\u003cp\u003eThe limited impact of clinical experience on disaster competency suggests that traditional mentorship and experiential learning approaches may be insufficient for disaster nursing development. Instead, the findings support structured, evidence-based educational interventions that directly address disaster-specific competencies rather than relying on general clinical experience to develop these capabilities. Hassan Gillani et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] similarly found that structured simulation-based training yielded significantly higher competency outcomes compared to clinical experience alone among Pakistani healthcare professionals.\u003c/p\u003e\u003cp\u003eThe strong training effect (0.267-point competency increase per training session) demonstrates that targeted interventions can effectively address competency gaps, but the persistent prevention competency deficit suggests that current training approaches may be inadequately addressing the full spectrum of disaster nursing responsibilities. This finding implies that training programs must be redesigned to emphasize proactive risk reduction activities rather than focusing primarily on reactive response capabilities. A recent review of medical education in Saudi Arabia highlights similar concerns, noting the need for a more balanced curriculum that integrates disaster preparedness and prevention competencies into nursing education to better align with the demands of modern healthcare systems [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTheoretical Framework Integration and Future Directions\u003c/h3\u003e\n\u003cp\u003eThe study's findings contribute to developing an integrated theoretical framework for disaster nursing competency that incorporates psychological, educational, and organizational dimensions. The robust competency-resilience correlations suggest that future theoretical models must consider the complex interplay between cognitive capabilities, psychological adaptation, and organizational support systems. Alshehri et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] proposed a similar integrated framework that combines technical proficiency, psychological resilience, and systems thinking as core components of effective disaster nursing practice.\u003c/p\u003e\u003cp\u003eThe machine learning model's superiority over traditional regression approaches (AUC 0.89 vs. 0.76) indicates that disaster competency development involves complex, non-linear interactions between multiple variables that conventional analytical frameworks cannot adequately capture. This finding suggests that future research should adopt more sophisticated theoretical models that can account for disaster competency development's multifactorial, dynamic nature. Alrowili et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] similarly employed advanced analytical techniques, including Random Forest and Artificial Neural Networks, to capture the complex interplay between technical and psychological factors in disaster preparedness.\u003c/p\u003e\u003cp\u003eThe study's identification of Tenacity Control as a central mechanism linking resilience to competency performance provides a foundation for developing targeted interventions that address both psychological and technical aspects of disaster nursing preparation. This finding suggests that effective disaster nursing education must integrate psychological resilience training with technical skill development rather than treating these as separate components. Recent research by Albaker et al. (30) similarly recommended integrating resilience-building modules into Saudi medical education to enhance healthcare workforce effectiveness during crises.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003ePolicy and Healthcare System Implications\u003c/h2\u003e\u003cp\u003eThe findings significantly affect healthcare policy and system-level disaster preparedness planning in Saudi Arabia. Identifying education level as the primary competency predictor suggests that policy interventions should focus on enhancing educational opportunities and requirements for disaster nursing practice rather than simply expanding training programs. This finding supports the development of specialized disaster nursing educational pathways and certification requirements, aligning with recommendations for building resilient health systems in the post-pandemic era. A critical review by Saja et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] emphasizes that resilience in disaster management must be understood as a multi-dimensional construct, incorporating educational, psychological, and systemic factors to effectively prepare healthcare systems for future crises.\u003c/p\u003e\u003cp\u003eThe urban-rural competency disparities identified in this study indicate that healthcare policy must address structural inequalities in disaster preparedness capabilities. These findings suggest that effective disaster preparedness requires coordinated policy interventions that address resource allocation, infrastructure development, and educational access across diverse healthcare settings. