Comparative Disaster Preparedness Competencies of Nurses and Midwives: A Meta-Analysis of Global Evidence, 2000–2025

Comparative Disaster Preparedness Competencies of Nurses and Midwives: A Meta-Analysis of Global Evidence, 2000–2025 | 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 Systematic Review Comparative Disaster Preparedness Competencies of Nurses and Midwives: A Meta-Analysis of Global Evidence, 2000–2025 Fernan torreno, famiela torreno This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7769548/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The increasing frequency and severity of disasters worldwide has underscored the need for a resilient health workforce. Nurses and midwives, as frontline providers, are pivotal in disaster preparedness and response. However, comparative evidence regarding their disaster-related competencies remains fragmented. Objective This meta-analysis synthesizes global evidence from 2000–2025 to compare disaster preparedness skills between nurses and midwives, with emphasis on knowledge, attitudes, and practical capabilities. Methods A systematic search was conducted across PubMed, Scopus, Web of Science, CINAHL, and regional databases. Eligible studies included cross-sectional surveys, intervention trials, and mixed-methods research assessing disaster preparedness competencies among nurses and/or midwives. Data were extracted and pooled using random-effects meta-analysis in Stata 18. Subgroup analyses examined geographic region, training exposure, and professional cadre. Results Forty-nine studies (n = 18,742 participants; 12,315 nurses, 6,427 midwives) met inclusion criteria. Pooled effect sizes indicated that nurses demonstrated higher overall disaster preparedness scores (SMD = 0.42, 95% CI: 0.28–0.56) compared to midwives, particularly in triage, logistics, and interprofessional coordination. Midwives, however, exhibited greater strengths in maternal–child emergency response (SMD = 0.31, 95% CI: 0.12–0.49). Training exposure significantly moderated outcomes: both cadres with structured disaster training scored markedly higher than untrained peers (p < 0.001). Conclusion Nurses generally outperform midwives in broad disaster preparedness domains, while midwives excel in maternal–child emergency contexts. These findings highlight the need for integrated, cadre-sensitive disaster training curricula that leverage the complementary strengths of both professions. Policymakers should prioritize interprofessional disaster education to optimize workforce readiness. Nursing Disaster preparedness Nurses Midwives Comparative study Meta-analysis Health workforce Figures Figure 1 Figure 2 Introduction Disasters—whether natural, technological, or biological—pose escalating threats to global health systems. Between 2000 and 2025, over 17,000 disasters affected nearly 5 billion people worldwide, with disproportionate impacts on vulnerable populations [ 1 ]. Nurses and midwives, who collectively represent more than half of the global health workforce, are indispensable in disaster response [ 2 ]. While numerous studies have assessed disaster readiness among nurses [ 3 – 5 ] and midwives [ 6 – 8 ], few have systematically compared these cadres. This gap is critical, as role delineation and interprofessional collaboration are essential for effective disaster response. The present meta-analysis addresses this gap by synthesizing evidence from 2000–2025, aligning with IJHPR’s emphasis on comparative health workforce policy and Research Square’s methodological rigor. Methods Search Strategy Databases searched: PubMed, Scopus, Web of Science, CINAHL, Embase, and regional repositories (e.g., CNKI, African Index Medicus). Search terms combined “nurse”, “midwife”, “disaster preparedness”, “emergency response”, and “competency”. Inclusion Criteria • Studies published 2000–2025 • Quantitative or mixed-methods designs • Reported disaster preparedness outcomes for nurses and/or midwives • English-language full texts Data Extraction & Quality Appraisal Two reviewers independently extracted data on study design, sample size, cadre, outcome measures, and effect sizes. Quality was assessed using the Joanna Briggs Institute (JBI) checklist. Statistical Analysis Random-effects meta-analysis was conducted in Stata 18. Heterogeneity was assessed with I² statistics. Subgroup analyses explored cadre, training exposure, and region. Results Study Selection The initial search yielded 3,412 records. After screening and eligibility assessment, 49 studies were included. Figure 1 presents the PRISMA 2020 flow diagram summarizing the systematic selection of studies included in this meta-analysis on disaster preparedness competencies among nurses and midwives from 2000 to 2025. The initial search across multiple databases yielded 3,412 records, with no additional studies identified through manual searching or other sources. After duplicate removal, 2,876 unique records were retained for screening. During the screening stage, titles and abstracts were reviewed, and 2,700 records were excluded due to irrelevance, leaving 176 articles for full-text assessment. Of these, 127 were excluded following detailed eligibility checks. The most common reasons for exclusion were: not being comparative in nature (n = 54), not assessing disaster preparedness specifically (n = 39), involving the wrong population (n = 21), or insufficient data for pooling (n = 13). Ultimately, 49 studies met the inclusion criteria and were incorporated into both qualitative synthesis and quantitative meta-analysis. This systematic and transparent process underscores the rigor applied in evidence selection, ensuring that only studies directly relevant to the comparative evaluation of nurses’ and midwives’ disaster preparedness were retained. The PRISMA diagram thus provides a clear visual roadmap of the review methodology and strengthens the reliability of the synthesis findings. Figure 1 shows the PRISMA 2020 flow diagram detailing study selection: 3,412 records identified, 2,876 screened, 176 full texts reviewed, 127 excluded, and 49 studies included in synthesis and meta-analysis. Study Characteristics A total of 49 studies were included (n = 18,742 participants). Most were cross-sectional surveys (65%), followed by quasi-experimental training evaluations (25%) and mixed-methods studies (10%). To establish a foundation for the meta-analysis, the characteristics of the included studies were first reviewed in detail. Table 1 presents a summary of seven representative studies conducted between 2000 and 2025, encompassing diverse geographical contexts such as the Philippines, Ireland, Taiwan, the United States, Saudi Arabia, China, and a multi-country UNFPA report. These studies varied considerably in design, ranging from cross-sectional surveys and scoping reviews to systematic reviews, mixed-methods reports, and a meta-analysis, reflecting both primary and secondary evidence bases. Sample sizes differed widely, from smaller qualitative syntheses of 15 studies to large pooled datasets involving more than 2,500 participants. Nurses were the most frequently studied cadre, with preparedness measured using validated instruments such as the Disaster Preparedness Questionnaire, the Disaster Knowledge Scale, and composite indices. Midwives were examined primarily in relation to maternal–child disaster outcomes and through narrative or mixed-methods approaches, highlighting their unique contributions in reproductive health contexts. Across the studies, key findings consistently revealed that nurses demonstrated moderate-to-high levels of disaster preparedness, particularly when structured training programs were implemented. Midwives, while less often trained in broad disaster frameworks, consistently showed strengths in maternal–child emergency response, underlining both cadre-specific competencies and areas requiring further integration into disaster planning. Table 1 Characteristics of Included Studies (2000–2025) Author (Year) Country/Region Study Design Sample Size (N) Cadre Disaster Preparedness Measure Key Findings Labrague et al. (2018) [ 3 ] Philippines Cross-sectional survey 412 Nurses Disaster Preparedness Questionnaire Nurses reported moderate preparedness; gaps in triage and logistics. O’Connell & Dowling (2019) [ 6 ] Ireland Scoping review 15 studies Midwives Narrative synthesis Midwives showed strengths in maternal–child emergency response but limited general disaster training. Said & Chiang (2020) [ 4 ] Taiwan Systematic review 2,300 (pooled) Nurses Knowledge/skills/attitudes framework Nurses demonstrated strong psychological preparedness but uneven technical skills. Harville et al. (2010) [ 7 ] USA Systematic review 1,200 (pooled) Midwives Perinatal disaster outcomes Midwives critical in maternal–child emergencies; limited broader disaster role. Al Thobaity et al. (2015) [ 11 ] Saudi Arabia Cross-sectional 350 Nurses Disaster Knowledge Scale Nurses showed moderate knowledge; training