AIMS Public Health最新文献

{"title":"Silicosis mortality in Spain (1999-2020): A temporal and geographical approach.","authors":"Germán Sánchez-Díaz, Greta Arias-Merino, Elisa Gallego, Rodrigo Sarmiento-Suárez, Verónica Alonso-Ferreira","doi":"10.3934/publichealth.2024036","DOIUrl":"https://doi.org/10.3934/publichealth.2024036","url":null,"abstract":"<p><strong>Background: </strong>Silicosis is an occupational respiratory disease linked to silica dust inhalation. The main driver was traditional coal mining, but in recent decades, new sources of exposure have emerged. Our aim in this study was to assess the temporal and spatial distribution of mortality due to this disease over a 22-year period in Spain.</p><p><strong>Methods: </strong>Silicosis records, as an Underlying Cause of Death, were extracted from the National Institute of Statistics from 1999 to 2020 using the International Classification of Diseases 10th revision (code J62.8). Age- and sex-adjusted mortality rates per 1,000,000 inhabitants were calculated for the territory and by province. A geographic analysis was performed, and clusters of deaths were identified at the municipal level, and then the outcomes were compared in two periods of 11 years.</p><p><strong>Results: </strong>There were 2618 deaths due to silicosis in Spain. The mean age of death increased significantly by 0.66% annually from 1999 to 2013. The age-adjusted mortality rate decreased by 7.30% per year, falling from 3.00 to 0.65 per 1,000,000 inhabitants. The temporal pattern showed a significant decrease of mortality rate in 31% of the provinces (16 out of 52), while it increased in Pontevedra. Regarding the spatial analysis, 11 clusters were found in both periods, but some variations were observed in terms of their distribution in the Spanish territory, as well as in the affected municipalities.</p><p><strong>Conclusions: </strong>The decrease in mortality due to Silicosis could be related to less exposure to silica dust over the years and an improvement in the survival of those affected. It is thus essential to analyze the role of preventive measures for this occupational disease.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 3","pages":"715-728"},"PeriodicalIF":3.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is resistance to Covid-19 vaccination a \"problem\"? A critical policy inquiry of vaccine mandates for healthcare workers.","authors":"Claudia Chaufan, Natalie Hemsing","doi":"10.3934/publichealth.2024035","DOIUrl":"https://doi.org/10.3934/publichealth.2024035","url":null,"abstract":"<p><p>As the COVID-19 global vaccination campaign was launched in December of 2020, vaccination became mandatory for many healthcare workers (HCWs) worldwide. Large minorities resisted the policy, and the responses of authorities to this resistance led to damaged professional reputations, job losses, and suspension or termination of practice licenses. The joint effect of dismissals, early retirements, career changes, and vaccine injuries disabling some compliant HCWs from adequate performance has exacerbated existing crises within health systems. Nevertheless, leading health authorities have maintained that the benefits of a fully vaccinated healthcare labor force-believed to be protecting health systems, vulnerable patient populations, and even HCWs themselves-achieved through mandates, if necessary, outweigh its potential harms. Informed by critical policy and discourse traditions, we examine the expert literature on vaccine mandates for HCWs. We find that this literature neglects evidence that contradicts official claims about the safety and effectiveness of COVID-19 vaccines, dismisses the science supporting the contextual nature of microbial virulence, miscalculates patient and system-level harms of vaccination policies, and ignores or legitimizes the coercive elements built into their design. We discuss the implications of our findings for the sustainability of health systems, for patient care, and for the well-being of HCWs, and suggest directions for ethical clinical and policy practice.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 3","pages":"688-714"},"PeriodicalIF":3.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminating insulin resistance in middle-aged nondiabetic women using machine learning approaches.","authors":"Zailing Xing, Henian Chen, Amy C Alman","doi":"10.3934/publichealth.2024034","DOIUrl":"10.3934/publichealth.2024034","url":null,"abstract":"<p><strong>Objective: </strong>We employed machine learning algorithms to discriminate insulin resistance (IR) in middle-aged nondiabetic women.</p><p><strong>Methods: </strong>The data was from the National Health and Nutrition Examination Survey (2007-2018). The study subjects were 2084 nondiabetic women aged 45-64. The analysis included 48 predictors. We randomly divided the data into training (n = 1667) and testing (n = 417) datasets. Four machine learning techniques were employed to discriminate IR: extreme gradient boosting (XGBoosting), random forest (RF), gradient boosting machine (GBM), and decision tree (DT). The area under the curve (AUC) of receiver operating characteristic (ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were compared as performance metrics to select the optimal technique.