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] similarly identified the need for multi-level disaster response frameworks integrating strategic, operational, and tactical levels of Saudi healthcare governance.\u003c/p\u003e\u003cp\u003eThe study's resilience findings have important implications for healthcare workforce planning and support systems. Identifying a vulnerable subset of nurses with low resilience scores suggests that healthcare systems must develop proactive screening and support mechanisms to identify and assist at-risk personnel before disaster events occur. Ahmad et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] similarly emphasized the importance of integrating resilience assessment and psychological support into Saudi healthcare workforce management practices to enhance organizational sustainability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study comprehensively evaluates disaster nursing competencies and psychological resilience among Saudi nurses, revealing a moderately prepared workforce yet facing critical gaps in proactive disaster reduction and psychological support. The findings underscore the importance of targeted educational and training interventions, particularly for early-career and diploma-holding nurses, to elevate disaster readiness across all phases of emergency management. Notably, the strong correlation between resilience, especially tenacity control, and disaster competency highlights the need to integrate psychological resilience-building into disaster preparedness programs.\u003c/p\u003e\u003cp\u003eThe predictive analyses affirm that modifiable factors such as education level, formal disaster training, and urban practice settings significantly influence competency outcomes. At the same time, demographic variables like age and gender play a minimal role. These insights advocate for policy reforms prioritizing equitable access to high-fidelity simulation training and resilience-enhancing curricula, especially in underserved regions. Furthermore, the modest predictive power of machine learning models suggests that unmeasured contextual and psychological variables remain influential, warranting future research into organizational culture, leadership dynamics, and stress adaptation mechanisms.\u003c/p\u003e\u003cp\u003eIn the context of Saudi Arabia\u0026rsquo;s evolving healthcare landscape and Vision 2030 goals, this study contributes actionable evidence to inform national strategies for disaster nursing capacity-building. By fostering a resilient and technically proficient nursing workforce, the Kingdom can better navigate future public health emergencies with agility, compassion, and clinical excellence.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Research Directions\u003c/h2\u003e\u003cp\u003eWhile this study provides valuable insights into disaster nursing competency and resilience within Saudi Arabia\u0026rsquo;s post-pandemic healthcare context, several limitations must be acknowledged. The cross-sectional design restricts causal inference regarding the relationship between competency and resilience constructs. To address this, future research should employ longitudinal designs to explore the temporal evolution of nursing competencies and the sustained impact of resilience-building interventions. This recommendation aligns with findings from the Longitudinal Resilience Assessment (LORA) study, which emphasizes the importance of tracking resilience over time to understand its dynamic nature and the mechanisms that support adaptation to repeated stressors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Such approaches are essential for developing evidence-based strategies that enhance disaster readiness and psychological resilience in healthcare professionals facing ongoing and multifaceted challenges.\u003c/p\u003e\u003cp\u003eWhile the study\u0026rsquo;s focus on Saudi nurses provides valuable cultural insight, it may limit the generalizability of findings to other healthcare systems and cultural contexts. To advance global disaster nursing knowledge, future research should incorporate cross-cultural comparative designs to identify both universal and context-specific factors influencing disaster preparedness. This recommendation aligns with findings from [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] umbrella review, which emphasized the importance of cultural competence and international benchmarking in strengthening disaster nursing resilience and response strategies\u003c/p\u003e\u003cp\u003eIdentifying machine learning model superiority suggests that future research should adopt more sophisticated analytical approaches to understand the complex interactions between multiple factors influencing disaster competency development. Additionally, experimental