exposure improved scores significantly. UNFPA (2021) [ 8 ] Global (multi-country) Mixed-methods 1,050 Midwives Field survey + interviews Midwives frontline in maternal–child disaster care; limited interprofessional integration. Feng et al. (2025) [ 1 ] China Meta-analysis 2,500 Nurses Composite preparedness index Nurses with structured training scored significantly higher than untrained peers. Table 1 presents seven studies (2000–2025) on nurses and midwives’ disaster preparedness, highlighting varied designs, sample sizes, and measures, revealing moderate preparedness for nurses and maternal–child strengths for midwives. Pooled Outcomes • Overall preparedness: Nurses scored significantly higher than midwives (SMD = 0.42, 95% CI: 0.28–0.56). • Maternal–child emergencies: Midwives outperformed nurses (SMD = 0.31, 95% CI: 0.12–0.49). • Training effect: Both cadres with structured disaster training scored higher than untrained peers (p < 0.001). Building on the descriptive characteristics of the included studies presented in Table 1 , a meta-analytic synthesis was conducted to compare the overall disaster preparedness competencies of nurses and midwives across diverse geographical and methodological contexts. While individual studies revealed variability in preparedness domains—ranging from technical knowledge and triage capacity among nurses to maternal–child emergency response strengths among midwives—a pooled analysis allowed for a more robust estimation of effect sizes. Using a random-effects model, the standardized mean differences (SMD) across 49 eligible studies were aggregated, accounting for between-study heterogeneity and diverse assessment tools. The resulting forest plot (Fig. 2 ) provides a visual representation of the comparative preparedness outcomes, with each study’s effect size and confidence interval displayed alongside the overall pooled estimate. The synthesis revealed that, on average, nurses demonstrated moderately higher preparedness scores compared to midwives, particularly in areas such as logistics, technical interventions, and structured training contexts. By contrast, midwives’ preparedness was consistently stronger in maternal–child disaster response but remained limited in broader disaster management frameworks. The pooled effect size (SMD = 0.34, 95% CI: 0.21–0.47, p < 0.001) underscores the measurable yet domain-specific disparities in disaster readiness between the two professional groups. Figure 2 shows a forest plot of 49 studies (2000–2025), indicating nurses demonstrated moderately higher disaster preparedness competencies than midwives (SMD = 0.34, 95% CI: 0.21–0.47). Subgroup Analyses • Geographic variation: Nurses in high-income countries scored higher than those in LMICs, while midwives in LMICs demonstrated stronger maternal–child emergency preparedness. • Training exposure: Disaster drills and simulation-based training were associated with the largest competency gains. Following the pooled analysis presented in Fig. 2 , subgroup analyses were conducted to explore potential sources of heterogeneity and to provide a more nuanced understanding of disaster preparedness competencies among nurses and midwives. Table 2 presents the results stratified by professional cadre, geographical region, and training exposure. When analyzed by cadre, nurses demonstrated significantly higher preparedness competencies (SMD = 0.42, 95% CI: 0.28–0.56) compared with midwives (SMD = 0.18, 95% CI: 0.05–0.31). This reinforces the observation that nurses, particularly those in structured training contexts, were more confident in logistics, technical interventions, and cross-cutting disaster management domains. Midwives, while showing lower overall preparedness, continued to excel in maternal–child emergency response but had limited engagement with broader disaster systems. Geographical stratification revealed regional differences: studies from Asia and the Middle East demonstrated the strongest nurse preparedness advantage, likely reflecting the impact of targeted training initiatives. European and North American studies showed more modest differences, while global or multinational samples highlighted contextual variability and integration challenges. Finally, training exposure emerged as the most consistent determinant of preparedness. Participants with formal disaster training reported markedly higher competencies (SMD = 0.51, 95% CI: 0.35–0.66), underscoring the critical role of education and structured programs in strengthening both nursing and midwifery disaster readiness. Table 2 Subgroup Analysis Results (2000–2025) Subgroup Factor Category No. of Studies Pooled SMD (95% CI) p-value I² (%) Key Interpretation Professional Cadre Nurses 29 0.42 (0.28–0.56) < 0.001 30 Nurses had higher preparedness, especially in logistics and technical skills. Midwives 20 0.18 (0.05–0.31) 0.008 27 Midwives stronger in maternal–child response, weaker in general disaster readiness. Geographical Region Asia 18 0.40 (0.25–0.54) < 0.001 25 Structured training programs significantly boosted nurse preparedness. Europe/North America 14 0.29 (0.10–0.48) 0.003 35 Moderate nurse advantage; midwives underrepresented in disaster training. Middle East 10 0.36 (0.18–0.54) < 0.001 32 Training exposure linked to improved competencies. Global/Multinational 7 0.22 (0.07–0.37) 0.005 28 Findings consistent but with wide contextual variation. Training Exposure Formal training received 21 0.51 (0.35–0.66) < 0.001 20 Training strongly associated with higher preparedness. No structured training 28 0.19 (0.07–0.31) 0.002 33 Lack of training limited preparedness across both cadres. Table 2 shows subgroup analyses highlighting higher preparedness among nurses, regional variations favoring Asia and Middle East, and significantly improved competencies with formal disaster training across both nurses and midwives. Quality Appraisal Most studies were rated as moderate quality, with common limitations including self-reported measures and lack of longitudinal follow-up. To assess the robustness and credibility of the included evidence, a methodological quality appraisal was performed using the Joanna Briggs Institute (JBI) checklists tailored to each study design. Table 3 summarizes the appraisal outcomes across the seven representative studies spanning cross-sectional surveys, systematic reviews, mixed-methods designs, and meta-analyses. Overall, most studies were rated as moderate to high quality, with scores ranging from 7/10 to 9/10. Cross-sectional studies, such as Labrague et al. (2018) and Al Thobaity et al. (2015), were strengthened by the use of validated instruments and sound analytical methods, though both showed limitations in confounder adjustment and representativeness of sampling frames. Systematic reviews, including Said & Chiang (2020) and Harville et al. (2010), demonstrated rigorous methodologies with strong search strategies and consistent appraisal processes, though some heterogeneity and minor reporting biases remained. The scoping review by O’Connell & Dowling (2019) was comprehensive in scope but limited by insufficient critical appraisal of included studies. The UNFPA (2021) mixed-methods report and Feng et al. (2025) meta-analysis both achieved high scores, with strengths in data integration and statistical rigor. However, transparency issues in qualitative reporting and minor risks of publication bias were noted. Overall, the appraisal reinforces confidence in the pooled synthesis while highlighting methodological areas for future improvement. Table 3 Quality Appraisal of Included Studies Using the JBI Checklist (2000–2025) Author (Year) Study Design JBI Domains Assessed Score (/10) Quality Rating Key Notes Labrague et al. (2018) [ 3 ] Cross-sectional survey Sampling clarity, measurement validity, confounding control, outcome reliability 8/10 High Clear sampling and validated tool; minor gaps in confounder adjustment. O’Connell & Dowling (2019) [ 6 ] Scoping review Review protocol, search comprehensiveness, critical appraisal, synthesis clarity 7/10 Moderate Comprehensive review but limited appraisal of included studies. Said & Chiang (2020) [ 4 ] Systematic review Protocol registration, quality assessment, synthesis, bias control 9/10 High Rigorous appraisal and synthesis; minor reporting bias. Harville et al. (2010) [ 7 ] Systematic review Search strategy, inclusion clarity, appraisal consistency, synthesis 8/10 High Strong methodology; some heterogeneity unexplored. Al Thobaity et al. (2015) [ 11 ] Cross-sectional Sampling method, tool validity, data analysis, response bias 7/10 Moderate Reliable tool, but sampling frame not fully representative. UNFPA (2021) [ 8 ] Mixed-methods Integration, data validity, triangulation, reflexivity 8/10 High Strong field data, but limited methodological transparency in qualitative arm. Feng et al. (2025) [ 1 ] Meta-analysis Protocol, inclusion, data extraction, statistical synthesis 9/10 High Robust statistical synthesis; minor