</p><p><strong>Results: </strong>The XGBoosting algorithm achieved a relatively high AUC of 0.93 in the training dataset and 0.86 in the testing dataset to discriminate IR using 48 predictors and was followed by the RF, GBM, and DT models. After selecting the top five predictors to build models, the XGBoost algorithm with the AUC of 0.90 (training dataset) and 0.86 (testing dataset) remained the optimal prediction model. The SHapley Additive exPlanations (SHAP) values revealed the associations between the five predictors and IR, namely BMI (strongly positive impact on IR), fasting glucose (strongly positive), HDL-C (medium negative), triglycerides (medium positive), and glycohemoglobin (medium positive). The threshold values for identifying IR were 29 kg/m², 100 mg/dL, 54.5 mg/dL, 89 mg/dL, and 5.6% for BMI, glucose, HDL-C, triglycerides, and glycohemoglobin, respectively.</p><p><strong>Conclusion: </strong>The XGBoosting algorithm demonstrated superior performance metrics for discriminating IR in middle-aged nondiabetic women, with BMI, glucose, HDL-C, glycohemoglobin, and triglycerides as the top five predictors.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"667-687"},"PeriodicalIF":3.1,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of the literature: Gender-based violence in the construction and natural resources industry.","authors":"Joyce Lo, Sharan Jaswal, Matthew Yeung, Vijay Kumar Chattu, Ali Bani-Fatemi, Aaron Howe, Amin Yazdani, Basem Gohar, Douglas P Gross, Behdin Nowrouzi-Kia","doi":"10.3934/publichealth.2024033","DOIUrl":"10.3934/publichealth.2024033","url":null,"abstract":"<p><p>Gender-based violence (GBV) poses a significant concern in the construction and natural resources industries, where women, due to lower social status and integration, are at heightened risk. This systematic review aimed to identify the prevalence and experience of GBV in the construction and natural resources industries. A systematic search across databases including PubMed, OVID, Scopus, Web of Science, and CINAHL was conducted. The <i>Risk of Bias Instrument for Cross-sectional Surveys of Attitudes and Practices</i> by McMaster University and the <i>Critical Appraisal of Qualitative Studies</i> by the Center for Evidence Based Medicine at the University of Oxford were used to assess the studies included in the review. Six articles were included after full-text analysis. GBV was reported in the construction, mining, urban forestry, and arboriculture sectors. Workplace GBV was measured differently across the studies, and all studies examined more than one form of GBV. The main forms of GBV discussed in these studies were discrimination, sexual harassment, and sexism. The studies provided some insight for demographic factors that may or may not be associated with GBV, such as age, region of work, and number of years working in the industry. The review also suggests that workplace GBV has a negative impact on mental health and well-being outcomes, such as higher levels of stress and lower job satisfaction. The current research has not established the effectiveness of interventions, tools, or policies in these workplaces. Thus, additional research should include intervention studies that aim to minimize or prevent GBV in male-dominated workplaces. The current study can bring awareness and acknowledgement towards GBV in the workplace and highlight the importance of addressing it as this review outlines the negative consequences of GBV on mental health and well-being in these male-dominated industries.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"654-666"},"PeriodicalIF":3.1,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On mathematical modelling of measles disease via collocation approach.","authors":"Shahid Ahmed, Shah Jahan, Kamal Shah, Thabet Abdeljawad","doi":"10.3934/publichealth.2024032","DOIUrl":"10.3934/publichealth.2024032","url":null,"abstract":"<p><p>Measles, a highly contagious viral disease, spreads primarily through respiratory droplets and can result in severe complications, often proving fatal, especially in children. In this article, we propose an algorithm to solve a system of fractional nonlinear equations that model the measles disease. We employ a fractional approach by using the Caputo operator and validate the model's by applying the Schauder and Banach fixed-point theory. The fractional derivatives, which constitute an essential part of the model can be treated precisely by using the Broyden and Haar wavelet collocation methods (HWCM). Furthermore, we evaluate the system's stability by implementing the Ulam-Hyers approach. The model takes into account multiple factors that influence virus transmission, and the HWCM offers an effective and precise