Journal of Nursing最新文献

{"title":"[Preliminary Study on the Relationship Between Organizational Climate, Leadership Style, and Job Satisfaction in the Context of Nurse Practitioners].","authors":"Chien-Mei Sung, Ying-Tzu Lin","doi":"10.6224/JN.202410_71(5).07","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).07","url":null,"abstract":"<p><strong>Background: </strong>Job satisfaction in nurse practitioners is influenced by the level of autonomy and empowerment they perceive within their practice environment. Little in-depth research has been done to explore the relationship among organizational climate, leadership style, and job satisfaction in the context of nurse practitioners.</p><p><strong>Purpose: </strong>This study was developed to explore the relationship among organizational climate, leadership style, and job satisfaction in nurse practitioners with the goal of enhancing their job satisfaction.</p><p><strong>Methods: </strong>A cross-sectional correlational approach and snowball sampling method were employed to recruit 400 qualified nurse practitioners to complete an online survey. This survey was a structured questionnaire consisting of the Practice Organizational Climate Scale, Leadership Style Scale, and Job Satisfaction Scale. Statistical analyses used included the independent t test, one-way ANOVA, Pearson's correlation, and multiple linear regression analysis.</p><p><strong>Results: </strong>Most of the participants were 31 to 50 years old. A significant and positive correlation was identified among overall organizational climate, leadership style, and job satisfaction. In terms of organizational climate components, only professional visibility was not found to significantly correlate with job satisfaction. Transformational leadership, servant leadership, independent work, and relationships with support and management departments collectively explained nearly 72.4% of overall job satisfaction.</p><p><strong>Conclusions / implications for practice: </strong>The findings demonstrate job satisfaction in nurse practitioners to be significantly influenced by organizational climate and leadership style. Thus, fostering a positive organizational climate and enhancing transformational and servant leadership styles may be expected to improve job satisfaction in this group substantively. Therefore, it is recommended healthcare institutions focus on improving the organizational climate, providing more autonomy and support, and enhancing leadership training for supervisors with the goal of increasing overall job satisfaction and retention rates among nurse practitioners.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"46-57"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A Study on the Life Attitudes, Fear of Death, and Dying Care Abilities of Nursing Assistants in Long-Term Care Institutions].","authors":"Shu-Ling Lee, Hung-Lan Wu","doi":"10.6224/JN.202410_71(5).08","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).08","url":null,"abstract":"<p><strong>Background: </strong>End-of-life care in long-term care institutions is increasingly important. Nursing assistants are the primary care workforce, and their end-of-life care capabilities affect the quality of care provided to residents as well as residents' physical and mental health.</p><p><strong>Purpose: </strong>This study was designed to explore the life attitudes, fear of death status, end-of-life care abilities and experience, and coping styles of nursing assistants in long-term care institutions in the eastern region and the effects of these variables on care.</p><p><strong>Methods: </strong>A cross-sectional, mixed-methods research design was used. The study included 165 nursing assistants working in long-term care facilities in Taitung and Hualien counties. The study questionnaire included items covering life attitudes, fear of death, and dying care abilities. In addition, in-depth interviews were conducted with 12 of the participants.</p><p><strong>Results: </strong>Most of the participants held positive attitudes toward life and reported having a low to moderate fear of death. A significant and positive correlation between life attitude and dying care ability was identified (r = .426, p < .001). Although average ability to provide physical care to residents at end of life was found to be relatively good, ability to provide emotional support and allow residents to express their emotions was generally poor. The scales of fear of death, life attitude toward love and care, ideals, and high school education were identified as significant predictors of dying care ability, together explaining 22.8% of the variance. The interview results were summarized into five major themes that also echoed the quantitative results.</p><p><strong>Conclusions / implications for practice: </strong>Attitudes toward life and fear of death partially were shown to influence the dying care ability of the nursing assistants in this study. Dying care skills should be strengthened in nursing assistants, and they should be encouraged to explore the meanings of life and death through on-the-job education. Also, long-term care institutions should show concern for and support their nursing assistants by sponsoring case discussions, care experience sharing sessions, and support groups.