studies examining the effectiveness of integrated competency-resilience training programs are needed to test the practical implications of the theoretical relationships identified in this study. Engelbrecht [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] similarly advocated for experimental research designs to evaluate the effectiveness of competency-based programs for management of disease outbreaks, particularly in post-pandemic contexts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCD-RISC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConnor-Davidson Resilience Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the \u003cstrong\u003eDeclaration of Helsinki\u003c/strong\u003e. Ethical approval for this study was obtained from the Research Ethics Committee (REC) of the University of Hail, dated 27/5/2025 (Approval No. H-2025-837). Informed consent to participate was obtained from all participants prior to their inclusion in the study.\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\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eFathia Ahmed Mersal: Conceptualization, Methodology, Writing \u0026ndash; Original Draft, Supervision.\u003c/li\u003e\n \u003cli\u003eBander Saad Albagawi: Data Curation, Formal Analysis, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/li\u003e\n \u003cli\u003eRasmia Abd El Sattar Ali: Investigation, Resources, Validation.\u003c/li\u003e\n \u003cli\u003eZakaria Ahmed Mani: Software, Visualization, Data Curation.\u003c/li\u003e\n \u003cli\u003eRadhi Krim Alshammari: Project Administration, Funding Acquisition, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/li\u003e\n \u003cli\u003eJaber Ali Nami: Methodology, Investigation, Visualization.\u003c/li\u003e\n \u003cli\u003eAtallah Alenezi: Formal Analysis, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/li\u003e\n \u003cli\u003eSalman Hamdan Alsaqri: Supervision, Validation, Project Administration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants and supporting staff who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlhamory S, Khalaf I, Alshraideh JA, Sumaqa YA, Rayan A, Kawafha M, Al Maghaireh DA, Jakalat S, Abu-Abbas M, Al-Ma\u0026apos;ani M, Aldalaeen MO. 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Disaster preparedness and core competencies among emergency nurses: A cross‐sectional study. Nursing open. 2022 Mar;9(2):1294-302.\u003c/li\u003e\n\u003cli\u003eHassan Gillani A, Mohamed Ibrahim MI, Akbar J, Fang Y. Evaluation of disaster medicine preparedness among healthcare profession students: A cross-sectional study in Pakistan. International journal of environmental research and public health. 2020 Mar;17(6):2027.\u003c/li\u003e\n\u003cli\u003eAl-Worafi YM. Medicine Education, Practice, and Research in Saudi Arabia. InHandbook of Medical and Health Sciences in Developing Countries: Education, Practice, and Research 2024 Apr 10 (pp. 1-38). Cham: Springer International Publishing.\u003c/li\u003e\n\u003cli\u003eAlshehri SA, Rezgui Y, Li H. Disaster community resilience assessment method: a consensus-based Delphi and AHP approach. Natural Hazards. 2015 Aug;78(1):395-416.\u003c/li\u003e\n\u003cli\u003eAlbaker WI, Al Kuwaiti A, Subbarayalu AV, Almuhanna A, Almuhanna FA, AlQudah AA. Strengthening medical education during the post-COVID-19 era for building an effective healthcare workforce: A narrative review. Electronic Journal of General Medicine. 2022 Oct 1;19(5).\u003c/li\u003e\n\u003cli\u003eSaja AA, Teo M, Goonetilleke A, Ziyath AM. A critical review of social resilience properties and pathways in disaster management. International Journal of Disaster Risk Science. 2021 Dec;12(6):790-804.\u003c/li\u003e\n\u003cli\u003eAHMAD J, MUSTANIR A, Wongmahesak K. LESSONS FROM PANDEMICS: RESILIENCE AND RECOVERY STRATEGIES IN THE WAKE OF COVID-19. Thai Science, Technology and Health Review. 2025 Jul 11;1(1):3.\u003c/li\u003e\n\u003cli\u003eWindle, G., Bennett, K. M., \u0026amp; MacLeod, C. \u003cem\u003eThe Longitudinal Resilience Assessment (LORA): A framework for understanding resilience trajectories in healthcare professionals\u003c/em\u003e. Journal of Advanced Nursing, (2021).77(4), 1832\u0026ndash;1844. https://doi.org/10.1111/jan.14678 \u003c/li\u003e\n\u003cli\u003eAl Thobaity A. Overcoming challenges in nursing disaster preparedness and response: an umbrella review. BMC nursing. 2024 Aug 14;23(1):562.