solution for understanding insights into transmission dynamics through the use of fractional derivatives. We present the graphical results, which offer a comprehensive and invaluable perspective on how various parameters and fractional orders influence the behaviours of these compartments within the model. The study emphasizes the importance of modern techniques in understanding measles outbreaks, suggesting the methodology's applicability to various mathematical models. Simulations conducted by using MATLAB R2022a software demonstrate practical implementation, with the potential for extension to higher degrees with minor modifications. The simulation's findings clearly show the efficiency of the proposed approach and its application to further extend the field of mathematical modelling for infectious illnesses.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"628-653"},"PeriodicalIF":3.1,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of workplace bullying on job burnout and turnover intention among nursing staff in Greece: Evidence after the COVID-19 pandemic.","authors":"Petros Galanis, Ioannis Moisoglou, Aglaia Katsiroumpa, Panayota Sourtzi","doi":"10.3934/publichealth.2024031","DOIUrl":"10.3934/publichealth.2024031","url":null,"abstract":"<p><strong>Introduction: </strong>The prevalence of workplace bullying, job burnout, and turnover intention among nursing staff increased during the COVID-19 pandemic. However, to the best of our knowledge, there are no studies that have measured the relationships among variables of interest after the pandemic.</p><p><strong>Objective: </strong>Our intention is to investigate the effect of workplace bullying on job burnout and turnover intention in nursing staff.</p><p><strong>Methods: </strong>We conducted a cross-sectional study during January-February 2024 in Greece. We obtained a convenience sample of 450 nurses. We used the 22-item Negative Acts Questionnaire-Revised to assess workplace bullying. We measured job burnout with the single-item burnout measure. We measured nurses' turnover intention with a valid 6-point Likert scale.</p><p><strong>Results: </strong>The study sample included 450 nurses with the mean age of 39.1 years (standard deviation [<i>SD</i>] = 10.2). The mean workplace bullying score was 7.7 (<i>SD</i> = 2.0), while the mean job burnout score was 7.7 (<i>SD</i> = 2.0). Among our nurses, 57.3% showed a high level of turnover intention. After eliminating confounders, we found that increased workplace bullying (adjusted beta = 0.031, 95% confidence interval [<i>CI</i>] = 0.023 to 0.039, <i>p</i> < 0.001) was associated with increased job burnout. Moreover, multivariable logistic regression analysis showed that increased turnover intention was more common among nurses who experienced higher levels of workplace bullying (adjusted odds ratio = 1.057, 95% <i>CI</i> = 1.043 to 1.071, <i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>We found a positive relationship between workplace bullying, job burnout, and turnover intention. Nurse managers, organizations, and policy-makers ought to consider such findings to intervene and decrease workplace bullying by improving working conditions.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"614-627"},"PeriodicalIF":3.1,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotional intelligence protects nurses against quiet quitting, turnover intention, and job burnout.","authors":"Petros Galanis, Aglaia Katsiroumpa, Ioannis Moisoglou, Maria Kalogeropoulou, Parisis Gallos, Irene Vraka","doi":"10.3934/publichealth.2024030","DOIUrl":"10.3934/publichealth.2024030","url":null,"abstract":"<p><strong>Background: </strong>Emotional intelligence can improve nurses' interpersonal and coping skills, job performance, and resilience. However, there is a dearth in the literature on whether emotional intelligence affects levels of quiet quitting, turnover intention, and job burnout in nurses.</p><p><strong>Objective: </strong>We examined the relationship between emotional intelligence, quiet quitting, turnover intention, and job burnout.</p><p><strong>Methods: </strong>We conducted a cross-sectional study in Greece with a convenience sample of 992 nurses. We used the following valid tools to measure our study variables: the Trait Emotional Intelligence Questionnaire-Short Form, the Quiet Quitting Scale, and the single item burnout measure.</p><p><strong>Results: </strong>The mean age of our nurses was 42.2 years. After controlling for gender, age, work experience, shift work, and understaffed department, the multivariable linear regression models indicated significant negative relationships between emotional intelligence and quiet quitting, turnover intention, and job burnout. Specifically, self-control reduced detachment, lack of motivation, job burnout, and turnover intention. Moreover, emotionality reduced detachment, lack of motivation, and lack of initiative. Sociability reduced lack of initiative and lack of motivation, while well-being reduced lack of motivation, job burnout, and turnover intention.