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PURPOSE: Although mental disorders are common in the workplace, they often go unnoticed. There is frequently a gap in the recognition and effective management of these disorders, leading to delayed recovery and worsening conditions. This study evaluates the level of Mental Health Literacy (MHL) among employees of the Finance and Planning Departments in the Federal District, Brazil, and proposes preventive measures and de-stigmatization strategies for mental health in the workplace. METHODS: MHL among active employees was assessed using the Mental Health Literacy Scale (MHLS), developed by O'Connor and Casey (2015) and validated for Brazilian Portuguese by Buta et al. (2024). The data analysis included descriptive and inferential statistics, with nonparametric tests used to compare MHL across different groups. RESULTS: The results showed that 27.6% of participants had adequate literacy, 67.9% had problematic levels, and 4.5% had inadequate levels. Women were observed to have higher literacy levels than men. Additionally, significant variations in literacy levels were noted among individuals with different health conditions; those diagnosed with cancer, depression, or other illnesses had higher literacy than those without diagnosed conditions. CONCLUSION: To address MHL challenges, educational actions such as awareness campaigns, training, and consultancy programs are essential.

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Studies have highlighted the importance of applying Behavioral Sciences interventions to develop equity in the prevention of chronic diseases in the public health domain. Our study aims to assess the evidence of this influence. We undertook a systematic review study using the electronic databases PubMed, Web of Science, Scopus and Cochrane, searching for work published between 2013 and 2023. The research analyzed the influence of Behavioral Sciences intervention studies on public health. This review was registered and published in PROSPERO, registration number CRD42023412377. The systematic search identified 2951 articles. The review analyzed 26 studies. The quality assessment of the articles showed an overall average of 74%, with the majority of studies being of high quality. The interventions with the best evidence for chronic diseases used framing messages, nudges and vouchers. Messages with incentives also showed satisfactory evidence. The most prevalent outcomes were related to screening tests and patient adherence to treatment. The current state of decision-making remains mainly at the patient level, with potential for further exploration of the roles of healthcare professionals and decision-makers in future research efforts. Limitations relate to the heterogeneity of the study sample, which hinders a more precise analysis of specific interventions and outcomes in chronic diseases.

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There are several difficulties in evaluating interventions seeking to promote public health policies. In this article, we analyzed the promotion of the use of telemedicine during COVID-19 in Brazil. Using the random promotion method with instrumental variables, we showed that the policy of promoting telemedicine was adequate, with intense use of this type of care. Our results showed that telemedicine works if it is encouraged in the population. We contributed to the discussion of public health policies and their impact on the population's health in times of health crisis, such as during the COVID-19 pandemic.

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In recent years, weight gain and reduced physical activity in the general population have contributed to the development of obesity and other health problems; on the other hand, studies in behavioral sciences have been used to modify behaviors for a healthier life, so the objective of this study was to identify the evidence of interventions in behavioral sciences on adherence to physical activity and weight loss in obese patients. This systematic review study is based on a search of the electronic databases PubMed, Web of Science, Scopus, and Cochrane. Studies assessed the evidence from intervention studies that assessed the influence of intervention studies of behavioral sciences on public health. The articles were published between 2013 and 2023. The systematic search of the databases identified 2951 articles. The review analyzed 10 studies. Behavioral science interventions presented evidence through strategies such as multicomponent interventions, lottery and financial incentives, message framing, message framing with financial incentive and physical activity, and psychological satisfaction, demonstrating results in weight loss and maintenance and increased physical activity. This study presents scientific evidence through healthy behavior change methodologies, and future studies can explore these strategies in conjunction with public health technologies in the search for public-private partnerships to promote physical activity in adults.

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In recent decades, health literacy has garnered increasing attention alongside a variety of public health topics. This study aims to explore trends in this area through a bibliometric analysis. A Random Forest Model was utilized to identify keywords and other metadata that predict average citations in the field. To supplement this machine learning analysis, we have also implemented a bibliometric review of the corpus. Our findings reveal significant positive coefficients for the keywords "COVID-19" and "Male", underscoring the influence of the pandemic and potential gender-related factors in the literature. On the other hand, the keyword "Female" showed a negative coefficient, hinting at possible disparities that warrant further investigation. Additionally, evolving themes such as COVID-19, mental health, and social media were discovered. A significant change was observed in the main publishing journals, while the major contributing authors remained the same. The results hint at the influence of the COVID-19 pandemic and a significant association between gender-related keywords on citation likelihood, as well as changing publication strategies, despite the fact that the main researchers remain those who have been studying health literacy since its creation.

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In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported by Brazilian cities into a regularized VAR model. This model estimates directional COVID-19 transmission channels (connections or links between nodes) of each pair of cities (vertices or nodes) using spectral network analysis. Despite the simple epidemiological model, our predictions align well with the real COVID-19 dynamics across Brazilian municipalities, using data only up until May 2020. Given the rising number of infectious people in Brazil-a possible indicator of a second wave-these early-time approximations could be valuable in gauging the magnitude of the next contagion peak. We further examine the effect of public health policies, including social isolation and mask usage, by creating counterfactual scenarios to quantify the human impact of these public health measures in reducing peak COVID-19 cases. We discover that the effectiveness of social isolation and mask usage varies significantly across cities. We hope our study will support the development of future public health measures.

