Being tolerated and being discriminated against:Links to psychological well-being through threatened social identity needs

We investigated whether and how the experience of being tolerated and of being discriminated against are associated with psychological well‐being in three correlational studies among three stigmatized groups in Turkey (LGBTI group members, people with disabilities, and ethnic Kurds, total N = 862). Perceived threat to social identity needs (esteem, meaning, belonging, efficacy, and continuity) was examined as a mediator in these associations. Structural equation models showed evidence for the detrimental role of both toleration and discrimination experiences on positive and negative psychological well‐being through higher levels of threatened social identity needs. A mini‐meta analysis showed small to moderate effect sizes and toleration was associated with lower positive well‐being through threatened needs among all three stigmatized groups

Trust in government and its associations with health behaviour and prosocial behaviour during the COVID-19 pandemic

Previous studies suggested that public trust in government is vital for implementations of social policies that rely on public's behavioural responses. This study examined associations of trust in government regarding COVID-19 control with recommended health behaviours and prosocial behaviours. Data from an international survey with representative samples (N=23,733) of 23 countries were analysed. Specification curve analysis showed that higher trust in government was significantly associated with higher adoption of health and prosocial behaviours in all reasonable specifications of multilevel linear models (median standardised β=0.173 and 0.244, P<0.001). We further used structural equation modelling to explore potential determinants of trust in government regarding pandemic control. Governments perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated with trust in government (standardised β=0.358, 0.230, 0.055, and 0.250, P<0.01). These results highlighted the importance of trust in government in the control of COVID-19

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

.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 individuallevel 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

Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Before vaccines for 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-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

The Political Dimension of COVID-19 Health-Protective Behavior in the United States

According to health behavior theories, perceived vulnerability to a health threat and perceived effectiveness of recommended health-protective behaviors determine motivation to follow these recommendations. Because the U.S. President Trump and U.S. conservative politicians downplayed the risk and seriousness of contracting COVID-19 and the effectiveness of recommended actions, we predicted that politically conservative Americans would be less likely than liberals to enact recommended health-protective behaviors. We further predicted that these effects would be mediated by perceived health risk, perceived infection severity and perceived action effectiveness. In two studies of U.S. residents, political conservatism was inversely associated with perceived health risk and enactment of health-protective behaviors. Furthermore, perceived risk of infection (both studies), perceived severity of infection (Study 2), and perceived effectiveness of behaviors (Study 2), mediated effects of political orientation on health-protective behaviors. These effects were stronger for participants living in the U.S. (N=10,923) than outside the U.S. (N=51,986)