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"58-69"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Improving the Accuracy of Patient Self-Care After Joint Replacement Surgery].","authors":"Chen-Chan Kuo, Zi-Ting Ding, Chieh-Ling Huang, Hsiao-Yun Chien, Shu-Wei Hsu, Chiu-Tzu Lin","doi":"10.6224/JN.202410_71(5).09","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).09","url":null,"abstract":"<p><strong>Background & problems: </strong>Joint replacement wound stitches are typically not removed until two weeks after the operation. Therefore, patients with joint replacement must be able to execute proper wound care after discharge from the hospital to reduce the risk of wound infection. Prior data from Chang Gung Memorial Hospital's orthopedics ward indicate only 69% of joint-replacement patients are able to perform wound care properly at home. Potential causes for this noted by patients include age-related forgetfulness, being unable to discern redness or swelling in the wound, and language comprehension difficulties (i.e., Taiwanese vs. Mandarin). Poor rates of wound care may also be attributable to incomplete wound care education by nursing staff and wound care education being provided without adequate practice on the day of patient discharge.</p><p><strong>Purpose: </strong>This project was implemented to improve the accuracy of wound self-care performed by patients after joint replacement surgery and to enhance their related knowledge and wound-care technical correctness.</p><p><strong>Resolution: </strong>A wound care education checklist, wound care cue cards, wound care video clips, wound condition red flag cue cards, and customized wound care pack were proposed and implemented.</p><p><strong>Results: </strong>The rate of accuracy of wound self-care performance increased from 69% pretest to 98% posttest, showing the intervention to have effectively improved post-discharge wound care quality.</p><p><strong>Conclusions: </strong>To effectively improve the post-discharge accuracy of wound self-care in patients with joint replacement, the consistency of post-surgery wound care education given by nursing staff to patients should be improved, patients should be reminded of wound assessment and care steps, and patients should be aware that wound abnormalities require an immediate return to the hospital for follow-up treatment.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"70-78"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Nursing Education in the Era of Generative Artificial Intelligence: Are We Ready?]","authors":"Shu-Ling Chen","doi":"10.6224/JN.202410_71(5).01","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).01","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Generative artificial intelligence (GAI) has taken the world by storm, causing notable tension within the field of education. Nursing education is no exception, facing imminent challenges and opportunities. GAI, a unique and immensely powerful technology championed by ChatGPT (Chat generative pre-trained transformer), represents a new frontier in artificial intelligence. ChatGPT, a product of deep learning - a subset of machine learning that mirrors the human brain's approach to learning and responding to data, information, and prompts - exemplifies this technological leap (Sahoo et al., 2022). GAI stands out for its ability not only to provide responses but also to generate the content of those responses, surpassing the human-like interactions typically seen in conversational AI (Lim et al., 2023; Su & Yang, 2023). Currently, ChatGPT has demonstrated significant application potential in nursing education in various aspects. For example, ChatGPT provides personalized learning (Tam et al., 2023); is easy to use (Vaughn et al., 2024); provides rapid information (Goktas et al., 2024; Liu et al., 2023), rapid responses, and assistance in writing (Sun & Hoelscher, 2023); improves students' problem-solving and critical thinking skills (Goktas et al., 2024; Sun & Hoelscher, 2023); supports educators in developing curricula and preparing course materials and may be used in translation processes (Tam et al., 2023); and helps healthcare professionals better understand complex medical concepts and procedures by providing easily comprehensible and up-to-date information (Krüger et al., 2023). Therefore, integrating ChatGPT into nursing education not only provides students with a more effective and interactive learning experience but also offers educators supportive tools that are directly applicable in teaching. These technologies can enhance / improve teaching by providing personalized learning solutions through, for example, generating teaching cases and simulating clinical scenarios to enhance the learning experience of students (Liu et al., 2023; Vaughn et al., 2024). Despite the significant benefits realized, nursing education in the era of GAI also faces challenges and limitations. Over-reliance on ChatGPT may limit students' critical thinking, problem-solving, and innovation capabilities, leading to a lack of independent thought. Educators should integrate GAI-supported tools into the learning process, but encourage and guide students to use ChatGPT as a supplementary learning tool rather than a substitute (Tam et al., 2023). This approach will help ensure students develop the skills and knowledge necessary to use the technology responsibly and ethically and allow educators to better address key related challenges, enhance education quality, and lay a foundation for cultivating high-quality nursing professionals. GAI is inevitable, and banning it may lead to increased attention and psychological reactance, making students more eager to access th","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"4-6"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A Guide to Network Meta-Analysis Using Generative AI and No-Code Tools].","authors":"Jen-Wei Liu","doi":"10.6224/JN.202410_71(5).05","DOIUrl":"10.6224/JN.202410_71(5).05","url":null,"abstract":"<p><p>Network meta-analysis (NMA), an increasingly appealing method of statistical analysis, is superior to traditional analysis methods in terms of being able to compare multiple medical treatment methods in one analysis run. In recent years, the prevalence of NMA in the medical literature has increased significantly, while advances in NMA-related statistical methods and software