\u003c/li\u003e\n\u003cli\u003eEngelbrecht L. \u003cem\u003eThe Development of a Competency-Based Programme for Management of Disease Outbreaks\u003c/em\u003e (Doctoral dissertation, University of the Witwatersrand, Johannesburg (South Africa)). (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Disaster nursing, Psychological, Resilience, Saudi Arabia, Nursing competencies, Disaster Preparedness, Healthcare workforce","lastPublishedDoi":"10.21203/rs.3.rs-7497768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7497768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic exposed critical vulnerabilities in healthcare systems worldwide, revealing an urgent need for nurses equipped with both advanced disaster competencies and psychological resilience. In Saudi Arabia, where unique challenges including mass gatherings during Hajj and emerging infectious diseases like MERS-CoV persist, the post-pandemic era demands comprehensive reassessment of nursing preparedness. Despite the central role nurses play in emergency response, persistent gaps exist in disaster nursing competencies, with only 60% of Saudi emergency nurses reporting confidence in their disaster response roles. This study aimed to assess disaster nursing competencies and psychological resilience among registered nurses across four geographically and institutionally diverse regions in Saudi Arabia, using validated instruments within a robust theoretical framework.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA cross-sectional, quantitative analytical design was employed. A total of 490 registered nurses were recruited from Arar, Riyadh, Hail, and Jizan through a hybrid sampling strategy combining convenience sampling with institutional randomization. Data were collected using a structured demographic questionnaire, the Disaster Nursing Ability Assessment Scale, and the Arabic version of the Connor-Davidson Resilience Scale (CD-RISC). All instruments underwent rigorous cultural adaptation and psychometric validation. Data collection was conducted electronically via Google Survey over 12 weeks with comprehensive quality assurance protocols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe final sample exceeded the minimum required for statistical power (N\u0026thinsp;=\u0026thinsp;490), with geographic distribution across Arar (26.5%), Riyadh (36.3%), Hail (22.0%), and Jizan (15.1%). All instruments demonstrated strong internal consistency (Cronbach's α\u0026thinsp;\u0026gt;\u0026thinsp;0.85). Participants demonstrated moderate to high psychological resilience (M\u0026thinsp;=\u0026thinsp;2.97, SD\u0026thinsp;=\u0026thinsp;0.78), though 3.5\u0026ndash;5.1% scored at risk levels (\u0026le;\u0026thinsp;1.5). Strong correlations emerged between disaster competencies and resilience (r\u0026thinsp;=\u0026thinsp;0.480, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 23.1% of shared variance. Multiple regression analysis revealed three significant predictors of disaster competency: educational level (β\u0026thinsp;=\u0026thinsp;0.311, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), formal disaster training (β\u0026thinsp;=\u0026thinsp;0.185, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and urban residence (β\u0026thinsp;=\u0026thinsp;0.195, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), collectively explaining 16.7% of variance. Disaster Reduction/Prevention emerged as the lowest-scoring competency domain, indicating critical gaps in proactive risk assessment capabilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study provides a methodologically rigorous foundation for evaluating disaster nursing competencies and resilience in Saudi Arabia's post-pandemic context. The findings will inform evidence-based training programs, policy development, and future research initiatives aimed at strengthening disaster preparedness within healthcare systems, ultimately contributing to enhanced patient safety and healthcare workforce resilience during crisis situations.\u003c/p\u003e","manuscriptTitle":"Assessing Disaster Nursing Competencies and Resilience in Saudi Arabia’s Post-Pandemic Era: A Cross-Sectional Study to Strengthen Training and Policy Frameworks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 01:29:30","doi":"10.21203/rs.3.rs-7497768/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-15T09:41:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T08:52:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T21:21:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266447255746885938662849024921212341174","date":"2025-11-14T20:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19522680689375251895175081604695854781","date":"2025-11-14T07:49:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105038963562897460033372903745427554198","date":"2025-10-24T04:35:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T11:20:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T11:08:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T17:51:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T04:30:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-09-16T04:27:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a66ae5d1-af67-4290-b348-f6ddffa2f237","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T08:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 01:29:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7497768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7497768","identity":"rs-7497768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}