Early Indicators of COVID-19 Infection Prevention Behaviors:Machine Learning Identifies Personal and Country-Level Factors

The Coronavirus is highly infectious and potentially deadly. In the absence of a cure or a vaccine, the infection prevention behaviors recommended by the World Health Organization constitute the only measure that is presently available to combat the pandemic. The unprecedented impact of this pandemic calls for swift identification of factors most important for predicting infection prevention behavior. In this paper, we used a machine learning approach to assess the relative importance of potential indicators of personal infection prevention behavior in a global psychological survey we conducted between March-May 2020 (N = 56,072 across 28 countries). The survey data were enriched with society-level variables relevant to the pandemic. Results indicated that the two most important indicators of self-reported infection prevention behavior were individual-level injunctive norms—beliefs that people in the community should engage in social distancing and self-isolation, followed by endorsement of restrictive containment measures (e.g., mandatory vaccination). Society-level factors (e.g., national healthcare infrastructure, confirmed infections) also emerged as important indicators. Social attitudes and norms were more important than personal factors considered most important by theories of health behavior. The model accounted for 52% of the variance in infection prevention behavior in a separate test sample—above the performance of psychological models of health behavior. These results suggest that individuals are intuitively aware that this pandemic constitutes a social dilemma situation, where their own infection risk is partly dependent on the behaviors of others. If everybody engaged in infection prevention behavior, the virus could be defeated even without a vaccine

‘We are all in the same boat’ : how societal discontent affects intention to help during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic has caused a global health crisis. Consequently, many countries have adopted restrictive measures that caused a substantial change in society. Within this framework, it is reasonable to suppose that a sentiment of societal discontent, defined as generalized concern about the precarious state of society, has arisen. Literature shows that collectively experienced situations can motivate people to help each other. Since societal discontent is conceptualized as a collective phenomenon, we argue that it could influence intention to help others, particularly those who suffer from coronavirus. Thus, in the present study, we aimed (a) to explore the relationship between societal discontent and intention to help at the individual level and (b) to investigate a possible moderating effect of societal discontent at the country level on this relationship. To fulfil our purposes, we used data collected in 42 countries (N = 61,734) from the PsyCorona Survey, a cross-national longitudinal study. Results of multilevel analysis showed that, when societal discontent is experienced by the entire community, individuals dissatisfied with society are more prone to help others. Testing the model with longitudinal data (N = 3,817) confirmed our results. Implications for those findings are discussed in relation to crisis management. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement

Is freedom contagious?:On reactance motivation and sensitivity to deviant peers

Psychological reactance is typically assumed to motivate resistance to controlling peer influences and societal prohibitions. However, some peer influences encourage behaviors prohibited by society. We consider whether reactant individuals are sensitive to such opportunities to enhance their autonomy. We specifically propose a self-regulatory perspective on reactance, wherein freedom/autonomy is the superordinate goal, and thus highly reactant individuals will be sensitive to peer influences that could enhance their behavioral freedoms. In 2 studies, we find that reactant individuals can be cooperative in response to autonomy-supportive peer influences. Participants read a scenario in which a peer’s intentions to engage in substance use were manipulated to imply freedom of choice or not. Results indicated that highly reactant participants were sensitive to deviant peers whose own behavior toward alcohol (Study 1, N = 160) or marijuana (Study 2, N = 124) appeared to be motivated by autonomy and thus afforded free choice. Altogether, the results support a self-regulatory model of reactance, wherein deviant peer influence can be a means to pursue autonom

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 psycho-logical 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.Web Information System

PsyCorona: A world of reactions to COVID-19:How an online data visualization tool reports data from an international psychological survey

The purpose of this data visualization tool is twofold. First, it serves as a resource for researchers, analysts, and practitioners to understand people’s thoughts, feelings, and responses to the coronavirus as well as the extraordinary societal measures taken against it. Such knowledge could provide pilot data for researchers, inform current policies to contain the pandemic, or help society prepare for similar events in the future. Second, it serves as a test case for how psychological scientists can use data visualization to engage the public and share results with respondents. Tens of thou-sands of respondents invested time and effort to share their experiences, and the app affords them access and agency over the data as well as an interactive experience of how data can be used

Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: a cross-sectional and longitudinal study

Background The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust in government regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government during the pandemic. Methods This study analysed data from the PsyCorona Survey, an international project on COVID-19 that included 23 733 participants from 23 countries (representative in age and gender distributions by country) at baseline survey and 7785 participants who also completed follow-up surveys. Specification curve analysis was used to examine concurrent associations between trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes. Results Higher trust in government regarding COVID-19 control was significantly associated with higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised β = 0.173 and 0.229, p < 0.001). Government 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, 0.056, and 0.249, p < 0.01). Higher trust at baseline survey was significantly associated with lower rate of decline in health behaviours over time (p for interaction = 0.001). Conclusions These results highlighted the importance of trust in government in the control of COVID-19