tools continue to improve the effectiveness of this approach. Various commercial and free statistical software packages, some of which employ generative artificial intelligence (GAI) to generate code, have been developed for NMA, leading to numerous innovative developments. In this paper, the use of generative AI for writing R programming language scripts and the netmeta package for performing NMA are introduced. Also, the web-based tool ShinyNMA is introduced. ShinyNMA allows users to conduct NMA using an intuitive \"clickable\" interface accessible via a standard web browser, with visual charts employed to present results. In the first section, we detail the netmeta package documentation and use ChatGPT (chat generative pre-trained transformer) to write the R scripts necessary to perform NMA with the netmeta package. In the second section, a user interface is developed using the Shiny package to create a ShinyNMA tool. This tool provides a no-code option for users unfamiliar with programming to conduct NMA statistical analysis and plotting. With appropriate prompts, ChatGPT can produce R scripts capable of performing NMA. Using this approach, an NMA analysis tool is developed that meets the research objectives, and potential applications are demonstrated using sample data. Using generative AI and existing statistical packages or no-code tools is expected to make conducting NMA analysis significantly easier for researchers. Moreover, greater access to results generated by NMA analyses will enable decision-makers to review analysis results intuitively in real-time, enhancing the importance of statistical analysis in medical decision-making. Furthermore, enabling non-specialists to conduct clinically meaningful systematic reviews may be expected to sustainably improve analytical capabilities and produce higher-quality evidence.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"29-35"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Promoting Equity, Diversity, and Inclusion in Healthcare: An Example of the COVID-19 Pandemic].","authors":"Mei-Fang Chen, Mei-Ling Yeh","doi":"10.6224/JN.202410_71(5).12","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).12","url":null,"abstract":"<p><p>Healthcare systems must embody equity, diversity, and inclusion (EDI) and, in the event of unfairness, appropriate policies / countermeasures should be enacted. The healthcare system response to the COVID-19 pandemic not only highlighted how socioeconomic disparities affect mortality risk but also posed significant challenges to the successful practice of EDI in healthcare. In light of this, this article was written to provide an overview of EDI, analyze the international efforts to promote it, and suggest strategies for promoting EDI in infectious disease healthcare using COVID-19 as an example. In healthcare settings, equity centers on ensuring patients receive fair treatment regardless of race, gender, age, or socioeconomic status; diversity centers on healthcare providers understanding the uniqueness of patients from different cultural backgrounds and the health barriers they face; and inclusion centers on ensuring patients are treated with respect and given the attention they deserve. During pandemics, social determinants of health (SDOH) greatly impact patient health outcomes and hinder the practice of EDI. Reflecting on the impact of COVID-19, healthcare systems can actively apply EDI in clinical practice to provide to all patients equitable access to healthcare opportunities and outcomes. Practical strategies include establishing EDI committees within healthcare systems, monitoring relevant data, conducting staff training, and continuously addressing the SDOH and needs of marginalized groups to achieve EDI in healthcare.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"96-103"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Oral Feeding Readiness Assessment Tools for Preterm Infants].","authors":"Chun-Chi Huang, Tzu-Ting Liao, Mei-Chih Huang","doi":"10.6224/JN.202410_71(5).11","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).11","url":null,"abstract":"<p><p>Due to their underdeveloped physiological maturity, preterm infants often face challenges related to sucking, breathing, and swallowing coordination during initial feeding. This lack of coordination may lead to episodes of apnea and choking, resulting in unstable vital signs. Preterm infants with this issue must gradually learn oral feeding skills appropriate to their developmental stage. Registered nurses play a critical role in assessing the right time to transition from tube to oral feeding and in providing a safe and positive oral feeding experience. In this article, three validated assessment tools for feeding premature infants are introduced, accompanied by clinical research data demonstrating their use in clinical practice. These three tools include: (1) the Neonatal Oral Motor Assessment Scale, which is applied to evaluate oral motor skills using observations of nonnutritive sucking and the sucking state during the two minutes before feeding; (2) the Premature Oral Feeding Readiness Assessment Scale, which is used to assess readiness for oral feeding in preterm infants; and (3) the Early Feeding Skills assessment, which is used to evaluate the oral feeding skills of preterm infants. These tools aid nurses in helping preterm infants achieve independent oral feeding, facilitating earlier discharge and return to home. The clinical implications and effectiveness of these tools are also discussed to provide to nurses the means and confidence necessary to apply them appropriately in clinical settings.