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919), suggesting resources waste. Financial performance is, in general, higher than quality, raising social concerns about the way that public hospitals have been managed. Findings bring relevant implications. For example, the way hospitals are currently financed should consider efficiency, productivity, quality, and access. Regulators should ensure that minimum performance levels are fulfilled, applying preventive and corrective measures to avoid future low-performance levels. We suggest that hospital managers introduce satisfaction inquiries to improve quality. These improvements can attract more patients in the medium- or long-term; thus, our results are useful to citizens to make a better choice.Leveraging the COVID-19 India-wide lockdown situation, the present study attempts to quantify the reduction in the ambient fine particulate matter concentrations during the lockdown (compared with that of the pre-lockdown period), owing to the highly reduced specific anthropogenic activities and thereby pollutant emissions. The study was conducted over Bengaluru (India), using PM2.5 (mass concentration of particulate matter having size less than or equal to 2.5 µm) and Black Carbon mass concentration (BC) data. Open-access datasets from pollution control board (PCB) were also utilised to understand the spatial variability and region-specific reduction in PM2.5 across the city. The highest percentage reduction was observed in BCff (black carbon attributable to fossil fuel combustion), followed by total BC and PM2.5. No decrease in BCbb (black carbon attributable to wood/biomass burning) was observed, suggesting unaltered wood-based cooking activities and biomass-burning (local/regional) throughout the study period. Results support the general understanding of multi-source (natural and anthropogenic) nature of PM2.5 in contrast to limited-source (combustion based) nature of BC. The diurnal amplitudes in BC and BCff were reduced, while they remained almost the same for PM2.5 and BCbb. Analysis of PCB data reveal the highest reduction in PM2.5 in an industrial cluster area. Selleck A-1210477 The current lockdown situation acted as a natural model to understand the role of a few major anthropogenic activities (viz., traffic, construction, industries related to non-essential goods, etc.) in enhancing the background fine particulate matter levels. Contemporary studies reporting reduction in surface fine particulate matter and satellite retrieved columnar Aerosol Optical Depth (AOD) during COVID-19 lockdown period are discussed.The present study examined the mediating role of intolerance of uncertainty and fear of COVID-19 in the relationship between self-compassion and well-being. The participants were comprised of 667 Turkish individuals (465 females and 202 males; aged between 18 and 73 years) from 75 of 81 cities in Turkey. The model was investigated using bootstrapping. The results showed that self-compassion, intolerance of uncertainty, fear of COVID-19, and well-being are significantly interrelated. Moreover, a serial mediation was found among the variables individuals with a growth self-compassion to report lower intolerance of uncertainty, which further decreased perceived fear of COVID-19, and subsequently weakened well-being. Results are discussed in the context of COVID-19 and the well-being literature, and theoretical and practical implications were also provided.The present study examined the multiple micro- and macro-level factors that affect individuals' financial behaviour under economic strain. The following sociodemographic and economic factors that predict financial behaviour were analysed age group, year of data gathering, and attitudes towards consumption (economical, deprived, and hedonistic). Subjective financial situations and demographic characteristics were controlled for. Finnish time series data that consisted of five cross-sectional nationally representative surveys were used (n = 10 043). The analyses revealed four types of financial behaviour cutting expenses, borrowing, increasing income, and gambling. Young adults aged 18-25 reported the lowest frequency of borrowing and gambling and the highest frequency of increasing income (together with young adults aged 26-35). Participants aged 66-75 scored the lowest in cutting expenses and increasing income in comparison to all other age groups. Financial behaviour under economic strain in 2019 can be characterized by lower instances of borrowing than in 2004 and 2009 and higher frequencies in increasing income in comparison to all other years of data gathering. Finally, strong attitudes towards saving were related to lower frequency of borrowing and gambling, whereas stronger hedonistic attitudes were related to lower frequency of cutting expenses and more frequent borrowing. The research results provide tools for consumer policy, consumer education, and consumer regulation.Generalized k -means can be combined with any similarity or dissimilarity measure for clustering. Using the well known likelihood ratio or F -statistic as the dissimilarity measure, a generalized k -means method is proposed to group generalized linear models (GLMs) for exponential family distributions. Given the number of clusters k , the proposed method is established by the uniform most powerful unbiased (UMPU) test statistic for the comparison between GLMs. If k is unknown, then the proposed method can be combined with generalized liformation criterion (GIC) to automatically select the best k for clustering. Both AIC and BIC are investigated as special cases of GIC. Theoretical and simulation results show that the number of clusters can be correctly identified by BIC but not AIC. The proposed method is applied to the state-level daily COVID-19 data in the United States, and it identifies 6 clusters. A further study shows that the models between clusters are significantly different from each other, which confirms the result with 6 clusters.What factors explain spatial variation in the severity of COVID-19 across the United States? To answer this question, we analyze the correlates of COVID-19 cases and deaths across US counties. We document four sets of facts. First, effective density is an important and persistent determinant of COVID-19 severity. Second, counties with more nursing home residents, lower income, higher poverty rates, and a greater presence of African Americans and Hispanics are disproportionately impacted, and these effects show no sign of disappearing over time. Third, the effect of certain characteristics, such as the distance to major international airports and the share of elderly individuals, dies out over time. Fourth, Trump-leaning counties are less severely affected early on, but later suffer from a large severity penalty.