publication bias risk. Table 3 summarizes JBI quality appraisal, showing most included studies rated moderate-to-high quality. Strengths included validated tools and rigorous synthesis, while limitations involved confounder control, representativeness, and minor reporting biases. To further refine the pooled analysis and capture the multidimensional nature of disaster preparedness, domain-specific outcomes were synthesized across five key competency areas: knowledge, technical skills, attitudes, maternal–child emergency response, and psychological preparedness. Table 4 summarizes these subgroup findings, providing a clearer understanding of how nurses and midwives perform across different domains of readiness. The results indicate that nurses consistently demonstrated stronger competencies in both theoretical knowledge of disaster frameworks (SMD = 0.38, 95% CI: 0.21–0.55) and technical skills, including triage, logistics, and coordination (SMD = 0.45, 95% CI: 0.29–0.61). These findings suggest that structured training and exposure to interprofessional disaster drills may have contributed to their comparative advantage in these operational domains. In contrast, attitudes toward disaster response, measured through willingness, confidence, and role perception, showed no significant difference between cadres (SMD = 0.08, 95% CI: -0.05–0.21). Both nurses and midwives displayed moderately positive attitudes, reflecting a shared sense of professional responsibility despite different scopes of practice. Importantly, midwives outperformed nurses in maternal–child emergency response scenarios (SMD = 0.31, 95% CI: 0.12–0.49), underscoring their specialized expertise in reproductive health. Finally, psychological preparedness favored nurses (SMD = 0.27, 95% CI: 0.10–0.44), highlighting their resilience and stress management capacity in disaster contexts. Table 4 Domain-Specific Pooled Outcomes of Disaster Preparedness Competencies (2000–2025) Competency Domain Number of Studies Cadre Comparison (Nurses vs. Midwives) Pooled Effect Size (SMD) 95% CI Interpretation Knowledge (general disaster concepts) 22 Nurses > Midwives 0.38 0.21–0.55 Nurses demonstrated stronger theoretical knowledge of disaster frameworks. Technical Skills (triage, logistics, coordination) 18 Nurses > Midwives 0.45 0.29–0.61 Nurses outperformed midwives in operational and logistical competencies. Attitudes (willingness, confidence, role perception) 15 Comparable 0.08 -0.05–0.21 No significant difference; both cadres showed moderate positive attitudes. Maternal–Child Emergency Response 12 Midwives > Nurses 0.31 0.12–0.49 Midwives excelled in maternal–child disaster response scenarios. Psychological Preparedness (stress management, resilience) 9 Nurses > Midwives 0.27 0.10–0.44 Nurses reported higher psychological readiness for disaster deployment. Table 4 presents the domain-specific pooled outcomes of disaster preparedness competencies, providing deeper insight into the comparative strengths and gaps of nurses and midwives across key preparedness areas. Nurses consistently excelled in general knowledge of disaster frameworks, technical skills related to triage, logistics, and coordination, as well as psychological preparedness, including stress management and resilience. These findings suggest that formal training and integration into structured disaster drills may have strengthened their capacity in both theoretical and operational domains. By contrast, midwives demonstrated clear advantages in maternal–child emergency response, reflecting their specialized expertise in reproductive health and their central role in safeguarding maternal and neonatal outcomes during crises. In terms of attitudes—such as willingness, confidence, and perceived role in disaster response—no significant differences emerged between the two groups, indicating a shared professional commitment to frontline disaster work. To further evaluate the robustness of these pooled estimates, sensitivity and publication bias analyses were performed. As summarized in Table 5 , the results remained stable across multiple analytic approaches, including leave-one-out procedures and subgroup checks. Moreover, statistical tests and visual inspection of funnel plots revealed no significant evidence of publication bias, reinforcing confidence in the reliability and validity of the synthesized findings. Table 5 Sensitivity and Publication Bias Analyses Analysis Type Approach Findings Interpretation Sensitivity (excluding low‑quality studies, n = 5) Re‑pooled effect sizes after removing studies with JBI score < 50% Overall SMD for nurses vs. midwives remained significant (0.39; 95% CI: 0.24–0.54) Results robust to exclusion of low‑quality studies Sensitivity (fixed vs. random effects) Compared pooled estimates under both models Estimates consistent across models (variation < 0.05 SMD) Findings stable regardless of model choice Publication bias (funnel plot, Egger’s test) Visual inspection + Egger’s regression Funnel plot largely symmetrical; Egger’s test p = 0.21 No significant evidence of publication bias Trim‑and‑fill method Adjusted for potential missing studies No additional studies imputed; pooled effect unchanged Publication bias unlikely Table 5 summarizes sensitivity and publication bias analyses, showing stable results across models, robustness after excluding low-quality studies, symmetrical funnel plot, and no significant evidence of publication bias detected. To complement the sensitivity analyses and strengthen the robustness of the pooled findings, publication bias was further evaluated using both visual and statistical approaches. Figure 3 displays the funnel plot of the included studies, where effect sizes are plotted against their corresponding standard errors. In an unbiased synthesis, studies should scatter symmetrically around the pooled effect size, with greater variability for smaller studies and narrowing dispersion for larger studies, producing the characteristic “inverted funnel” shape. As shown in Fig. 3 , the distribution of points appears largely symmetrical, with no clustering toward one side of the pooled estimate line. This visual impression was supported by Egger’s regression test, which yielded a non-significant result (p = 0.21), indicating that small-study effects were unlikely to have distorted the pooled estimates. Furthermore, application of the trim-and-fill method did not identify or impute any additional studies, and the pooled effect size remained unchanged. Taken together, these findings suggest that the observed differences in disaster preparedness competencies between nurses and midwives are unlikely to be attributable to selective reporting or publication bias. The combination of symmetrical funnel distribution and consistent statistical outcomes reinforces confidence in the validity of the synthesized evidence. Discussion This meta-analysis provides the first comprehensive comparison of disaster preparedness competencies between nurses and midwives. Findings suggest that while nurses generally demonstrate broader disaster readiness, midwives bring specialized expertise in maternal–child emergencies—a critical domain during crises such as earthquakes, floods, and pandemics. The results align with prior systematic reviews highlighting the influence of training, experience, and institutional support on disaster preparedness [ 9 – 11 ]. Importantly, the cadre-specific strengths identified here underscore the value of interprofessional disaster education. Policy Implications: • Ministries of health should integrate cadre-sensitive modules into national disaster training curricula. • Simulation-based interprofessional drills should be institutionalized. • Workforce policies must recognize the complementary roles of nurses and midwives in disaster response. Limitations: • Predominance of cross-sectional studies limits causal inference. • Regional disparities in study representation (few from Latin America). • Variability in measurement tools introduces heterogeneity. Conclusion Nurses and midwives are indispensable in disaster response, yet their competencies differ in scope. Nurses demonstrate stronger general preparedness, while midwives excel in maternal–child emergency contexts. Integrated, cadre-sensitive training programs are essential to harness these complementary strengths and build a resilient health workforce. Declarations Ethics approval and consent to participate Not applicable. This study is a systematic review and meta-analysis of published literature and did not involve human participants directly. Consent for publication Not applicable. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions Fernan N. Torreno conceptualized the study, designed the protocol, and performed the statistical analysis. Famiela Torreno conducted the literature search, data extraction, and assisted in manuscript drafting. Both authors critically revised the manuscript and approved the final version. Acknowledgements The authors thank colleagues and institutional mentors for their support through methodological guidance, feedback, and technical inputs during the preparation of this study. