The core competencies in disaster nursing and influencing factors among clinical nurses in Guangzhou: a cross-sectional study based on latent profile analysis

The core competencies in disaster nursing and influencing factors among clinical nurses in Guangzhou: a cross-sectional study based on latent profile analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The core competencies in disaster nursing and influencing factors among clinical nurses in Guangzhou: a cross-sectional study based on latent profile analysis Chaoqun Ma, Qishan Zhang, Gongzhen Wen, Danting Weng, Pingjuan He, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5201065/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To explore the core competencies in disaster nursing possessed by clinical nurses in Guangzhou, China, and then to uncover the influencing factors that shape these competencies. Methods A cross-sectional design of 1,151 nurses from Guangzhou was conducted from December 2022 to February 2023.A localized scale was compiled to construct a measurement tool that conforms to international standards and China's actual situation, providing guidance for analyzing capability shortcomings and constructing training programs and improving the disaster response level of Chinese nurses. Latent Profile Analysis (LPA), univariate analysis, and multivariate logistic regression analysis was conducted to explore the predictors of latent profile membership and their influencing factors. Results A total of 1151 questionnaires were valid.The core competencies of nurses were categorized into three distinct groups: deficient competence group(62.6%),moderate competence group(26.6%), and acceptable competence group(16.9%).Factors such as participation in disaster relief, drills, and belonging to emergency/disaster medical response teams were found to be significant influences. Conclusion Heterogeneity exists in the core competencies of disaster nursing among nurses in Guangzhou, with the moderate competence group scoring the highest in the legal and ethical dimensions. This study recommends the implementation of targeted training programs and multidisciplinary drills to enhance the capabilities of nurses. Figures Figure 1 Introduction In 2020, for instance, the unforeseen proliferation of COVID-19 impeded global endeavors to accomplish the Sustainable Development Goals worldwide[1].Nevertheless, this pandemic has also engendered an unparalleled surge in media and public attention towards nursing[2].Nurses are the largest group of healthcare professionals globally and serve as the primary responders during disaster rescue operations[3], as well as, nursing is an essential part of health care, and nurses play a key role in achieving universal health coverage (UHC) and medical rescue as well as improving the population health outcomes[4].Disasters often strike without warning, creating tumultuous and intricate scenarios at rescue locations that sharply diverge from the organized clinical milieus, where fixed departments and dedicated medical personnel thrive. Consequently, the presence of seasoned or specially trained nurses is paramount in navigating the intricacies of such unprecedented situations. The arduous treatment environment, marked by resource constraints, shortages of supplies and medications, pressing tasks, and psychological pressures, imposes immense challenges on nurses engaged in disaster relief operations[5].As per the 2020 State of the World's Nursing Report, the progression of disaster nursing disciplines stands as a crucial factor in defining the future trajectory of nursing on a global scale[6]. Disaster nursing is a specialized field that focuses on the provision of care and support individuals and communities who are affected by emergencies and crises[7]. Disaster competencies are crucial because an immediate and effective response to disasters directly impacts the life and safety of people. Te International Council of Nurses suggests imparting disaster nursing competencies to nurses, who form the core of healthcare professionals and serve in a variety of important disaster response roles[8]. However, disaster nursing faces major challenges that must be acknowledged and addressed[9].but currently, there is a deficiency in core disaster nursing capabilities. A survey[10]targeting nurses in East China shows that the disaster preparedness of nurses in China is at a medium level, and the situation in key departments and among community nurses is not ideal. Qualitative research on managers of the COVID-19 epidemic also indicates that senior nursing staff need to undertake new responsibilities and receive additional educational support[11]. In 2019, the International Council of Nurses released the Core Competencies in Disaster Nursing Version 2.0[12](CCDN2.0), expanding the four dimensions to eight dimensions and multiple competency indicators, and clearly stating that all nurses need to- reach level I. The research on core disaster nursing capabilities in China is not in line with international standards, manifested as inconsistent quality of evaluation scales and an unformed system of education and on-the-job training. Particularly, soft skills such as pre-disaster preparation, post-disaster recovery, and legal and ethical aspects need improvement. Some domestic scholars have explored the feasibility of this guideline in doctoral training systems[13]and COVID-19 nursing practices[14]. Given the strong guidance of CCDN2.0 but the need to adapt to China's national conditions, taking the 35 GPN capabilities as the starting point and combining China's nurse functions and emergency rescue systems, a localized scale is compiled to construct a measurement tool that conforms to international standards and China's actual situation, providing guidance for analyzing capability shortcomings and constructing training programs and improving the disaster response level of Chinese nurses. Methods Design This is a cross-sectional study conducted from December 2022 to February 2023, which adhered to the STROBE statement. Participants Registered nurses from medical institutions including hospitals, primary-level medical facilities, and maternal and child health care hospitals in Guangzhou were selected as survey subjects via convenience sampling. The survey was conducted from December 2022 to February 2023. Inclusion criteria: ①Aged between 18 and 60 years. ②Having obtained the Nurse Practicing Certificate of the People's Republic of China. ③Providing informed consent and voluntarily participating in this study. Exclusion criteria: ①Those who failed the nursing annual assessment. ②Those who are absent from work due to maternity leave, further studies, etc. ③Those whose work units are not located in Guangzhou. Sample size Everitt[15]proposes that the sample size for exploratory factor analysis (EFA) should be at least 10 times the number of items in the scale. Bandalos[16]suggests that the sample size for confirmatory factor analysis (CFA) should be no less than 200 cases. Considering a 20% failure rate, the sample size is calculated as follows: [(35×10) + 200]×(1 + 20%) = 636. Considering a 20% sample attrition rate, the minimum requisite sample size is calculated as 636 ÷ (1–20%) = 795. In this study, 1151 cases were finally included. Instruments General Information Questionnaire. Referring to the influencing factors of core competencies in disaster nursing in existing studies[17, 18], we designed 17 questions encompassing gender, age, marital status, number of children, highest educational attainment, hospital level, hospital type, department, years of nursing work, whether one is affiliated with the establishment, nursing level, professional title, whether one has participated in disaster relief other than the COVID-19 epidemic, whether one has participated in disaster nursing-related training conducted by academic societies in the past three years, whether one has participated in disaster drills in the past three years, the level of the emergency medical rescue team of the affiliated unit, and whether one is a member of the emergency medical rescue team. Core Competencies in Disaster Nursing Version 2.0(CCDN2.0) Under the auspices of the International Council of Nurses (ICN), in collaboration with multiple steering committees including the International Red Cross, the World Association for Disaster and Emergency Medicine, and the World Society of Disaster Nursing, the "Core Competencies in Disaster Nursing Version 2.0" was released in 2019. This guideline, with an overall Cronbach’s alpha coefficient of 0.980, divides the core competencies of disaster nursing into eight dimensions, namely preparation and planning, communication, incident management system, safety, assessment, intervention, recovery, law and ethics; and three levels, namely general nurses, advanced nurses, and disaster nursing experts. The researcher obtained the authorization of ICN in 2022.This study is based on the cross-cultural translation