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"89-95"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Application of Artificial Intelligence Models in Nursing Research].","authors":"Cheng-Pei Lin, Lu-Yen Anny Chen","doi":"10.6224/JN.202410_71(5).03","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).03","url":null,"abstract":"<p><p>In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"14-20"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Analysis of the Effectiveness of a Fall Prevention Program Incorporating an Interprofessional Team Collaboration Model on Reducing Fall Risk in Elderly Living in Long-term Care Facilities].","authors":"Shu-Tsun Lin, Shu-Fang Chang","doi":"10.6224/JN.202410_71(5).10","DOIUrl":"https://doi.org/10.6224/JN.202410_71(5).10","url":null,"abstract":"<p><strong>Background: </strong>Concurrent with population ageing, falls have become a significantly more challenging public health issue among older adults. Three years of data collected recently from a nursing home in northern Taiwan reveals an increasing trend in fall density that is primarily associated with aging, physiological decline, chronic diseases, polypharmacy, osteoporosis, and lack of exercise. The percentage of nursing home residents at high risk of falls is currently at 12.6%, and the fall rate has been reported as reaching as high as 30% annually.</p><p><strong>Purpose: </strong>A fall prevention program was implemented to reduce the fall incidence rate to 18%, with secondary goals of improving fall prevention awareness, behavior, self-efficacy, lower limb muscle strength, balance, and gait by 10% on average, respectively, between pre-test and post-test.</p><p><strong>Resolution: </strong>From September 30, 2023 to February 29, 2024, a health promotion activity and fall prevention exercise course were implemented using an interdisciplinary team collaboration model over a six-week period, providing individualized exercise for the participants.</p><p><strong>Results: </strong>The study included 20 older adults with an average age of 88 years. Most (90%; n = 18) had chronic diseases, 25% (n = 5) were on more than nine medications, 70% (n = 14) had reduced bone mass, and 40% (n = 8) were at high risk of falls, with a fall incidence rate of 30% during the immediately preceding year. Post-intervention, the fall incidence rate dropped to 5%, fall prevention awareness, behavior, and self-efficacy increased by 18.3%, and lower limb muscle strength, balance, and gait improved by 11.7%. The post-test results in fall prevention awareness, behavioral changes, self-efficacy, and lower limb strength, balance, and gait were all significantly better than pre-test results, with all results achieving statistical significance.</p><p><strong>Conclusions: </strong>The project results support the positive effects of the developed intervention effectively on elderly physical fitness and fall risk, providing valuable insights for the implementation of fall prevention strategies in nursing homes.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"71 5","pages":"79-88"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[The Correlates and Predictive Factors of Work Stress, Job Satisfaction, and Resilience in Nurses in the Post-COVID-19 Era].","authors":"Chiu-Chu Chen, Tzu-Yueh Lee, Li-Mei Chao, Tzu-Jung Wu","doi":"10.6224/JN.202408_71(4).07","DOIUrl":"10.6224/JN.202408_71(4).07","url":null,"abstract":"<p><strong>Background: </strong>Individuals in the asymptomatic incubation period of COVID-19 are highly contagious. This threat of asymptomatic transmission contributes to increased stress among nursing staffs and undermines their resilience.</p><p><strong>Purpose: </strong>This study was designed to explore the correlates and predictive factors of resilience in the contexts of work stress and job satisfaction among nursing staffs.</p><p><strong>Methods: </strong>A cross-sectional study design was employed on a convenience sample of 408 nurses. The survey included a demographic datasheet, the Nurse Occupational Stressor Scale, Job Satisfaction Scale, and Resilience Scale. Inferential statistics were conducted using independent sample t test, Pearson correlation analysis, and stepwise multiple linear regression.</p><p><strong>Results: </strong>The participants were an average 32.6 years old. The average resilience score indicated a \"moderate\" resilience level. Resilience was treated as the dependent variable, while the demographic variables, Nurse Occupational Stressor Scale score, and job satisfaction dimension scores were treated as independent variables. Stepwise regression analysis was used to identify the key predictors of resilience, which included professional autonomy and development (β = .468, p <.001), occupational hazards (β = .163, p <.001), interpersonal interaction and collaboration (β = .223, p < .001), self-perceived economic status (β = -.093, p < .05), supervisor's leadership style (β = -.118, p < .05), and marital status (β = .078, p < .05). The model explained 39.4% of the total variance.</p><p><strong>Conclusions / implications for practice: </strong>The results of this study support healthcare providers promoting resilience in several specific ways. Healthcare organizations should enhance professional competence through professional education and training programs; improve workplace safety; foster an atmosphere conducive to team cooperation; provide job support through mentorship and apprenticeship systems and caring leadership from nursing supervisors; continuously conduct caring and stress-relief activities; and utilize online self-report health questionnaires to enable nursing staff facing psychological and emotional challenges to seek professional counseling and support. Enhancing resilience strategies on a long-term basis can improve the mental health of nursing staff, which may be expected to enhance the quality of patient care.</p>","PeriodicalId":35672,"journal":{"name":"Journal of Nursing","volume":"70 4","pages":"44-56"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}