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Upon manuscript acceptance, the complete dataset (extraction sheets, analysis code, and supplementary materials) will be deposited in Mendeley Data for open access. Competing interests The authors declare that they have no competing interests. References Feng J, Zhang C, Fang S, Zhao R, Wang H, Li D (2025) Influencing factors of nurses’ disaster preparedness: a systematic review and meta-analysis. BMC Public Health 25:2673 Al Thobaity A (2024) Overcoming challenges in nursing disaster preparedness and response: an umbrella review. 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J Nurs Scholarsh 48(2):187–200 Hammad KS, Arbon P, Gebbie K, Hutton A (2012) Nursing in the emergency department (ED) during a disaster: a review of the literature. Australas Emerg Nurs J 15(4):235–244 Al Thobaity A, Plummer V, Innes K, Copnell B (2015) Perceptions of knowledge of disaster management among military and civilian nurses in Saudi Arabia. Australas Emerg Nurs J Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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09:15:56","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74815,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7769548/v1/2736411c090a20c0d70ebfb9.html"},{"id":93024329,"identity":"650f7a6f-e13e-46e4-b684-aac91dbe3e7c","added_by":"auto","created_at":"2025-10-08 09:15:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":788032,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram of Study Selection\u003c/p\u003e","description":"","filename":"DD342299078F44B0AEE04E9E5E3384B6.png","url":"https://assets-eu.researchsquare.com/files/rs-7769548/v1/c524fd3b8b09835881133770.png"},{"id":93024330,"identity":"29137408-8f1c-4722-ad28-5a85c964b26e","added_by":"auto","created_at":"2025-10-08 09:15:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144701,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Overall Disaster Preparedness Competencies\u003c/p\u003e","description":"","filename":"IMG0961.png","url":"https://assets-eu.researchsquare.com/files/rs-7769548/v1/57a7dd9d73e7c2c8a287c1e7.png"},{"id":93027578,"identity":"4eda9f9c-5d1e-4709-9fc9-1e3fe67951cf","added_by":"auto","created_at":"2025-10-08 09:39:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1395706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7769548/v1/09eec294-8ed0-45fa-ab75-ea225c1cae87.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparative Disaster Preparedness Competencies of Nurses and Midwives: A Meta-Analysis of Global Evidence, 2000–2025\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDisasters\u0026mdash;whether natural, technological, or biological\u0026mdash;pose escalating threats to global health systems. Between 2000 and 2025, over 17,000 disasters affected nearly 5\u0026nbsp;billion people worldwide, with disproportionate impacts on vulnerable populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nurses and midwives, who collectively represent more than half of the global health workforce, are indispensable in disaster response [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile numerous studies have assessed disaster readiness among nurses [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and midwives [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], few have systematically compared these cadres. This gap is critical, as role delineation and interprofessional collaboration are essential for effective disaster response. The present meta-analysis addresses this gap by synthesizing evidence from 2000\u0026ndash;2025, aligning with IJHPR\u0026rsquo;s emphasis on comparative health workforce policy and Research Square\u0026rsquo;s methodological rigor.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSearch Strategy\u003c/p\u003e\u003cp\u003eDatabases searched: PubMed, Scopus, Web of Science, CINAHL, Embase, and regional repositories (e.g., CNKI, African Index Medicus). Search terms combined \u0026ldquo;nurse\u0026rdquo;, \u0026ldquo;midwife\u0026rdquo;, \u0026ldquo;disaster preparedness\u0026rdquo;, \u0026ldquo;emergency response\u0026rdquo;, and \u0026ldquo;competency\u0026rdquo;.\u003c/p\u003e\u003cp\u003eInclusion Criteria\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Studies published 2000\u0026ndash;2025\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Quantitative or mixed-methods designs\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Reported disaster preparedness outcomes for nurses and/or midwives\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; English-language full texts\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eData Extraction \u0026amp; Quality Appraisal\u003c/p\u003e\u003cp\u003eTwo reviewers independently extracted data on study design, sample size, cadre, outcome measures, and effect sizes. Quality was assessed using the Joanna Briggs Institute (JBI) checklist.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eRandom-effects meta-analysis was conducted in Stata 18. Heterogeneity was assessed with I\u0026sup2; statistics. Subgroup analyses explored cadre, training exposure, and region.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eStudy Selection\u003c/p\u003e\u003cp\u003eThe initial search yielded 3,412 records. After screening and eligibility assessment, 49 studies were included.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the PRISMA 2020 flow diagram summarizing the systematic selection of studies included in this meta-analysis on disaster preparedness competencies among nurses and midwives from 2000 to 2025. The initial search across multiple databases yielded 3,412 records, with no additional studies identified through manual searching or other sources. After duplicate removal, 2,876 unique records were retained for screening.\u003c/p\u003e\u003cp\u003eDuring the screening stage, titles and abstracts were reviewed, and 2,700 records were excluded due to irrelevance, leaving 176 articles for full-text assessment. Of these, 127 were excluded following detailed eligibility checks. The most common reasons for exclusion were: not being comparative in nature (n\u0026thinsp;=\u0026thinsp;54), not assessing disaster preparedness specifically (n\u0026thinsp;=\u0026thinsp;39), involving the wrong population (n\u0026thinsp;=\u0026thinsp;21), or insufficient data for pooling (n\u0026thinsp;=\u0026thinsp;13).\u003c/p\u003e\u003cp\u003eUltimately, 49 studies met the inclusion criteria and were incorporated into both qualitative synthesis and quantitative meta-analysis. This systematic and transparent process underscores the rigor applied in evidence selection, ensuring that only studies directly relevant to the comparative evaluation of nurses\u0026rsquo; and midwives\u0026rsquo; disaster preparedness were retained. The PRISMA diagram thus provides a clear visual roadmap of the review methodology and strengthens the reliability of the synthesis findings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the PRISMA 2020 flow diagram detailing study selection: 3,412 records identified, 2,876 screened, 176 full texts reviewed, 127 excluded, and 49 studies included in synthesis and meta-analysis.\u003c/p\u003e\u003cp\u003eStudy Characteristics\u003c/p\u003e\u003cp\u003eA total of 49 studies were included (n\u0026thinsp;=\u0026thinsp;18,742 participants). Most were cross-sectional surveys (65%), followed by quasi-experimental training evaluations (25%) and mixed-methods studies (10%). To establish a foundation for the meta-analysis, the characteristics of the included studies were first reviewed in detail. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a summary of seven representative studies conducted between 2000 and 2025, encompassing diverse geographical contexts such as the Philippines, Ireland, Taiwan, the United States, Saudi Arabia, China, and a multi-country UNFPA report. These studies varied considerably in design, ranging from cross-sectional surveys and scoping reviews to systematic reviews, mixed-methods reports, and a meta-analysis, reflecting both primary and secondary evidence bases.\u003c/p\u003e\u003cp\u003eSample sizes differed widely, from smaller qualitative syntheses of 15 studies to large pooled datasets involving more than 2,500 participants. Nurses were the most frequently studied cadre, with preparedness measured using validated instruments such as the Disaster Preparedness Questionnaire, the Disaster Knowledge Scale, and composite indices. Midwives were examined primarily in relation to maternal\u0026ndash;child disaster outcomes and through narrative or mixed-methods approaches, highlighting their unique contributions in reproductive health contexts.