process of the Beaton assessment tool, having six steps: direct translation by two target-language-native translators; synthesis of results; back translation by two source-language-native translators in a back-to-back way; expert meeting review; modification through pre-survey; submission to original author/experts for review. Referring to relevant research[19–21]and considering subsequent scale development needs, after research group discussion, the Chinese translation process of the CCDN2.0 guideline in this study has four steps: direct translation; synthesis; back translation; group discussion. Data collection The secretary of the Disaster Specialized Committee of Guangdong Nursing Association was contacted to recruit committee members with work locations in Guangzhou as investigators for this study and to compile a list of hospital assistants for the survey. The background and purpose of this study were uniformly introduced and explained to the liaison officers. After obtaining their consent for research cooperation, the online survey date and the number of nurses were determined. During the survey period, the liaison officers distributed the questionnaire QR code to the nurse group WeChat groups of various departments for online surveys. During the survey, 40 research subjects were selected by the convenience sampling method, their contact information was retained, and a second test was scheduled two weeks later. Data analysis A total of 1160 questionnaires were distributed in this study. After deleting invalid questionnaires and abnormal values with too small a proportion, a total of 1151 questionnaires were included. The effective recovery rate of questionnaires was 99.2%. Descriptive statistics were performed using SPSS27.0 Count data were described by frequency and percentage, and measurement data were described by mean ± standard deviation. In this study, the average scores of the eight dimensions of disaster nursing core competencies were taken as explicit variables, and the latent profile model was constructed using Mplus8.3. The Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size-adjusted BIC (aBIC), information entropy (Entropy), likelihood ratio test (LMR), and bootstrap-based likelihood ratio test (BLRT) were used as model fitting test indicators. Among them, the lower the values of AIC, BIC, and aBIC, the better the model fit. Entropy is the classification accuracy rate. When Entropy ≥ 0.8, it indicates that the classification accuracy is higher than 90%. When LMR and BLRT reach a significant level (P < 0.05), it indicates that the model fits well. The best category model is comprehensively selected in combination with the practical research significance[22].The influencing factors were explored by using univariate logistic regression and multivariate logistic regression. Results participants description A total of 1357 nurses were surveyed in this study and 1151 valid questionnaires were collected, the effective recovery rate of the questionnaire was 84.82%.Among the 1151 nurses,the vast majority of nurses (86.19%) were female, and the average age was 32.36 years(SD = 7.23 ). A total of 81.75%(n = 941) of the participants possessed a bachelor 's degree in nursing. Nearly 88.10% (n = 1014) of participants work in general hospital,the average working years was 10.32 years(SD = 7.81 ),more than half of the nurses had not participated in disaster relief in the past three years (74.20%), and they were more balanced in participating in disaster nursing training and exercises. Although there are different levels of emergency medical rescue teams in most units, but only 21.81% of nurses are members of the emergency medical rescue team .The detailed participant characteristics are listed in Table 1 . Table 1 Descriptive characteristics of the participants (N = 1151) Variable Categories N(%) or M ± SD Gender Male 159(13.81) Female 992(86.19) Age 32.36 ± 7.23 18–30 years old 551(47.87) 31–40 years old 433(37.62) 41–50 years old 152(13.21) 51–60 years old 15(1.30) Marital status Unmarried 472(41.00) Married 662(57.52) Divorce or widowed 17(1.48) Education junior college 181(15.73) undergraduate 941(81.75) postgraduate 29(2.52) hospital type Other specialties 36(3.13) Women's Hospital 29(2.52) general hospital 1014(88.10) Cancer hospital 35(3.04) occupational disease hospital 37(3.21) Department Internal medical 127(11.03) Surgery 129(11.21) ICU 252(21.89) emergency department 442(38.41) operating room 65(5.65) pediatrics 15(1.30) Department of obstetrics and gynecology 17(1.48) administration section 19(1.65) Others 85(7.38) Working years 10.32 ± 7.81 20years 137(11.90) Belong to the business establishment Yes 271(23.54) No 880(76.46) Nursing level N0 98(8.51) N1 253(21.98) N2 367(31.89) N3 248(21.55) N4 112(9.73) N5 27(2.34) No rating 46(4.00) Professional titles primary 256(22.24) intermediate 820(71.24) senior 75(6.52) Head nurse or nursing group leader Yes 256(30.93) No 795(69.07) Have participated in disaster relief Yes 297(25.80) No 854(74.20) Have participated in disaster / emergency / public emergency nursing training Yes 624(54.21) No 527(45.79) Have participated in disaster / emergency / public emergency drills Yes 685(59.51) No 466(40.49) Is there an emergency / emergency medical rescue team in unit Yes 859(74.63) No/unclear 292(25.37) Are you an emergency / emergency medical rescue team member Yes 251(21.81) No 900(78.19) Exploratory latent profile analysis In this study, a total of five latent profile models were explored,and their fitting indexes of different profile were presented in Table 2 . As the number of categories increased, the values of AIC, BIC, and aBIC gradually decreased. When the profile was divided into five, the LMR was not statistically significant ( P > 0.05 ), indicating that the four-profile model outperformed the five-profile model,but the four-profile model had a profile with a very small proportion,making it impractical to adopt.upon comparison,the three-profile model demonstrated lower AIC,BIC and aBIC values than the two-profile model ,with a higher entropy value of ≥ 0.8.Model 3 has ideal fit evaluation indexes and sample sizes for each group ,the average attribution probabilities of the three categories in Model 3 were 0.971,0.983 and 0.976 respectively(Table 3 ),and the LMR and BLRT for the category model were statistically significant ,considering the interpretability of each category and the practical significance of the categorisation, model 3 is finally selected as the best-fitting model,and is shown in Fig. 1 . In the three-category model,category1,with a total of 20.5% nurses, had the lowest scores on eight domains, indicating that the core competence of nurses in this category is the lowest among all the respondents, so it is named as the “deficient competence group”, which is represented by C1.Category 2 consisted of 62.6% nurses with moderate scores and was named the “moderate competence group”,which is represented by C2.The score of each dimension of the category 3 was the highest, indicating that the ability was the highest, so it was named “acceptable competence group”, accounting for 16.9%,which is represented by C3. Table 2 The core competencies in disaster nursing profile model fit information(N = 1151) Class K Log-likelihood AIC BIC aBIC Entropy LMR( P ) BLRT( P ) Proportion 1 16 −9488.924 19009.849 19090.623 19039.802 - - - - 2 25 −7453.734 14957.468 15083.677 15004.269 0.923 <0.001 <0.001 0.284/0.716 3 34 −6239.360 12546.719 12718.364 12610.370 0.954 <0.001 <0.001 0.205/0.626/0.169 4 43 −5635.928 11357.856 11574.937 11438.355 0.943 0.001 <0.001 0.219/0.544/0.074/0.163 5 52 −5385.265 10874.529 11137.045 10971.877 0.899 0.057 <0.001 0.048/0.142/0.255/0.406/0.149 k The Free parameters, AIC Akaike information criterion, BIC Bayesian information criterion, aBIC Sample-size adjusted Bayesian information criterion, LMRT Lo-Mendell-Rub test, BLRT Bootstrap likelihood ratio test Table 3 Average probability of attribution for each potential profile class Profile1(%) Profile2(%) Profile3(%) Profile1 0.971 0.029 0.000 Profile2 0.010 0.984 0.007 Profile3 0.000 0.029 0.971 Discussion Latent profile of the CCDN among nurses in Guangdong Province and application in nursing practice Through latent profile analysis, this study found that the core competencies of nurses in disaster nursing in Guangdong Province could be divided into three profiles: "deficient competence group" "medium-ability group" and "acceptable competence group". It was suggested that there was heterogeneity in the level of nurses’ CCDN. The medium-ability group had the largest number(62.9%), which account for more than half of the total, follow by the deficient competence group(20.5%), and the acceptable competence group had the least number(16.9%). This result showed that the CCDN of nurses in Guangdong Province was at the level of the medium on the low side,which was consistent with the research results of Wang[23]. In the moderate competence group (C2), the highest score was observed in the legal and ethical dimension, suggesting that most nurses in this group could demonstrate high levels of legal knowledge and ethical awareness during disaster events. This finding implies that current disaster nursing education not only emphasizes the cultivation of rescue skills but also places greater importance on nurses' legal and ethical thinking[24]. Additionally, the medium-ability group accounted for 26.6% of the study participants, indicating that the majority of nurses possess moderate levels of core competencies in disaster nursing.The acceptable competence group (C3) had the lowest proportion (16.9%) but a relatively high overall level, with the highest score in the intervention dimension. This suggests that the nurses in this group might have stronger core competencies in disaster nursing due to their direct involvement and proficiency in intervention and management of disaster events. Similarly, nurses in the deficient competence group (C1) also showed high scores in legal and ethical dimensions, which could be attributed to the fact that nurses with lower core competencies in disaster nursing might demonstrate a stronger sense of respect and humanitarianism during disaster events. The acceptable competence group (C3) had the lowest proportion (16.9%) but a relatively high overall level, with the highest score in the intervention dimension. It could be known from Table 3 that nurses in this group account for a large proportion in terms of having participated in training or drills in the past three years, and most of them had emergency rescue team in their unit. This suggests that the nurses in this group might have stronger core competencies in disaster nursing due to their direct involvement and proficiency in intervention and management of disaster events. Communication is one of the five common disaster nursing skills that nurses should possess[25]. However, this study found that across all three categories of nurses' core competencies in disaster nursing, communication was relatively weaker compared to other dimensions within each group, consistent with previous research[26]. In contrast, the intervention dimension performed relatively well, which might be related to an emphasis on theoretical knowledge transmission without sufficient practical experience, as well as a focus on professional skill training with less attention to communication skills development.The heterogeneity in nurses' core competencies in disaster nursing in Guangdong Province, as identified through latent profile analysis, provides more specific guidance for disaster nursing education. In the future, targeted and hierarchical training programs can be designed to address the weaknesses of each category, and multi-departmental and multi-disciplinary disaster response drills can be organized to enhance nurses' competencies. The influence of factors on the CCDN for nurses in Guangzhou Multivariate logistic regression analysis showed that nurses who had participated in disaster relief effortswere more likely to be classified into the high-ability group, consistent with Park's study[27]. This can be attributed to the fact that these nurses have amassed significant practical expertise in disaster nursing, acutely recognizing their limitations in emergency situations, and proactively engaged in focused learning endeavors to refine their overall competencies. Consequently, it underscores the imperative for nursing administrators to recognize disaster relief experience as a vital incubator for nurturing proficient disaster response leaders. They ought to systematically deploy potential key nurses in diverse rescue operations, thereby enabling them to bolster their disaster nursing skills through hands-on exposure and real-world experiences. The study also found that participating in disaster/emergency/public health emergency drills in the past three years was an effective way to improve nurses' core competencies in disaster nursing, consistent with numerous previous studies[28, 29]. This is presumably attributable to the inherently practical nature of disaster nursing, wherein nurses who have undergone rigorous disaster simulation exercises are equipped with a robust foundation of theoretical knowledge, refined skills, and a mature clinical mindset. This formidable combination equips them to execute disaster response roles with heightened efficacy and precision[30]. Since not every nurse has the opportunity to participate in actual disaster relief, simulating various emergency situations and conducting regular emergency drills are effective methods to improve nurses' rescue skills[31, 32]. Furthermore, nurses who were members of emergency/disaster medical response teams were more likely to be classified into the high-ability group. This phenomenon could be attributed to two pivotal factor. Firstly, these nurses enjoy a wealth of opportunities for disaster relief operations and specialized training, offering a crucial arena for knowledge consolidation and skill mastery.Secondly, rescue teams have well-established command and dispatch mechanisms[33], which can hone nurses' emergency response, communication, and management skills, fostering nursing talents capable of handling complex and ever-changing disaster relief tasks[34]. Studies by O'Leary, J [35], Chegini, Z [36], and Demirtas, H [37]have suggested that nurses who hold intermediate or higher professional titles exhibit a heightened level of core competencies in disaster nursing, attributed to their extensive working hours, a wealth of disaster response experience, and an increased avenue for specialized training. This cumulative advantage equips them with a superior grasp of disaster nursing practices. However, this conclusion contradicts our findings, which revealed that nurses with higher professional titles were more likely to be classified into the low-ability group, similar to Li, H's study [38]. This phenomenon might stem from the tendency of the participating medical institutions to prioritize junior nurses for disaster nursing training and relief endeavors, whereas senior nurses are often more engrossed in research activities and management roles, subsequently resulting in a more protracted disengagement from direct clinical practice. Conclusion The study categorize Guangzhou clinical nurses into three groups based on their disaster nursing core competencies: the "deficient competence group," the "moderate competence group," and the "acceptable competence group," revealing heterogeneity in their core capabilities. The "moderate competence group" scored the highest in legal and ethical dimensions, while the "acceptable competence group," despite having the lowest proportion, demonstrated a relatively elevated overall level, with the highest score in the intervention dimension. Compared to other dimensions, all groups showed relatively weaker performance in communication. Multivariate logistic regression analysis revealed that participation in disaster relief, involvement in drills, and being a member of emergency medical rescue teams are associated with enhanced core competencies. However, the study found that nurses with higher professional titles were paradoxically more likely to be classified into the "deficient competence group." This may be due to the prioritization of junior nurses for training and dispatch in disaster relief efforts in the involved medical institutions, leading to senior nurses' long-term detachment from clinical practice. Overall, these findings provide specific guidance for disaster nursing education and underscore the significance of targeted training and multidisciplinary drills in enhancing the capabilities of nurses.In the future, targeted and hierarchical training programs can be designed to address the weaknesses of each category, and multi-departmental and multi-disciplinary disaster response drills can be organized to enhance nurses' competencies. Abbreviations CCDN the Core Competencies in Disaster Nursing AIC Akaike Information Criterion aBIC Sample-size Adjusted Bayesian Information Criterion BIC Bayesian Information Criterion BLRT Bootstrap Likelihood Ratio Test LMR Lo–Mendell–Rubin Test LPA Latent profle analysis SD Standard deviation Declarations Ethics approval and consent to participate This study has obtained the approval and consent of the person in charge of the Disaster Specialized Committee of Guangdong Nursing Association and developed members in Guangzhou as investigators. This study is filled out anonymously. All subjects are informed and voluntarily participate in this study. This study has been reviewed by the ethics committee of Guangdong Provincial Hospital of Traditional Chinese Medicine (approval number YE202223801). Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available from the cor responding author on reasonable request. Competing interests The authors declare no competing interests. Funding This works was supported by Research on the strategy of improving the core competence of disaster nursing of emergency rescue nurses in Guangdong Province for the strategy of resilient city(2024WTSCX069),Guangdong Provincial Nursing Study on the measurement and promotion countermeasures of core competence of disaster nursing(GD23XGL086),Research and practice of disaster nursing course based on ICN guidelines Authors' contribution CHX,DSJ,DQY and TYBconceived and designed the study,ZQS and LJJcollected the data,MCQ and WGZ analyzed and interpreted the results,MCQ,WGZ,WDT and HPJdrafted the manuscript,TYB and ZQS critically reviewed the manuscript,All authors read and approved the fnal manuscript. Acknowledgements We acknowledge the hospital managers and all nurses who participated in this study. Authors' information 1 Guangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China. 2 Sun Yat-sen University Cancer Center,Guangzhou,Guangdong,China. 3 The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China. 4 Guangzhou Red Cross Hospital,Guangzhou,Guangdong,China. 5 Nursing Department of The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China. 6 Department of Emergency,The Second Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China. + Chaoqun Ma and Qishan Zhang contributed equally to this work. *Correspondence: Yibing Tan [email protected] References Barbier EB, Burgess JC: Sustainability and development after COVID-19. WORLD DEV 2020, 135:105082. Freysteinson WM, Celia T, Gilroy H, Gonzalez K: The experience of nursing leadership in a crisis: A hermeneutic phenomenological study. J NURS MANAGE 2021, 29(6):1535–1543. 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Li H, Dong S, He L, Wang R, Long S, He F, Tang H, Feng L: Nurses' core emergency competencies for COVID-19 in China: A cross-sectional study. INT NURS REV 2021, 68(4):524–532. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5201065","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366898058,"identity":"10570db7-1b66-491b-82e6-78ae1760285d","order_by":0,"name":"Chaoqun Ma","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China.","correspondingAuthor":false,"prefix":"","firstName":"Chaoqun","middleName":"","lastName":"Ma","suffix":""},{"id":366898064,"identity":"5495a4a8-bb40-4cbc-92d2-c6c03e102ed2","order_by":1,"name":"Qishan Zhang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Qishan","middleName":"","lastName":"Zhang","suffix":""},{"id":366898065,"identity":"5481cd48-1474-4a69-ae45-f3661701cd19","order_by":2,"name":"Gongzhen Wen","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China.","correspondingAuthor":false,"prefix":"","firstName":"Gongzhen","middleName":"","lastName":"Wen","suffix":""},{"id":366898066,"identity":"30bfda2f-9d6e-4604-ad39-035cbaa84c2e","order_by":3,"name":"Danting Weng","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China.","correspondingAuthor":false,"prefix":"","firstName":"Danting","middleName":"","lastName":"Weng","suffix":""},{"id":366898067,"identity":"da97897f-7ce7-43be-904e-e1643ec8e8d9","order_by":4,"name":"Pingjuan He","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China.","correspondingAuthor":false,"prefix":"","firstName":"Pingjuan","middleName":"","lastName":"He","suffix":""},{"id":366898068,"identity":"97ffb148-9d28-4395-b067-63bac4887aaf","order_by":5,"name":"Jinjia Lai","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China","correspondingAuthor":false,"prefix":"","firstName":"Jinjia","middleName":"","lastName":"Lai","suffix":""},{"id":366898069,"identity":"fa210f17-cf07-4207-8298-8959d03e66c5","order_by":6,"name":"Hanxi Chen","email":"","orcid":"","institution":"Guangzhou Red Cross Hospital,Guangzhou,Guangdong,China","correspondingAuthor":false,"prefix":"","firstName":"Hanxi","middleName":"","lastName":"Chen","suffix":""},{"id":366898070,"identity":"a59169d2-3082-40bf-9b85-372df617e60c","order_by":7,"name":"Shaojuan Deng","email":"","orcid":"","institution":"Nursing Department of The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China","correspondingAuthor":false,"prefix":"","firstName":"Shaojuan","middleName":"","lastName":"Deng","suffix":""},{"id":366898071,"identity":"059749d1-82d8-45c3-9e8e-2f997226b93e","order_by":8,"name":"Qiuying Deng","email":"","orcid":"","institution":"Department of Emergency,The Second Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China","correspondingAuthor":false,"prefix":"","firstName":"Qiuying","middleName":"","lastName":"Deng","suffix":""},{"id":366898072,"identity":"f49f650b-f6a1-4dae-9c96-6dc5d793ba02","order_by":9,"name":"Yibing Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYLCCxAYbGJOZsGoeiJY0UrUwNhwmQYs9+9ljEg93nLebPyP56QaGCuvEBvazB/DbwpOXJpF45nZy44w0sxsMZ9ITG3jyEgg4LMdMIrHtdjKzRA7bDca2w4kNEjwG+LXwvwFpOZfMBtbyjxgtEmBbDtjxgLU0EKPlxhtji8QzyQkSPM/MbiQcSzdu48nBr4W9P8fw5s8ddvby7cnPbnyosZbtZz+DXwsQsEgwgGITxEwAYjZC6oGA+QOQsCdC4SgYBaNgFIxUAAB+j0ME8HcsaAAAAABJRU5ErkJggg==","orcid":"","institution":"Guangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China.","correspondingAuthor":true,"prefix":"","firstName":"Yibing","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-10-04 02:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5201065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5201065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66921864,"identity":"b97c0c39-859a-48d6-823b-3e720585ff86","added_by":"auto","created_at":"2024-10-18 04:51:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63716,"visible":true,"origin":"","legend":"\u003cp\u003eCore Competencies in Disaster Nursing Potential Profile\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5201065/v1/a435c551fda5af57d7e63c0e.png"},{"id":79844810,"identity":"e660549f-6238-407a-9039-fbcdbe8a7016","added_by":"auto","created_at":"2025-04-03 13:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":920979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5201065/v1/60a32eec-d303-45e9-8055-41f44b1cea37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The core competencies in disaster nursing and influencing factors among clinical nurses in Guangzhou: a cross-sectional study based on latent profile analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2020, for instance, the unforeseen proliferation of COVID-19 impeded global endeavors to accomplish the Sustainable Development Goals worldwide[1].Nevertheless, this pandemic has also engendered an unparalleled surge in media and public attention towards nursing[2].Nurses are the largest group of healthcare professionals globally and serve as the primary responders during disaster rescue operations[3], as well as, nursing is an essential part of health care, and nurses play a key role in achieving universal health coverage (UHC) and medical rescue as well as improving the population health outcomes[4].Disasters often strike without warning, creating tumultuous and intricate scenarios at rescue locations that sharply diverge from the organized clinical milieus, where fixed departments and dedicated medical personnel thrive. Consequently, the presence of seasoned or specially trained nurses is paramount in navigating the intricacies of such unprecedented situations. The arduous treatment environment, marked by resource constraints, shortages of supplies and medications, pressing tasks, and psychological pressures, imposes immense challenges on nurses engaged in disaster relief operations[5].As per the 2020 State of the World's Nursing Report, the progression of disaster nursing disciplines stands as a crucial factor in defining the future trajectory of nursing on a global scale[6].\u003c/p\u003e \u003cp\u003eDisaster nursing is a specialized field that focuses on the provision of care and support individuals and communities who are affected by emergencies and crises[7]. Disaster competencies are crucial because an immediate and effective response to disasters directly impacts the life and safety of people. Te International Council of Nurses suggests imparting disaster nursing competencies to nurses, who form the core of healthcare professionals and serve in a variety of important disaster response roles[8]. However, disaster nursing faces major challenges that must be acknowledged and addressed[9].but currently, there is a deficiency in core disaster nursing capabilities. A survey[10]targeting nurses in East China shows that the disaster preparedness of nurses in China is at a medium level, and the situation in key departments and among community nurses is not ideal. Qualitative research on managers of the COVID-19 epidemic also indicates that senior nursing staff need to undertake new responsibilities and receive additional educational support[11]. In 2019, the International Council of Nurses released the Core Competencies in Disaster Nursing Version 2.0[12](CCDN2.0), expanding the four dimensions to eight dimensions and multiple competency indicators, and clearly stating that all nurses need to- reach level I. The research on core disaster nursing capabilities in China is