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We contribute to the literature on financial networks by presenting empirical evidence that the global shock of the COVID-19 pandemic caused changes in the forms and intensity of banking sector connections between different countries. These changes include providing the highest level of connectivity observed in the timeline initiated in 2005. We used a comprehensive set of information containing data from 35 countries (developed and emerging economies) and showed the change in the classification of transmitting and receiving spillover during the COVID-19 crisis. Our results provide relevant insights into systemic integration between countries' banking markets, especially during difficult times. Our results are significant to Central Banks, banking sector investors, and governments seeking assistance from banks in the solutions for the resumption of the economy in the face of the COVID-19 shock.

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This article evaluates the effects of the crisis caused by the new Coronavirus (COVID-19) on the Chinese sectoral indices. Using the complexity-entropy plane methodology, we find that the COVID-19 crisis caused increased inefficiency in most of China's equity sectors. We also find heterogeneous effects depending on the economic sector. Our results are useful for a better understanding the effect of global shocks on the stock markets and how their effects are distributed across economic sectors.

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We use a controlled experiment to analyze the impact of watching different types of educational traffic campaign videos on overconfidence of undergraduate university students in Brazil. The videos have the same underlying traffic educational content but differ in the form of exhibition. We find that videos with shocking content (Australian school) are more effective in reducing drivers' overconfidence, followed by those with punitive content (American school). We do not find empirical evidence that videos with technical content (European school) change overconfidence. Since several works point to a strong association between overconfidence and road safety, our study can support the conduit of driving safety measures by identifying efficient ways of reducing drivers' overconfidence. Finally, this paper also introduces how to use machine learning techniques to mitigate the usual subjectivity in the design of the econometric specification that is commonly faced in many researches in experimental economics.

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We open the black box of the monetary policy transmission mechanism with a granular model that considers the balance-sheet composition and network relationships of each economic agent. Though there are several well-documented channels through which monetary policy operates, we focus on the overlooked trading book channel, which arises because of adjustments in the accounting value of trading book exposures on banks' balance sheets that have to be marked to market when interest rates change. Variations in banks' net worth due these adjustments are used as input to a network model that incorporates the financial and corporate sectors. The framework permits us to determine the effects of interest rate changes on every bank and firm in the economy and any second-round (contagion) effects in the short run. We apply the model to a comprehensive database of Brazilian banks and firms from 2015 to 2020. We find that interest rate shocks affect more strongly financial stability in periods of monetary policy tightening. We also find notable asymmetric effects of positive and negative interest rate shocks in the Brazilian economy, with positive interest rate shocks affecting more financial stability. Finally, our results also suggest a non-linear relationship between interest rate changes and financial stability, reinforcing the need to mitigate monetary policy shocks through interest rate smoothing and adequate communication and transparency to society.

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This paper examines the bank lending channel, which considers how monetary authority actions affect the variation of loans. We focus on the BRICS (Brazil, Russia, India, China and South Africa) totalizing 1254 banks from five countries in the period 2000-2012 (totalizing 13 years). The empirical results show that the effect of money supply growth on the growth of loans is non-linear and inverted U-shaped. In this context, our results show empirical evidence expansionary monetary policies do not increase the propensity of economic agents to systematically take greater risks on the market. After a certain level of money stock, increases in the money supply do not lead to increased negotiated credit.

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This paper evaluates the effect of a change in the quantity of money on relative prices in the U.S. economy based on quarterly time-series for the period of 1959 to 2013. We also estimate the implication of a change in relative prices on the rate of inflation and macroeconomic variables. The empirical results indicate that the change of money supply not only affects relative prices but also affects the inflation rate and real variables, such as investment, natural rate of unemployment and potential GDP, through the change in relative prices. The relevant finding of our study is that money is not neutral in a non-traditional sense because a change in the money supply disturbs relative prices and, consequently, the allocation of resources in the economy. This finding has serious implications that must be considered in the transmission mechanisms of monetary policy.

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This work investigates the scaling properties of fluctuations in the flux of individual agents with respect to their average flux in an interbank network. The analyzed data provide information on daily values of f(i)(asset), the credit provided by bank i in the interbank network, and f(i)(liab), the credit received by bank i from the other banks of the network. The investigation focuses on the scaling properties of the fluctuations in the raw data f(i)(asset), f(i)(liab), and f(R,i)(ext)(t) = f(i)(asset)-f(i)(liab), as well as on similar properties internal and external fluctuations f(i)(int) and f(i)(ext), which are derived according to a recently proposed methodology [M. Argollo de Menezes and A. L. Barabasi, Phys. Rev. Lett. 93, 068701 (2004)]. Finally, a "rolling sampling" approach is introduced in order to deal with the nonstationarity of the fluxes. The results suggest that exponents are time varying, hinting that the considered interbank network is changing with time.