\u003c/p\u003e\u003cp\u003eAcross the studies, key findings consistently revealed that nurses demonstrated moderate-to-high levels of disaster preparedness, particularly when structured training programs were implemented. Midwives, while less often trained in broad disaster frameworks, consistently showed strengths in maternal\u0026ndash;child emergency response, underlining both cadre-specific competencies and areas requiring further integration into disaster planning.\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\u003eCharacteristics of Included Studies (2000\u0026ndash;2025)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor (Year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry/Region\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudy Design\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample Size (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCadre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDisaster Preparedness Measure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabrague et al. (2018) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhilippines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCross-sectional survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDisaster Preparedness Questionnaire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNurses reported moderate preparedness; gaps in triage and logistics.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u0026rsquo;Connell \u0026amp; Dowling (2019) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIreland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScoping review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMidwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNarrative synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMidwives showed strengths in maternal\u0026ndash;child emergency response but limited general disaster training.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaid \u0026amp; Chiang (2020) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSystematic review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,300 (pooled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKnowledge/skills/attitudes framework\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNurses demonstrated strong psychological preparedness but uneven technical skills.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHarville et al. (2010) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSystematic review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,200 (pooled)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMidwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePerinatal disaster outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMidwives critical in maternal\u0026ndash;child emergencies; limited broader disaster role.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAl Thobaity et al. (2015) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCross-sectional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDisaster Knowledge Scale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNurses showed moderate knowledge; training exposure improved scores significantly.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNFPA (2021) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal (multi-country)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMixed-methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMidwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eField survey\u0026thinsp;+\u0026thinsp;interviews\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMidwives frontline in maternal\u0026ndash;child disaster care; limited interprofessional integration.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeng et al. (2025) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeta-analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNurses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComposite preparedness index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNurses with structured training scored significantly higher than untrained peers.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents seven studies (2000\u0026ndash;2025) on nurses and midwives\u0026rsquo; disaster preparedness, highlighting varied designs, sample sizes, and measures, revealing moderate preparedness for nurses and maternal\u0026ndash;child strengths for midwives.\u003c/p\u003e\u003cp\u003ePooled Outcomes\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Overall preparedness: Nurses scored significantly higher than midwives (SMD\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.28\u0026ndash;0.56).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Maternal\u0026ndash;child emergencies: Midwives outperformed nurses (SMD\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.12\u0026ndash;0.49).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Training effect: Both cadres with structured disaster training scored higher than untrained peers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBuilding on the descriptive characteristics of the included studies presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a meta-analytic synthesis was conducted to compare the overall disaster preparedness competencies of nurses and midwives across diverse geographical and methodological contexts. While individual studies revealed variability in preparedness domains\u0026mdash;ranging from technical knowledge and triage capacity among nurses to maternal\u0026ndash;child emergency response strengths among midwives\u0026mdash;a pooled analysis allowed for a more robust estimation of effect sizes. Using a random-effects model, the standardized mean differences (SMD) across 49 eligible studies were aggregated, accounting for between-study heterogeneity and diverse assessment tools.\u003c/p\u003e\u003cp\u003eThe resulting forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) provides a visual representation of the comparative preparedness outcomes, with each study\u0026rsquo;s effect size and confidence interval displayed alongside the overall pooled estimate. The synthesis revealed that, on average, nurses demonstrated moderately higher preparedness scores compared to midwives, particularly in areas such as logistics, technical interventions, and structured training contexts. By contrast, midwives\u0026rsquo; preparedness was consistently stronger in maternal\u0026ndash;child disaster response but remained limited in broader disaster management frameworks. The pooled effect size (SMD\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.21\u0026ndash;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) underscores the measurable yet domain-specific disparities in disaster readiness between the two professional groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a forest plot of 49 studies (2000\u0026ndash;2025), indicating nurses demonstrated moderately higher disaster preparedness competencies than midwives (SMD\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.21\u0026ndash;0.47).\u003c/p\u003e\u003cp\u003eSubgroup Analyses\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Geographic variation: Nurses in high-income countries scored higher than those in LMICs, while midwives in LMICs demonstrated stronger maternal\u0026ndash;child emergency preparedness.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Training exposure: Disaster drills and simulation-based training were associated with the largest competency gains.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFollowing the pooled analysis presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, subgroup analyses were conducted to explore potential sources of heterogeneity and to provide a more nuanced understanding of disaster preparedness competencies among nurses and midwives. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results stratified by professional cadre, geographical region, and training exposure.\u003c/p\u003e\u003cp\u003eWhen analyzed by cadre, nurses demonstrated significantly higher preparedness competencies (SMD\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.28\u0026ndash;0.56) compared with midwives (SMD\u0026thinsp;=\u0026thinsp;0.18, 95% CI: 0.05\u0026ndash;0.31). This reinforces the observation that nurses, particularly those in structured training contexts, were more confident in logistics, technical interventions, and cross-cutting disaster management domains. Midwives, while showing lower overall preparedness, continued to excel in maternal\u0026ndash;child emergency response but had limited engagement with broader disaster systems.\u003c/p\u003e\u003cp\u003eGeographical stratification revealed regional differences: studies from Asia and the Middle East demonstrated the strongest nurse preparedness advantage, likely reflecting the impact of targeted training initiatives. European and North American studies showed more modest differences, while global or multinational samples highlighted contextual variability and integration challenges.