not in line with international standards, manifested as inconsistent quality of evaluation scales and an unformed system of education and on-the-job training. Particularly, soft skills such as pre-disaster preparation, post-disaster recovery, and legal and ethical aspects need improvement. Some domestic scholars have explored the feasibility of this guideline in doctoral training systems[13]and COVID-19 nursing practices[14]. Given the strong guidance of CCDN2.0 but the need to adapt to China's national conditions, taking the 35 GPN capabilities as the starting point and combining China's nurse functions and emergency rescue systems, a localized scale is compiled to construct a measurement tool that conforms to international standards and China's actual situation, providing guidance for analyzing capability shortcomings and constructing training programs and improving the disaster response level of Chinese nurses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThis is a cross-sectional study conducted from December 2022 to February 2023, which adhered to the STROBE statement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eRegistered nurses from medical institutions including hospitals, primary-level medical facilities, and maternal and child health care hospitals in Guangzhou were selected as survey subjects via convenience sampling. The survey was conducted from December 2022 to February 2023. Inclusion criteria: ①Aged between 18 and 60 years. ②Having obtained the Nurse Practicing Certificate of the People's Republic of China. ③Providing informed consent and voluntarily participating in this study. Exclusion criteria: ①Those who failed the nursing annual assessment. ②Those who are absent from work due to maternity leave, further studies, etc. ③Those whose work units are not located in Guangzhou.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eEveritt[15]proposes that the sample size for exploratory factor analysis (EFA) should be at least 10 times the number of items in the scale. Bandalos[16]suggests that the sample size for confirmatory factor analysis (CFA) should be no less than 200 cases. Considering a 20% failure rate, the sample size is calculated as follows: [(35\u0026times;10)\u0026thinsp;+\u0026thinsp;200]\u0026times;(1\u0026thinsp;+\u0026thinsp;20%)\u0026thinsp;=\u0026thinsp;636. Considering a 20% sample attrition rate, the minimum requisite sample size is calculated as 636 \u0026divide; (1\u0026ndash;20%)\u0026thinsp;=\u0026thinsp;795. In this study, 1151 cases were finally included.\u003c/p\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eGeneral Information Questionnaire.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eReferring to the influencing factors of core competencies in disaster nursing in existing studies[17, 18], we designed 17 questions encompassing gender, age, marital status, number of children, highest educational attainment, hospital level, hospital type, department, years of nursing work, whether one is affiliated with the establishment, nursing level, professional title, whether one has participated in disaster relief other than the COVID-19 epidemic, whether one has participated in disaster nursing-related training conducted by academic societies in the past three years, whether one has participated in disaster drills in the past three years, the level of the emergency medical rescue team of the affiliated unit, and whether one is a member of the emergency medical rescue team.\u003c/p\u003e\n\u003ch3\u003eCore Competencies in Disaster Nursing Version 2.0(CCDN2.0)\u003c/h3\u003e\n\u003cp\u003e Under the auspices of the International Council of Nurses (ICN), in collaboration with multiple steering committees including the International Red Cross, the World Association for Disaster and Emergency Medicine, and the World Society of Disaster Nursing, the \"Core Competencies in Disaster Nursing Version 2.0\" was released in 2019. This guideline, with an overall Cronbach\u0026rsquo;s alpha coefficient of 0.980, divides the core competencies of disaster nursing into eight dimensions, namely preparation and planning, communication, incident management system, safety, assessment, intervention, recovery, law and ethics; and three levels, namely general nurses, advanced nurses, and disaster nursing experts. The researcher obtained the authorization of ICN in 2022.This study is based on the cross-cultural translation process of the Beaton assessment tool, having six steps: direct translation by two target-language-native translators; synthesis of results; back translation by two source-language-native translators in a back-to-back way; expert meeting review; modification through pre-survey; submission to original author/experts for review. Referring to relevant research[19\u0026ndash;21]and considering subsequent scale development needs, after research group discussion, the Chinese translation process of the CCDN2.0 guideline in this study has four steps: direct translation; synthesis; back translation; group discussion.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe secretary of the Disaster Specialized Committee of Guangdong Nursing Association was contacted to recruit committee members with work locations in Guangzhou as investigators for this study and to compile a list of hospital assistants for the survey. The background and purpose of this study were uniformly introduced and explained to the liaison officers. After obtaining their consent for research cooperation, the online survey date and the number of nurses were determined. During the survey period, the liaison officers distributed the questionnaire QR code to the nurse group WeChat groups of various departments for online surveys. During the survey, 40 research subjects were selected by the convenience sampling method, their contact information was retained, and a second test was scheduled two weeks later.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eA total of 1160 questionnaires were distributed in this study. After deleting invalid questionnaires and abnormal values with too small a proportion, a total of 1151 questionnaires were included. The effective recovery rate of questionnaires was 99.2%. Descriptive statistics were performed using SPSS27.0 Count data were described by frequency and percentage, and measurement data were described by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. In this study, the average scores of the eight dimensions of disaster nursing core competencies were taken as explicit variables, and the latent profile model was constructed using Mplus8.3. The Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size-adjusted BIC (aBIC), information entropy (Entropy), likelihood ratio test (LMR), and bootstrap-based likelihood ratio test (BLRT) were used as model fitting test indicators. Among them, the lower the values of AIC, BIC, and aBIC, the better the model fit. Entropy is the classification accuracy rate. When Entropy\u0026thinsp;\u0026ge;\u0026thinsp;0.8, it indicates that the classification accuracy is higher than 90%. When LMR and BLRT reach a significant level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), it indicates that the model fits well. The best category model is comprehensively selected in combination with the practical research significance[22].The influencing factors were explored by using univariate logistic regression and multivariate logistic regression.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003e \u003cb\u003eparticipants description\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 1357 nurses were surveyed in this study and 1151 valid questionnaires were collected, the effective recovery rate of the questionnaire was 84.82%.Among the 1151 nurses,the vast majority of nurses (86.19%) were female, and the average age was 32.36 years(SD\u0026thinsp;=\u0026thinsp;7.23 ). A total of 81.75%(n\u0026thinsp;=\u0026thinsp;941) of the participants possessed a bachelor 's degree in nursing. Nearly 88.10% (n\u0026thinsp;=\u0026thinsp;1014) of participants work in general hospital,the average working years was 10.32 years(SD\u0026thinsp;=\u0026thinsp;7.81 ),more than half of the nurses had not participated in disaster relief in the past three years (74.20%), and they were more balanced in participating in disaster nursing training and exercises. Although there are different levels of emergency medical rescue teams in most units, but only 21.81% of nurses are members of the emergency medical rescue team .The detailed participant characteristics are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDescriptive characteristics of the participants (N\u0026thinsp;=\u0026thinsp;1151)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN(%) or M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159(13.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e992(86.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e551(47.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433(37.