AIMS Public Health最新文献

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.

Alzheimer's disease stands as one of the most widespread neurodegenerative conditions associated with aging, giving rise to dementia and posing significant public health challenges. Mathematical models are considered as valuable tools to gain insights into the mechanisms underlying the onset, progression, and potential therapeutic approaches for AD. In this paper, we introduce a mathematical model for AD that employs the fractal fractional operator in the Caputo sense to characterize the temporal dynamics of key cell populations. This model encompasses essential elements, including amyloid-β ($mathbb{ A_beta }$), neurons, astroglia and microglia. Using the fractal fractional operator, we have established the existence and uniqueness of solutions for the model under consideration, employing Leray-Schaefer's theorem and the Banach fixed-point methods. Utilizing functional techniques, we have analyzed the proposed model stability under the Ulam-Hyers condition. The suggested model has been numerically simulated by using a fractional Adams-Bashforth approach, which involves a two-step Lagrange polynomial. For numerical simulations, different ranges of fractional order values and fractal dimensions are considered. This new fractal fractional operator in the form of the Caputo derivative was determined to yield better results than an ordinary integer order. Various outcomes are shown graphically by for different fractal dimensions and arbitrary orders.

Clinical training plays a fundamental role in nursing students' acquisition of professional capabilities. This study aimed to explore the perceived challenges nursing students face during clinical training. An explorative cross-sectional study was applied. A proportionate, stratified, random sample was enrolled in the study with inclusive criteria, including nursing students (2nd - 4th year) and interns who attended their internship in regional hospitals. A validated electronic questionnaire was used for data collection, which consisted of three sections and 29 items. The section that focused on the difficulties experienced by nursing students during their practical training included six elements: teachers, healthcare professionals, the students themselves, tasks, time management, and the location of the training. Another section inquired about the students' perspectives on the benefits of clinical training. A three-point "Likert scale" was applied. The findings indicated that mild (24%), moderate (62%), and severe (14%) degree of challenges were perceived by the study participants. The mean score for the total challenges during clinical training was 2.00 ± 0.28, and there were variations in the perceived challenges among grade levels. In conclusion, there are variations in the perceived challenges during clinical training among different grade levels. These challenges were related to teachers, health workers, the students, tasks, the time, and the place. Enhancing the nursing curricula alignment with practical training objectives is recommended, focusing on the development of technical and interpersonal skills with appropriate guidance, alongside positive clinical settings to help nursing students learn and boost their confidence in their approach.

Background: In this study, we addressed the gap between health research and policymaking in Latin America and the Caribbean (LAC), focusing on health education/health literacy. Despite growing research, translating findings into effective policies needs to be improved. We explored the factors that make research on health education and health literacy to be referenced and mentioned in policy documents in LAC (and in Peru). We proposed a model based on the hypothesis that the relationship between research and policymaking depends on the research strength of scientific evidence, timing, and social media activity.

Methods: A mixed-methods approach was employed, combining quantitative and qualitative data analysis. Quantitative data sources included multidisciplinary databases, altmetric data, and citations of policy documents. For data analysis, we obtained descriptive statistics to identify patterns and then verified the association between variables using χ2. The negative binomial regression was used to test the empirical model introduced above. Quantitative analysis was complemented by analysis of responses to a set of open questions from a sample of Peruvian health policymakers.

Results: We found that timing, strength of evidence, and social media activity were significant predictors of research cited in policy documents. Policy documents tended to rely more on qualitative evidence. A positive correlation between timing and cites in policy documents highlighted the importance of timely dissemination, whereas social media activity, while having an impact, had a relatively minor effect. Peruvian policymakers' responses emphasized the role of political context, the relevance of results, and policymakers' commitment to incorporating research into policies.

Conclusion: Strength of evidence, social media engagement, and publication timing are key predictors of citations for health education/literacy research in LAC policy documents. However, qualitative findings highlight challenges, including some distrust in research findings, together with limited access to relevant research. The findings offer opportunities to enhance evidence-informed health education/health literacy policy decisions.