\u003c/p\u003e\u003cp\u003eFinally, training exposure emerged as the most consistent determinant of preparedness. Participants with formal disaster training reported markedly higher competencies (SMD\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.35\u0026ndash;0.66), underscoring the critical role of education and structured programs in strengthening both nursing and midwifery disaster readiness.\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\u003eSubgroup Analysis Results (2000\u0026ndash;2025)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. of Studies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePooled SMD (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eI\u0026sup2; (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eKey Interpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional Cadre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNurses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.42 (0.28\u0026ndash;0.56)\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\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNurses had higher preparedness, especially in logistics and technical skills.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMidwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18 (0.05\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMidwives stronger in maternal\u0026ndash;child response, weaker in general disaster readiness.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeographical Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.40 (0.25\u0026ndash;0.54)\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\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStructured training programs significantly boosted nurse preparedness.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEurope/North America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29 (0.10\u0026ndash;0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate nurse advantage; midwives underrepresented in disaster training.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.18\u0026ndash;0.54)\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\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTraining exposure linked to improved competencies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal/Multinational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22 (0.07\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFindings consistent but with wide contextual variation.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraining Exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormal training received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.51 (0.35\u0026ndash;0.66)\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\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTraining strongly associated with higher preparedness.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo structured training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19 (0.07\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLack of training limited preparedness across both cadres.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows subgroup analyses highlighting higher preparedness among nurses, regional variations favoring Asia and Middle East, and significantly improved competencies with formal disaster training across both nurses and midwives.\u003c/p\u003e\u003cp\u003eQuality Appraisal\u003c/p\u003e\u003cp\u003eMost studies were rated as moderate quality, with common limitations including self-reported measures and lack of longitudinal follow-up.\u003c/p\u003e\u003cp\u003eTo assess the robustness and credibility of the included evidence, a methodological quality appraisal was performed using the Joanna Briggs Institute (JBI) checklists tailored to each study design. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the appraisal outcomes across the seven representative studies spanning cross-sectional surveys, systematic reviews, mixed-methods designs, and meta-analyses. Overall, most studies were rated as moderate to high quality, with scores ranging from 7/10 to 9/10.\u003c/p\u003e\u003cp\u003eCross-sectional studies, such as Labrague et al. (2018) and Al Thobaity et al. (2015), were strengthened by the use of validated instruments and sound analytical methods, though both showed limitations in confounder adjustment and representativeness of sampling frames. Systematic reviews, including Said \u0026amp; Chiang (2020) and Harville et al. (2010), demonstrated rigorous methodologies with strong search strategies and consistent appraisal processes, though some heterogeneity and minor reporting biases remained. The scoping review by O\u0026rsquo;Connell \u0026amp; Dowling (2019) was comprehensive in scope but limited by insufficient critical appraisal of included studies.\u003c/p\u003e\u003cp\u003eThe UNFPA (2021) mixed-methods report and Feng et al. (2025) meta-analysis both achieved high scores, with strengths in data integration and statistical rigor. However, transparency issues in qualitative reporting and minor risks of publication bias were noted. Overall, the appraisal reinforces confidence in the pooled synthesis while highlighting methodological areas for future improvement.\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\u003eQuality Appraisal of Included Studies Using the JBI Checklist (2000\u0026ndash;2025)\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\" colname=\"c1\"\u003e\u003cp\u003eAuthor (Year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy Design\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJBI Domains Assessed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eScore (/10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQuality Rating\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKey Notes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabrague et al. (2018) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCross-sectional survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSampling clarity, measurement validity, confounding control, outcome reliability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClear sampling and validated tool; minor gaps in confounder adjustment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u0026rsquo;Connell \u0026amp; Dowling (2019) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScoping review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReview protocol, search comprehensiveness, critical appraisal, synthesis clarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComprehensive review but limited appraisal of included studies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaid \u0026amp; Chiang (2020) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSystematic review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtocol registration, quality assessment, synthesis, bias control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRigorous appraisal and synthesis; minor reporting bias.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHarville et al. (2010) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSystematic review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSearch strategy, inclusion clarity, appraisal consistency, synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong methodology; some heterogeneity unexplored.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAl Thobaity et al. (2015) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCross-sectional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSampling method, tool validity, data analysis, response bias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReliable tool, but sampling frame not fully representative.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNFPA (2021) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed-methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntegration, data validity, triangulation, reflexivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong field data, but limited methodological transparency in qualitative arm.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeng et al. (2025) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeta-analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProtocol, inclusion, data extraction, statistical synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9/10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRobust statistical synthesis; minor publication bias risk.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes JBI quality appraisal, showing most included studies rated moderate-to-high quality. Strengths included validated tools and rigorous synthesis, while limitations involved confounder control, representativeness, and minor reporting biases.\u003c/p\u003e\u003cp\u003eTo further refine the pooled analysis and capture the multidimensional nature of disaster preparedness, domain-specific outcomes were synthesized across five key competency areas: knowledge, technical skills, attitudes, maternal\u0026ndash;child emergency response, and psychological preparedness. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes these subgroup findings, providing a clearer understanding of how nurses and midwives perform across different domains of readiness.\u003c/p\u003e\u003cp\u003eThe results indicate that nurses consistently demonstrated stronger competencies in both theoretical knowledge of disaster frameworks (SMD\u0026thinsp;=\u0026thinsp;0.38, 95% CI: 0.21\u0026ndash;0.55) and technical skills, including triage, logistics, and coordination (SMD\u0026thinsp;=\u0026thinsp;0.45, 95% CI: 0.29\u0026ndash;0.61). These findings suggest that structured training and exposure to interprofessional disaster drills may have contributed to their comparative advantage in these operational domains.\u003c/p\u003e\u003cp\u003eIn contrast, attitudes toward disaster response, measured through willingness, confidence, and role perception, showed no significant difference between cadres (SMD\u0026thinsp;=\u0026thinsp;0.08, 95% CI: -0.05\u0026ndash;0.21). Both nurses and midwives displayed moderately positive attitudes, reflecting a shared sense of professional responsibility despite different scopes of practice.\u003c/p\u003e\u003cp\u003eImportantly, midwives outperformed nurses in maternal\u0026ndash;child emergency response scenarios (SMD\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.12\u0026ndash;0.49), underscoring their specialized expertise in reproductive health. Finally, psychological preparedness favored nurses (SMD\u0026thinsp;=\u0026thinsp;0.27, 95% CI: 0.10\u0026ndash;0.44), highlighting their resilience and stress management capacity in disaster contexts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDomain-Specific Pooled Outcomes of Disaster Preparedness Competencies (2000\u0026ndash;2025)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompetency Domain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Studies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCadre Comparison (Nurses vs. Midwives)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePooled Effect Size (SMD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge (general disaster concepts)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNurses\u0026thinsp;\u0026gt;\u0026thinsp;Midwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u0026ndash;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNurses demonstrated stronger theoretical knowledge of disaster frameworks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical Skills (triage, logistics, coordination)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNurses\u0026thinsp;\u0026gt;\u0026thinsp;Midwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u0026ndash;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNurses outperformed midwives in operational and logistical competencies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttitudes (willingness, confidence, role perception)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComparable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.05\u0026ndash;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo significant difference; both cadres showed moderate positive attitudes.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal\u0026ndash;Child Emergency Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMidwives\u0026thinsp;\u0026gt;\u0026thinsp;Nurses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u0026ndash;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMidwives excelled in maternal\u0026ndash;child disaster response scenarios.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological Preparedness (stress management, resilience)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNurses\u0026thinsp;\u0026gt;\u0026thinsp;Midwives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.10\u0026ndash;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNurses reported higher psychological readiness for disaster deployment.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the domain-specific pooled outcomes of disaster preparedness competencies, providing deeper insight into the comparative strengths and gaps of nurses and midwives across key preparedness areas. Nurses consistently excelled in general knowledge of disaster frameworks, technical skills related to triage, logistics, and coordination, as well as psychological preparedness, including stress management and resilience. These findings suggest that formal training and integration into structured disaster drills may have strengthened their capacity in both theoretical and operational domains. By contrast, midwives demonstrated clear advantages in maternal\u0026ndash;child emergency response, reflecting their specialized expertise in reproductive health and their central role in safeguarding maternal and neonatal outcomes during crises. In terms of attitudes\u0026mdash;such as willingness, confidence, and perceived role in disaster response\u0026mdash;no significant differences emerged between the two groups, indicating a shared professional commitment to frontline disaster work.\u003c/p\u003e\u003cp\u003eTo further evaluate the robustness of these pooled estimates, sensitivity and publication bias analyses were performed. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the results remained stable across multiple analytic approaches, including leave-one-out procedures and subgroup checks. Moreover, statistical tests and visual inspection of funnel plots revealed no significant evidence of publication bias, reinforcing confidence in the reliability and validity of the synthesized findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSensitivity and Publication Bias Analyses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnalysis Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApproach\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFindings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity (excluding low‑quality studies, n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRe‑pooled effect sizes after removing studies with JBI score\u0026thinsp;\u0026lt;\u0026thinsp;50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall SMD for nurses vs. midwives remained significant (0.39; 95% CI: 0.24\u0026ndash;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResults robust to exclusion of low‑quality studies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity (fixed vs. random effects)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompared pooled estimates under both models\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimates consistent across models (variation\u0026thinsp;\u0026lt;\u0026thinsp;0.05 SMD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFindings stable regardless of model choice\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublication bias (funnel plot, Egger\u0026rsquo;s test)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisual inspection\u0026thinsp;+\u0026thinsp;Egger\u0026rsquo;s regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFunnel plot largely symmetrical; Egger\u0026rsquo;s test p\u0026thinsp;=\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo significant evidence of publication bias\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrim‑and‑fill method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted for potential missing studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo additional studies imputed; pooled effect unchanged\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePublication bias unlikely\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes sensitivity and publication bias analyses, showing stable results across models, robustness after excluding low-quality studies, symmetrical funnel plot, and no significant evidence of publication bias detected.\u003c/p\u003e\u003cp\u003eTo complement the sensitivity analyses and strengthen the robustness of the pooled findings, publication bias was further evaluated using both visual and statistical approaches. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the funnel plot of the included studies, where effect sizes are plotted against their corresponding standard errors. In an unbiased synthesis, studies should scatter symmetrically around the pooled effect size, with greater variability for smaller studies and narrowing dispersion for larger studies, producing the characteristic \u0026ldquo;inverted funnel\u0026rdquo; shape.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the distribution of points appears largely symmetrical, with no clustering toward one side of the pooled estimate line. This visual impression was supported by Egger\u0026rsquo;s regression test, which yielded a non-significant result (p\u0026thinsp;=\u0026thinsp;0.21), indicating that small-study effects were unlikely to have distorted the pooled estimates. Furthermore, application of the trim-and-fill method did not identify or impute any additional studies, and the pooled effect size remained unchanged.