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152(13.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e472(41.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e662(57.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorce or widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ejunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181(15.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eundergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e941(81.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(2.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ehospital type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther specialties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(3.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen's Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(2.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egeneral hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1014(88.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(3.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoccupational disease hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37(3.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal medical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127(11.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129(11.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252(21.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eemergency department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e442(38.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoperating room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65(5.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epediatrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of obstetrics and gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(1.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadministration section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19(1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85(7.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWorking years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;2years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169(14.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e534(46.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026minus;20years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311(27.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;20years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(11.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBelong to the business establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271(23.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e880(76.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eNursing level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98(8.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253(21.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e367(31.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248(21.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112(9.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27(2.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(4.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProfessional titles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256(22.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e820(71.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75(6.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHead nurse or nursing group leader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256(30.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e795(69.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave participated in disaster relief\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e297(25.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e854(74.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave participated in disaster / emergency / public emergency nursing training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e624(54.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e527(45.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHave participated in disaster / emergency / public emergency drills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e685(59.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466(40.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIs there an emergency / emergency medical rescue team in unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e859(74.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/unclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e292(25.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAre you an emergency / emergency medical rescue team member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e251(21.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e900(78.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExploratory latent profile analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, a total of five latent profile models were explored,and their fitting indexes of different profile were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As the number of categories increased, the values of AIC, BIC, and aBIC gradually decreased. When the profile was divided into five, the LMR was not statistically significant ( P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 ), indicating that the four-profile model outperformed the five-profile model,but the four-profile model had a profile with a very small proportion,making it impractical to adopt.upon comparison,the three-profile model demonstrated lower AIC,BIC and aBIC values than the two-profile model ,with a higher entropy value of \u0026ge;\u0026thinsp;0.8.Model 3 has ideal fit evaluation indexes and sample sizes for each group ,the average attribution probabilities of the three categories in Model 3 were 0.971,0.983 and 0.976 respectively(Table\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e),and the LMR and BLRT for the category model were statistically significant ,considering the interpretability of each category and the practical significance of the categorisation, model 3 is finally selected as the best-fitting model,and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the three-category model,category1,with a total of 20.5% nurses, had the lowest scores on eight domains, indicating that the core competence of nurses in this category is the lowest among all the respondents, so it is named as the \u0026ldquo;deficient competence group\u0026rdquo;, which is represented by C1.Category 2 consisted of 62.6% nurses with moderate scores and was named the \u0026ldquo;moderate competence group\u0026rdquo;,which is represented by C2.The score of each dimension of the category 3 was the highest, indicating that the ability was the highest, so it was named \u0026ldquo;acceptable competence group\u0026rdquo;, accounting for 16.9%,which is represented by C3.\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\u003eThe core competencies in disaster nursing profile model fit information(N\u0026thinsp;=\u0026thinsp;1151)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog-likelihood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLMR(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBLRT(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;9488.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19009.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19090.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19039.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7453.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14957.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15083.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15004.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.284/0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;6239.360\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e12546.719\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12718.364\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12610.370\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.954\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.205/0.626/0.169\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;5635.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11357.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11574.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11438.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.219/0.544/0.074/0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;5385.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10874.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11137.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10971.