A 32‐society investigation of the influence of perceived economic inequality on social class stereotyping

There is a growing body of work suggesting that social class stereotypes are amplified when people perceive higher levels of economic inequality—that is, the wealthy are perceived as more competent and assertive and the poor as more incompetent and unassertive. The present study tested this prediction in 32 societies and also examines the role of wealth-based categorization in explaining this relationship. We found that people who perceived higher economic inequality were indeed more likely to consider wealth as a meaningful basis for categorization. Unexpectedly, however, higher levels of perceived inequality were associated with perceiving the wealthy as less competent and assertive and the poor as more competent and assertive. Unpacking this further, exploratory analyses showed that the observed tendency to stereotype the wealthy negatively only emerged in societies with lower social mobility and democracy and higher corruption. This points to the importance of understanding how socio-structural features that co-occur with economic inequality may shape perceptions of the wealthy and the poor.info:eu-repo/semantics/publishedVersio

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

COVID-19 stressors and health behaviors. A multilevel longitudinal study across 86 countries

Anxiety associated with the COVID-19 pandemic and home confinement has been associated with adverse health behaviors, such as unhealthy eating, smoking, and drinking. However, most studies have been limited by regional sampling, which precludes the examination of behavioral consequences associated with the pandemic at a global level. Further, few studies operationalized pandemic-related stressors to enable the investigation of the impact of different types of stressors on health outcomes. This study examined the association between perceived risk of COVID-19 infection and economic burden of COVID-19 with health-promoting and health-damaging behaviors using data from the PsyCorona Study: an international, longitudinal online study of psychological and behavioral correlates of COVID-19. Analyses utilized data from 7,402 participants from 86 countries across three waves of assessment between May 16 and June 13, 2020. Participants completed self-report measures of COVID-19 infection risk, COVID-19-related economic burden, physical exercise, diet quality, cigarette smoking, sleep quality, and binge drinking. Multilevel structural equation modeling analyses showed that across three time points, perceived economic burden was associated with reduced diet quality and sleep quality, as well as increased smoking. Diet quality and sleep quality were lowest among respondents who perceived high COVID-19 infection risk combined with high economic burden. Neither binge drinking nor exercise were associated with perceived COVID-19 infection risk, economic burden, or their interaction. Findings point to the value of developing interventions to address COVID-related stressors, which have an impact on health behaviors that, in turn, may 111 influence vulnerability to COVID-19 and other health outcomes

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Paralog Studies Augment Gene Discovery: DDX and DHX Genes

Members of a paralogous gene family in which variation in one gene is known to cause disease are eight times more likely to also be associated with human disease. Recent studies have elucidated DHX30 and DDX3X as genes for which pathogenic variant alleles are involved in neurodevelopmental disorders. We hypothesized that variants in paralogous genes encoding members of the DExD/H-box RNA helicase superfamily might also underlie developmental delay and/or intellectual disability (DD and/or ID) disease phenotypes. Here we describe 15 unrelated individuals who have DD and/or ID, central nervous system (CNS) dysfunction, vertebral anomalies, and dysmorphic features and were found to have probably damaging variants in DExD/H-box RNA helicase genes. In addition, these individuals exhibit a variety of other tissue and organ system involvement including ocular, outer ear, hearing, cardiac, and kidney tissues. Five individuals with homozygous (one), compound-heterozygous (two), or de novo (two) missense variants in DHX37 were identified by exome sequencing. We identified ten total individuals with missense variants in three other DDX/DHX paralogs: DHX16 (four individuals), DDX54 (three individuals), and DHX34 (three individuals). Most identified variants are rare, predicted to be damaging, and occur at conserved amino acid residues. Taken together, these 15 individuals implicate the DExD/H-box helicases in both dominantly and recessively inherited neurodevelopmental phenotypes and highlight the potential for more than one disease mechanism underlying these disorders.status: publishe

Lockdown Lives: A Longitudinal Study of Inter-Relationships Among Feelings of Loneliness, Social Contacts, and Solidarity During the COVID-19 Lockdown in Early 2020

We examine how social contacts and feelings of solidarity shape experiences of loneliness during the COVID-19 lockdown in early 2020. From the PsyCorona database, we obtained longitudinal data from 23 countries, collected between March and May 2020. The results demonstrated that although online contacts help to reduce feelings of loneliness, people who feel more lonely are less likely to use that strategy. Solidarity played only a small role in shaping feelings of loneliness during lockdown. Thus, it seems we must look beyond the current focus on online contact and solidarity to help people address feelings of loneliness during lockdown. Finally, online contacts did not function as a substitute for face-to-face contacts outside the home—in fact, more frequent online contact in earlier weeks predicted more frequent face-to-face contacts in later weeks. As such, this work provides relevant insights into how individuals manage the impact of restrictions on their social lives. © 2021 by the Society for Personality and Social Psychology, Inc

Lockdown Lives: A Longitudinal Study of Inter-Relationships Among Feelings of Loneliness, Social Contacts, and Solidarity During the COVID-19 Lockdown in Early 2020

We examine how social contacts and feelings of solidarity shape experiences of loneliness during the COVID-19 lockdown in early 2020. From the PsyCorona database, we obtained longitudinal data from 23 countries, collected between March and May 2020. The results demonstrated that although online contacts help to reduce feelings of loneliness, people who feel more lonely are less likely to use that strategy. Solidarity played only a small role in shaping feelings of loneliness during lockdown. Thus, it seems we must look beyond the current focus on online contact and solidarity to help people address feelings of loneliness during lockdown. Finally, online contacts did not function as a substitute for face-to-face contacts outside the home—in fact, more frequent online contact in earlier weeks predicted more frequent face-to-face contacts in later weeks. As such, this work provides relevant insights into how individuals manage the impact of restrictions on their social lives

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant. © 2022 The Author(s