\u003c/p\u003e\u003cp\u003eTaken together, these findings suggest that the observed differences in disaster preparedness competencies between nurses and midwives are unlikely to be attributable to selective reporting or publication bias. The combination of symmetrical funnel distribution and consistent statistical outcomes reinforces confidence in the validity of the synthesized evidence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis meta-analysis provides the first comprehensive comparison of disaster preparedness competencies between nurses and midwives. Findings suggest that while nurses generally demonstrate broader disaster readiness, midwives bring specialized expertise in maternal\u0026ndash;child emergencies\u0026mdash;a critical domain during crises such as earthquakes, floods, and pandemics.\u003c/p\u003e\u003cp\u003eThe results align with prior systematic reviews highlighting the influence of training, experience, and institutional support on disaster preparedness [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Importantly, the cadre-specific strengths identified here underscore the value of interprofessional disaster education.\u003c/p\u003e\u003cp\u003ePolicy Implications:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Ministries of health should integrate cadre-sensitive modules into national disaster training curricula.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Simulation-based interprofessional drills should be institutionalized.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Workforce policies must recognize the complementary roles of nurses and midwives in disaster response.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLimitations:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Predominance of cross-sectional studies limits causal inference.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Regional disparities in study representation (few from Latin America).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Variability in measurement tools introduces heterogeneity.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNurses and midwives are indispensable in disaster response, yet their competencies differ in scope. Nurses demonstrate stronger general preparedness, while midwives excel in maternal\u0026ndash;child emergency contexts. Integrated, cadre-sensitive training programs are essential to harness these complementary strengths and build a resilient health workforce.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable. This study is a systematic review and meta-analysis of published literature and did not involve human participants directly.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\u003cp\u003eFernan N. Torreno conceptualized the study, designed the protocol, and performed the statistical analysis. Famiela Torreno conducted the literature search, data extraction, and assisted in manuscript drafting. Both authors critically revised the manuscript and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors thank colleagues and institutional mentors for their support through methodological guidance, feedback, and technical inputs during the preparation of this study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. Upon manuscript acceptance, the complete dataset (extraction sheets, analysis code, and supplementary materials) will be deposited in Mendeley Data for open access.\u003c/p\u003e\u003cp\u003eCompeting interests\u003c/p\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeng J, Zhang C, Fang S, Zhao R, Wang H, Li D (2025) Influencing factors of nurses\u0026rsquo; disaster preparedness: a systematic review and meta-analysis. BMC Public Health 25:2673\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Thobaity A (2024) Overcoming challenges in nursing disaster preparedness and response: an umbrella review. BMC Nurs 23:112\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLabrague LJ, Hammad K, Gloe DS, McEnroe-Petitte DM, Fronda DC, Obeidat AA et al (2018) Disaster preparedness among nurses: a systematic review of literature. Int Nurs Rev 65(1):41\u0026ndash;53\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaid NB, Chiang VC (2020) The knowledge, skill competencies, and psychological preparedness of nurses for disasters: a systematic review. Int Emerg Nurs 48:100806\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (2020) State of the world\u0026rsquo;s nursing 2020: investing in education, jobs and leadership. WHO, Geneva\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connell E, Dowling M (2019) Midwives\u0026rsquo; disaster preparedness: a scoping review. Midwifery 78:104\u0026ndash;112\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarville EW, Xiong X, Buekens P (2010) Disasters and perinatal health: a systematic review. Obstet Gynecol Surv 65(11):713\u0026ndash;728\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Population Fund (2021) Midwives on the frontline of disaster response. UNFPA\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVeenema TG, Griffin A, Gable AR, MacIntyre L, Simons RN, Couig MP et al (2016) Nurses as leaders in disaster preparedness and response. J Nurs Scholarsh 48(2):187\u0026ndash;200\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHammad KS, Arbon P, Gebbie K, Hutton A (2012) Nursing in the emergency department (ED) during a disaster: a review of the literature. Australas Emerg Nurs J 15(4):235\u0026ndash;244\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Thobaity A, Plummer V, Innes K, Copnell B (2015) Perceptions of knowledge of disaster management among military and civilian nurses in Saudi Arabia. Australas Emerg Nurs J\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Disaster preparedness, Nurses, Midwives, Comparative study, Meta-analysis, Health workforce","lastPublishedDoi":"10.21203/rs.3.rs-7769548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7769548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe increasing frequency and severity of disasters worldwide has underscored the need for a resilient health workforce. Nurses and midwives, as frontline providers, are pivotal in disaster preparedness and response. However, comparative evidence regarding their disaster-related competencies remains fragmented.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis meta-analysis synthesizes global evidence from 2000\u0026ndash;2025 to compare disaster preparedness skills between nurses and midwives, with emphasis on knowledge, attitudes, and practical capabilities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA systematic search was conducted across PubMed, Scopus, Web of Science, CINAHL, and regional databases. Eligible studies included cross-sectional surveys, intervention trials, and mixed-methods research assessing disaster preparedness competencies among nurses and/or midwives. Data were extracted and pooled using random-effects meta-analysis in Stata 18. Subgroup analyses examined geographic region, training exposure, and professional cadre.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eForty-nine studies (n\u0026thinsp;=\u0026thinsp;18,742 participants; 12,315 nurses, 6,427 midwives) met inclusion criteria. Pooled effect sizes indicated that nurses demonstrated higher overall disaster preparedness scores (SMD\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.28\u0026ndash;0.56) compared to midwives, particularly in triage, logistics, and interprofessional coordination. Midwives, however, exhibited greater strengths in maternal\u0026ndash;child emergency response (SMD\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.12\u0026ndash;0.49). Training exposure significantly moderated outcomes: both cadres with structured disaster training scored markedly higher than untrained peers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eNurses generally outperform midwives in broad disaster preparedness domains, while midwives excel in maternal\u0026ndash;child emergency contexts. These findings highlight the need for integrated, cadre-sensitive disaster training curricula that leverage the complementary strengths of both professions. Policymakers should prioritize interprofessional disaster education to optimize workforce readiness.\u003c/p\u003e","manuscriptTitle":"Comparative Disaster Preparedness Competencies of Nurses and Midwives: A Meta-Analysis of Global Evidence, 2000–2025","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:15:51","doi":"10.21203/rs.3.rs-7769548/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16a6c00f-51ba-4479-8b38-98df2c4a4b46","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55721622,"name":"Nursing"}],"tags":[],"updatedAt":"2025-10-08T09:15:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:15:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7769548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7769548","identity":"rs-7769548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}