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.048/0.142/0.255/0.406/0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ek\u003c/em\u003e The Free parameters, \u003cem\u003eAIC\u003c/em\u003e Akaike information criterion, \u003cem\u003eBIC\u003c/em\u003e Bayesian information criterion,\u003cem\u003eaBIC\u003c/em\u003e Sample-size adjusted Bayesian information criterion, \u003cem\u003eLMRT\u003c/em\u003e Lo-Mendell-Rub test, \u003cem\u003eBLRT\u003c/em\u003e Bootstrap likelihood ratio test\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\u003eAverage probability of attribution for each potential profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfile1(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfile2(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile3(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLatent profile of the CCDN among nurses in Guangdong Province and application in nursing practice\u003c/h2\u003e \u003cp\u003eThrough latent profile analysis, this study found that the core competencies of nurses in disaster nursing in Guangdong Province could be divided into three profiles: \"deficient competence group\" \"medium-ability group\" and \"acceptable competence group\". It was suggested that there was heterogeneity in the level of nurses\u0026rsquo; CCDN. The medium-ability group had the largest number(62.9%), which account for more than half of the total, follow by the deficient competence group(20.5%), and the acceptable competence group had the least number(16.9%). This result showed that the CCDN of nurses in Guangdong Province was at the level of the medium on the low side,which was consistent with the research results of Wang[23].\u003c/p\u003e \u003cp\u003eIn the moderate competence group (C2), the highest score was observed in the legal and ethical dimension, suggesting that most nurses in this group could demonstrate high levels of legal knowledge and ethical awareness during disaster events. This finding implies that current disaster nursing education not only emphasizes the cultivation of rescue skills but also places greater importance on nurses' legal and ethical thinking[24].\u003c/p\u003e \u003cp\u003eAdditionally, the medium-ability group accounted for 26.6% of the study participants, indicating that the majority of nurses possess moderate levels of core competencies in disaster nursing.The acceptable competence group (C3) had the lowest proportion (16.9%) but a relatively high overall level, with the highest score in the intervention dimension. This suggests that the nurses in this group might have stronger core competencies in disaster nursing due to their direct involvement and proficiency in intervention and management of disaster events. Similarly, nurses in the deficient competence group (C1) also showed high scores in legal and ethical dimensions, which could be attributed to the fact that nurses with lower core competencies in disaster nursing might demonstrate a stronger sense of respect and humanitarianism during disaster events.\u003c/p\u003e \u003cp\u003eThe acceptable competence group (C3) had the lowest proportion (16.9%) but a relatively high overall level, with the highest score in the intervention dimension. It could be known from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that nurses in this group account for a large proportion in terms of having participated in training or drills in the past three years, and most of them had emergency rescue team in their unit. This suggests that the nurses in this group might have stronger core competencies in disaster nursing due to their direct involvement and proficiency in intervention and management of disaster events.\u003c/p\u003e \u003cp\u003eCommunication is one of the five common disaster nursing skills that nurses should possess[25]. However, this study found that across all three categories of nurses' core competencies in disaster nursing, communication was relatively weaker compared to other dimensions within each group, consistent with previous research[26].\u003c/p\u003e \u003cp\u003eIn contrast, the intervention dimension performed relatively well, which might be related to an emphasis on theoretical knowledge transmission without sufficient practical experience, as well as a focus on professional skill training with less attention to communication skills development.The heterogeneity in nurses' core competencies in disaster nursing in Guangdong Province, as identified through latent profile analysis, provides more specific guidance for disaster nursing education. In the future, targeted and hierarchical training programs can be designed to address the weaknesses of each category, and multi-departmental and multi-disciplinary disaster response drills can be organized to enhance nurses' competencies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe influence of factors on the CCDN for nurses in Guangzhou\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis showed that nurses who had participated in disaster relief effortswere more likely to be classified into the high-ability group, consistent with Park's study[27]. This can be attributed to the fact that these nurses have amassed significant practical expertise in disaster nursing, acutely recognizing their limitations in emergency situations, and proactively engaged in focused learning endeavors to refine their overall competencies. Consequently, it underscores the imperative for nursing administrators to recognize disaster relief experience as a vital incubator for nurturing proficient disaster response leaders. They ought to systematically deploy potential key nurses in diverse rescue operations, thereby enabling them to bolster their disaster nursing skills through hands-on exposure and real-world experiences.\u003c/p\u003e \u003cp\u003e The study also found that participating in disaster/emergency/public health emergency drills in the past three years was an effective way to improve nurses' core competencies in disaster nursing, consistent with numerous previous studies[28, 29]. This is presumably attributable to the inherently practical nature of disaster nursing, wherein nurses who have undergone rigorous disaster simulation exercises are equipped with a robust foundation of theoretical knowledge, refined skills, and a mature clinical mindset. This formidable combination equips them to execute disaster response roles with heightened efficacy and precision[30]. Since not every nurse has the opportunity to participate in actual disaster relief, simulating various emergency situations and conducting regular emergency drills are effective methods to improve nurses' rescue skills[31, 32].\u003c/p\u003e \u003cp\u003eFurthermore, nurses who were members of emergency/disaster medical response teams were more likely to be classified into the high-ability group. This phenomenon could be attributed to two pivotal factor. Firstly, these nurses enjoy a wealth of opportunities for disaster relief operations and specialized training, offering a crucial arena for knowledge consolidation and skill mastery.Secondly, rescue teams have well-established command and dispatch mechanisms[33], which can hone nurses' emergency response, communication, and management skills, fostering nursing talents capable of handling complex and ever-changing disaster relief tasks[34].\u003c/p\u003e \u003cp\u003eStudies by O'Leary, J [35], Chegini, Z [36], and Demirtas, H [37]have suggested that nurses who hold intermediate or higher professional titles exhibit a heightened level of core competencies in disaster nursing, attributed to their extensive working hours, a wealth of disaster response experience, and an increased avenue for specialized training. This cumulative advantage equips them with a superior grasp of disaster nursing practices. However, this conclusion contradicts our findings, which revealed that nurses with higher professional titles were more likely to be classified into the low-ability group, similar to Li, H's study [38]. This phenomenon might stem from the tendency of the participating medical institutions to prioritize junior nurses for disaster nursing training and relief endeavors, whereas senior nurses are often more engrossed in research activities and management roles, subsequently resulting in a more protracted disengagement from direct clinical practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study categorize Guangzhou clinical nurses into three groups based on their disaster nursing core competencies: the \"deficient competence group,\" the \"moderate competence group,\" and the \"acceptable competence group,\" revealing heterogeneity in their core capabilities. The \"moderate competence group\" scored the highest in legal and ethical dimensions, while the \"acceptable competence group,\" despite having the lowest proportion, demonstrated a relatively elevated overall level, with the highest score in the intervention dimension. Compared to other dimensions, all groups showed relatively weaker performance in communication. Multivariate logistic regression analysis revealed that participation in disaster relief, involvement in drills, and being a member of emergency medical rescue teams are associated with enhanced core competencies. However, the study found that nurses with higher professional titles were paradoxically more likely to be classified into the \"deficient competence group.