Implications: To increase the influence on health policymakers, researchers should prioritize the timely dissemination of solid evidence, considering both traditional and digital platforms. Policymakers should focus on the quality and relevance of evidence when formulating policies.

This study explores the use of artificial intelligence (AI) to analyze information from X (previously Twitter) feeds related to COVID-19, specifically focusing on the time following the World Health Organization's (WHO) vaccination announcement. This aspect of the pandemic has not been studied by other researchers focusing on vaccination news. By utilizing advanced AI algorithms, the research aims to examine a wealth of data, sentiments, and trends to enhance crisis management strategies effectively. Our methods involved collecting a dataset of tweets from December 2020 to July 2021. By using specific keywords strategically, we gathered a substantial 15.5 million tweets, focusing on important hashtags like #vaccine and #coronavirus while filtering out irrelevant replies and retweets. The assessment of three different machine learning models-BiLSTM, FFNN, and CNN - highlights the exceptional performance of BiLSTM, achieving an impressive F1-score of 0.84 on the test set, with Precision and Recall metrics at 0.85 and 0.83, respectively. The study provides a detailed visualization of global sentiments on COVID-19 topics, with a main goal of extracting insights to manage public health crises effectively. Sentiment labels were predicted using various classification models and categorized as positive, negative, and neutral for each country after adjusting for population differences. An important finding from the analysis is the variation in sentiments across regions, for instance, with Eastern European countries showing positive views on post-vaccination economic recovery, while China and the United States express negative opinions on the same topic.

The return-to-play process' characteristics can vary by injury and sport type but are typically composed of phases of different durations, training targets, and intensities that gradually increase the physiological and mechanical load. In team sports, contact drills are a necessary part of the last phases of this process, and they should be planned using the optimal mechanical load. The present study investigated the external load and kinetic recovery in U19 soccer players performing 6vs6 and 3vs3 small-sided games. A global positioning system (GPS) measured external load metrics. The rate of perceived exertion (RPE) was registered at the end. Total quality of recovery (TQR) was collected at the beginning of the training session and after 24 h. Moreover, before and after the small-sided games (SSGs) and at 24 h, delayed-onset muscle soreness (DOMS) of the legs, sprinting time, and vertical jump height (CMJ) were collected. 6vs6 presented higher values in total distance low-, moderate-, high, and very-high-speed distance, and maximum speed (p < 0.05). However, 3vs3 showed higher number of sprints, acceleration, and deceleration at different intensities. Furthermore, no difference was shown in RPE. The effect of fatigue on sprint seems greater for 6vs6, showing an impairment persistent at 24 h (p < 0.05). Moreover, CMJ height was impaired after 6vs6 and at 24 h (p < 0.05) but did not change after 3vs3 (p > 0.05). DOMS values after SSGs and at 24 h were higher than baseline for both conditions (p < 0.05), while TQR decreased at 24 h in both conditions (p < 0.05). Based on our results, it seems that 6vs6, leading to a greater high-speed running distance, might cause a training load that needs more time to recover. This point may be crucial in a return-to-play process, especially when hamstring muscles are involved.

Adults with Attention-Deficit/Hyperactivity Disorder (ADHD) often face significant deficits in executive function and adverse work-related outcomes. This study aimed to explore the role of executive function deficits in job burnout of employees with ADHD. We hypothesized that employees with ADHD, relative to employees without ADHD, will experience higher levels of job burnout and deficits in executive function. We also hypothesized that the ADHD-job burnout relationship would be mediated through executive function deficits, specifically by self-management to time and self-organization/problem-solving. A field study with 171 employees provided support for the research hypotheses and mediation model in which the employees' ADHD-job burnout relationship was mediated through executive function deficits. Additional mediation analyses indicated that the specific executive function of self-management to time and self-organization/problem-solving mediated the effect of ADHD on job burnout and its facets. Specifically, for physical fatigue, the mediation was realized through self-management to time, and for emotional exhaustion and cognitive weariness, the mediation was significant through self-organization/problem-solving. The present findings shed light on the relevance of referring ADHD among employees, their vulnerability to job burnout, and the role of executive function deficits in job burnout of employees with ADHD.