AIMS Public Health最新文献

Background: 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.

Objective: We examined the relationship between emotional intelligence, quiet quitting, turnover intention, and job burnout.

Methods: 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.

Results: 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.

Conclusion: 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.

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.

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.

Introduction: Healthcare workers in anesthesiology departments often experience burnout syndrome, which may be combined with anxiety and depression.

Aim: 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.

Methodology: 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.

Results: 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 (p = 0.008), staff aged 45-55 (p = 0.021), lower educational level (p = 0.025), nurses (p = 0.001), more than 21 years of service (p = 0.001), and having children (p = 0.008) were determinants of greater levels of personal burnout. Work-related burnout correlated with having children (p = 0.017), whereas client-related burnout was significantly higher for nurses (p = 0.002). In addition, a correlation was found between anxiety, depression, and increased levels of burnout (p = 0.000).

Conclusions: 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.

Background: The unique challenges Black men face within the criminal justice system underscore structural and systemic factors driving widespread inequalities. The long-term effects of these challenges on economic, health, and social outcomes as individuals transition to retirement remain poorly understood, highlighting a critical gap in our knowledge of life trajectories long after justice system involvement.

Objectives: This study investigated the enduring health impacts of incarceration on Black men, particularly focusing on the transition into retirement. It aimed to explore the influence of race and gender on experiences of incarceration before age 50, and how such experiences affected self-rated health during the retirement transition.

Methods: Utilizing data from the Health and Retirement Study, which followed individuals aged 50-59 for up to thirty years, this research examined the interplay of race, gender, incarceration history, and self-rated health during the retirement transition. Logistic regression and path modeling were employed for data analysis.

Results: Logistic regression results indicated that being Black, male, and having lower educational attainment significantly increased the likelihood of experiencing incarceration before the age of 50 (p < 0.05). This suggests that Black men with lower levels of education are at the greatest risk of incarceration. The path model revealed a correlation between incarceration experiences before age 50 and poorer self-rated health at the time of retirement.

Conclusion: The findings highlighted the disproportionately high risk of incarceration among Black men, especially those with lower educational attainment, and its persistent negative impacts on health decades later, including during the transition into retirement. Addressing structural racism and the mass incarceration of Black men is crucial for achieving racial health equity as individuals retire.

Digital intervention has been shown to be helpful in improving perinatal mental health. However, the design characteristics of such interventions have not been systematically reviewed. Considering that a lack of support-especially from a partner-is one of the major contributing factors to perinatal depression and anxiety, it is crucial to determine whether digital interventions have included partner participation. In this review, we systematically examined the design characteristics of digital interventions related to perinatal depression and anxiety and aimed to determine whether partner participation was incorporated as part of the interventions. Based on the PRISMA 2020 guidelines, five databases (PubMed, EBSCO, Cochrane, ProQuest, and Scopus) were searched. Narrative results of design characteristics were developed to provide a framework for the design and evaluation of the studies. A total of 12 intervention studies from China, Sweden, Australia, New Zealand, Singapore, Norway, and the United Kingdom were included. Across all studies, internet cognitive behavioral therapy and mindfulness therapy were overwhelmingly utilized as the major intervention approaches. While all studies reported reduced depressive symptoms after the intervention, only four studies reported subsequent decreased levels of both depressive and anxiety symptoms. Only one study included partner support in the intervention. Cognitive behavioral therapy and mindfulness therapy, two of the most common intervention approaches, were found to be effective in alleviating perinatal depression and anxiety. Partner participation should be prioritized in designing digital interventions to ensure comprehensive and easily accessible social support for persons in need.

The investigation goal here was to analyze how the level of public debt affects preparedness of health systems to face emergencies. In particular, this study examined the negative effects of high public debt on health systems of European countries in the presence of the COVID-19 pandemic crisis. Empirical evidence revealed that European countries with a lower level of government debt as a percentage of GDP both in 2009 and 2019 (the period before the arrival of the pandemic) had lower COVID-19 fatality rates compared to countries with higher levels of public debt. The explanation is that high levels of public debt in countries trigger budget constraints that limit their ability to allocate resources to healthcare systems (e.g., health expenditures and investments), weakening health system performance and causing systemic vulnerability and lower preparedness during emergencies, such as with the COVID-19 pandemic. Implications of health policies are suggested to improve strategies of crisis management.

Background: Due to the worldwide reach of the COVID-19 pandemic, authorities across the globe deemed it essential to enforce exceptional containment measures. Maintaining physical activity (PA) during this time was only feasible through engaging in activities at home. Therefore, this study focused on elucidating the levels of PA and well-being among Somali students in the aftermath of the lockdown measures implemented by governments at the onset of the COVID-19 pandemic.

Methods: This study was conducted in Somalia among undergraduate students studying at Somali International University. A total of 1266 students were included in the present study. An online survey was utilized to measure participant PA behavior. The assessment of PA was conducted in the aftermath of the COVID-19 pandemic, utilizing the Godin Leisure questionnaire. The study showed that 85.8% of the study participants (n = 1086) were between the ages of 17 and 22. More than half of the participants (58.7%, n = 743) were female and had no other employment (57.3%, n = 743).

Results: Jogging was the most frequently reported PA (57.3%, n = 726), and PA level was on average 59.7 minutes per day (SD = 25.9). Also, most of the study participants were in their last year (82.1%). In the regression analysis, age, gender, academic year, and work status were significant predictors of being physically active after the COVID-19 pandemic.

Conclusion: Factors affecting PA after the COVID-19 pandemic include age, gender, academic year, and work status. Males, younger individuals, and those who engage in outdoor exercise are more likely to be physically active. Once the COVID-19 restrictions were relaxed, undergraduate students in Somalia were physically active. A high level of PA appears to be advantageous for public health. Universities in Somalia should uphold school policies that promote an active lifestyle among students, aiming to maintain or enhance the existing level of PA.

Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.

Background: Nursing is pivotal to healthcare delivery but is often associated with high levels of organizational stress. In this cross-sectional study, we aimed to investigate the associations between exposure to organizational stressors, measured using the Health and Safety Executive Management Standards Indicator Tool, and psychosomatic complaints among nurses in a medium-sized city hospital in northeastern Italy.

Methods: A total of 215 nurses participated in the study, completing self-report questionnaires assessing organizational stressors and the prevalence of psychosomatic complaints experienced over the preceding six months.

Results: Significant associations were observed between various organizational stressors and psychosomatic complaints among nurses. Specifically, the Relationships factor emerged as a significant predictor of palpitations, irritability, anxiety, physical and mental tiredness, and headache. Additionally, Demands and Managers' support were identified as significant predictors of specific psychosomatic complaints.

Conclusion: This study highlights the critical role of addressing organizational stressors, particularly those related to interpersonal relationships, in promoting nurse well-being and optimizing patient care delivery. Despite its strengths, including the use of a well-established measurement tool and a comprehensive assessment of psychosomatic complaints, limitations such as the cross-sectional design and self-report measures warrant consideration. By prioritizing supportive work environments and implementing targeted interventions, healthcare organizations can cultivate a culture of well-being among nurses, ultimately enhancing the quality and safety of healthcare delivery.