\" This may be due to the prioritization of junior nurses for training and dispatch in disaster relief efforts in the involved medical institutions, leading to senior nurses' long-term detachment from clinical practice. Overall, these findings provide specific guidance for disaster nursing education and underscore the significance of targeted training and multidisciplinary drills in enhancing the capabilities of nurses.In the future, targeted and hierarchical training programs can be designed to address the weaknesses of each category, and multi-departmental and multi-disciplinary disaster response drills can be organized to enhance nurses' competencies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCCDN the Core Competencies in Disaster Nursing\u003c/p\u003e\u003cp\u003eAIC Akaike Information Criterion\u003c/p\u003e\u003cp\u003eaBIC Sample-size Adjusted Bayesian Information Criterion\u003c/p\u003e\u003cp\u003eBIC Bayesian Information Criterion\u003c/p\u003e\u003cp\u003eBLRT Bootstrap Likelihood Ratio Test\u003c/p\u003e\u003cp\u003eLMR Lo–Mendell–Rubin Test\u003c/p\u003e\u003cp\u003eLPA Latent profle analysis\u003c/p\u003e\u003cp\u003eSD Standard deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has obtained the approval and consent of the person in charge of the Disaster Specialized Committee of Guangdong Nursing Association and developed members in Guangzhou as investigators. This study is filled out anonymously. All subjects are informed and voluntarily participate in this study. This study has been reviewed by the ethics committee of Guangdong Provincial Hospital of Traditional Chinese Medicine (approval number YE202223801).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the cor\u003c/p\u003e\n\u003cp\u003eresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis works was supported by Research on the strategy of improving the core competence of disaster nursing of emergency rescue nurses in Guangdong Province for the strategy of resilient city(2024WTSCX069),Guangdong Provincial Nursing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy on the measurement and promotion countermeasures of core competence of disaster nursing(GD23XGL086),Research and practice of disaster nursing course based on ICN guidelines\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHX,DSJ,DQY and TYBconceived and designed the study,ZQS and LJJcollected the data,MCQ and WGZ analyzed and interpreted the results,MCQ,WGZ,WDT and HPJdrafted the manuscript,TYB and ZQS critically reviewed the manuscript,All authors read and approved the fnal manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the hospital managers and all nurses who participated in\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ethis study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eGuangzhou University of Chinese Medicine College of Nursing，Guangzhou,Guangdong,China.\u003csup\u003e2\u003c/sup\u003eSun Yat-sen University Cancer Center,Guangzhou,Guangdong,China.\u003csup\u003e3\u003c/sup\u003eThe First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China.\u003csup\u003e4\u003c/sup\u003eGuangzhou Red Cross Hospital,Guangzhou,Guangdong,China.\u003csup\u003e5\u003c/sup\u003eNursing Department of The First Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China.\u003csup\u003e6\u003c/sup\u003eDepartment of Emergency,The Second Affiliated Hospital of Guangzhou University of Chinese Medicine,Guangzhou,Guangdong,China.\u003csup\u003e+\u003c/sup\u003eChaoqun Ma and Qishan Zhang contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e*Correspondence:\u003c/p\u003e\n\u003cp\u003eYibing Tan\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Barbier EB, Burgess JC: Sustainability and development after COVID-19. \u003cem\u003eWORLD DEV\u003c/em\u003e 2020, 135:105082.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Freysteinson WM, Celia T, Gilroy H, Gonzalez K: The experience of nursing leadership in a crisis: A hermeneutic phenomenological study. \u003cem\u003eJ NURS MANAGE\u003c/em\u003e 2021, 29(6):1535\u0026ndash;1543.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Said NB, Chiang V: The knowledge, skill competencies, and psychological preparedness of nurses for disasters: A systematic review. \u003cem\u003eINT EMERG NURS\u003c/em\u003e 2020, 48:100806.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e World Health Organization: The WHO Global Strategic Directions for Nursing and Midwifery (2021\u0026ndash;2025).; 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\u003cli\u003e\u003cspan\u003e Jang I, Kim JS, Lee J, Seo Y: Educational needs and disaster response readiness: A cross-sectional study of clinical nurses. \u003cem\u003eJ ADV NURS\u003c/em\u003e 2021, 77(1):189\u0026ndash;197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Hung K, Mashino S, Chan E, MacDermot MK, Balsari S, Ciottone GR, Della CF, Dell'Aringa MF, Egawa S, Evio BD \u003cem\u003eet al\u003c/em\u003e: Health Workforce Development in Health Emergency and Disaster Risk Management: The Need for Evidence-Based Recommendations. \u003cem\u003eINT J ENV RES PUB HE\u003c/em\u003e 2021, 18(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Mahoney LE, Whiteside DF, Belue HE, Mortisugu KP, Esch VH: Disaster medical assistance teams. \u003cem\u003eANN EMERG MED\u003c/em\u003e 1987, 16(3):354\u0026ndash;358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e O'Leary J: Comparison of self-assessed competence and experience among critical care nurses. \u003cem\u003eJ NURS MANAGE\u003c/em\u003e 2012, 20(5):607\u0026ndash;614.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Chegini Z, Arab-Zozani M, Kakemam E, Lotfi M, Nobakht A, Aziz KH: Disaster preparedness and core competencies among emergency nurses: A cross-sectional study. \u003cem\u003eNURS OPEN\u003c/em\u003e 2022, 9(2):1294\u0026ndash;1302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Demirtaş H, Altuntaş S: Nurses' competence levels in disaster nursing management in Turkey: A comparative cross-sectional study. \u003cem\u003eINT NURS REV\u003c/em\u003e 2024, 71(3):556\u0026ndash;562.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Li H, Dong S, He L, Wang R, Long S, He F, Tang H, Feng L: Nurses' core emergency competencies for COVID-19 in China: A cross-sectional study. \u003cem\u003eINT NURS REV\u003c/em\u003e 2021, 68(4):524\u0026ndash;532.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5201065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5201065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003eTo explore the core competencies in disaster nursing possessed by clinical nurses in Guangzhou, China, and then to uncover the influencing factors that shape these competencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA cross-sectional design of 1,151 nurses from Guangzhou was conducted from December 2022 to February 2023.A localized scale was compiled to construct a measurement tool that conforms to international standards and China's actual situation, providing guidance for analyzing capability shortcomings and constructing training programs and improving the disaster response level of Chinese nurses. Latent Profile Analysis (LPA), univariate analysis, and multivariate logistic regression analysis was conducted to explore the predictors of latent profile membership and their influencing factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 1151 questionnaires were valid.The core competencies of nurses were categorized into three distinct groups: deficient competence group(62.6%),moderate competence group(26.6%), and acceptable competence group(16.9%).Factors such as participation in disaster relief, drills, and belonging to emergency/disaster medical response teams were found to be significant influences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003eHeterogeneity exists in the core competencies of disaster nursing among nurses in Guangzhou, with the moderate competence group scoring the highest in the legal and ethical dimensions. This study recommends the implementation of targeted training programs and multidisciplinary drills to enhance the capabilities of nurses.\u003c/p\u003e","manuscriptTitle":"The core competencies in disaster nursing and influencing factors among clinical nurses in Guangzhou: a cross-sectional study based on latent profile analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 04:51:52","doi":"10.21203/rs.3.rs-5201065/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":"0ff24a0d-0243-4949-bc48-b533a93901de","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-03T13:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 04:51:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5201065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5201065","identity":"rs-5201065","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}