</p><p><strong>Conclusion: </strong>Emotional intelligence reduced quiet quitting, turnover intention, and job burnout in nurses. Therefore, nurse managers and policy-makers should apply interventions to optimize the emotional intelligence profiles of nurses.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"601-613"},"PeriodicalIF":3.1,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review.","authors":"Giuseppe Palomba, Agostino Fernicola, Marcello Della Corte, Marianna Capuano, Giovanni Domenico De Palma, Giovanni Aprea","doi":"10.3934/publichealth.2024028","DOIUrl":"10.3934/publichealth.2024028","url":null,"abstract":"<p><p>Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"557-576"},"PeriodicalIF":3.1,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of exercise, physical activity, and sports on physical fitness in adults with Down syndrome: A systematic review.","authors":"Felipe Montalva-Valenzuela, Antonio Castillo-Paredes, Claudio Farias-Valenzuela, Oscar Andrades-Ramirez, Yeny Concha-Cisternas, Eduardo Guzmán-Muñoz","doi":"10.3934/publichealth.2024029","DOIUrl":"10.3934/publichealth.2024029","url":null,"abstract":"<p><p>This systematic review aimed to analyze the effects of exercise, physical activity, and sports on physical fitness in adults with Down syndrome (DS). A literature search was conducted across four databases EBSCO, Scopus, Web of Science, and PubMed. The PRISMA guidelines were followed. The PEDro scale and the Cochrane risk of bias tool were used to assess the quality and risk of the studies, respectively. The protocol was registered in PROSPERO (code: CRD42023449627). Of the 423 records initially found, 13 were finally included in the systematic review, in which 349 adults with DS participated. 92% of the articles declared at least one significant difference post-intervention. The available evidence indicates that exercise, physical activity, and sports have a positive effect on some variables of physical fitness, especially strength, balance, body composition, cardiorespiratory fitness, flexibility, and functional capacity. Furthermore, it should be considered as an additional treatment or complementary therapy to improve the functionality and quality of life of adults with DS.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"577-600"},"PeriodicalIF":3.1,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of burnout levels among healthcare staff in anesthesiology departments in Greece - Is there a connection with anxiety and depression?","authors":"Aikaterini Toska, Sofia Ralli, Evangelos C Fradelos, Ioanna Dimitriadou, Anastasios Christakis, Viktor Vus, Maria Saridi","doi":"10.3934/publichealth.2024027","DOIUrl":"10.3934/publichealth.2024027","url":null,"abstract":"<p><strong>Introduction: </strong>Healthcare workers in anesthesiology departments often experience burnout syndrome, which may be combined with anxiety and depression.</p><p><strong>Aim: </strong>The study aimed to assess the levels of burnout among nurses and physicians working in anesthesiology departments in public hospitals in Attica and to investigate a possible correlation between burnout, anxiety, and depression.</p><p><strong>Methodology: </strong>A cross-sectional study was conducted on physicians and nurses working in anesthesiology departments in public hospitals in Attica, Greece. A questionnaire was distributed electronically using the snowball sampling method, including questions about demographic characteristics, burnout, anxiety, and depression.</p><p><strong>Results: </strong>Physicians and nurses in anesthesiology departments were found to have moderate levels of burnout, and normal/low levels of anxiety and depression. More specifically, it was found that 2% of physicians and 14.4% of nurses had extremely elevated levels of burnout. On the other hand, 6.1% of physicians and 23.7% of nurses had high anxiety, while 6.1% of physicians and 15.5% of nurses had elevated levels of depression. Females (<i>p</i> = 0.008), staff aged 45-55 (<i>p</i> = 0.021), lower educational level (<i>p</i> = 0.025), nurses (<i>p</i> = 0.001), more than 21 years of service (<i>p</i> = 0.001), and having children (<i>p</i> = 0.008) were determinants of greater levels of personal burnout. Work-related burnout correlated with having children (<i>p</i> = 0.017), whereas client-related burnout was significantly higher for nurses (<i>p</i> = 0.002). In addition, a correlation was found between anxiety, depression, and increased levels of burnout (<i>p</i> = 0.000).</p><p><strong>Conclusions: </strong>As physicians and nurses working in anesthesiology departments have stressful jobs and work long hours, it is important to further study their physical, emotional, and mental exhaustion as well as psychological resilience levels.</p>","PeriodicalId":45684,"journal":{"name":"AIMS Public Health","volume":"11 2","pages":"543-556"},"PeriodicalIF":3.1,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}