Tracking variations in daily questionable health behaviors and their psychological roots: a preregistered experience sampling study

People resort to various questionable health practices to preserve or regain health - they intentionally do not adhere to medical recommendations (e.g. self-medicate or modify the prescribed therapies; iNAR), or use traditional/complementary/alternative (TCAM) medicine. As retrospective reports overestimate adherence and suffer from recall and desirability bias, we tracked the variations in daily questionable health behaviors and compared them to their retrospectively reported lifetime use. We also preregistered and explored their relations to a wide set of psychological predictors - distal (personality traits and basic thinking dispositions) and proximal (different unfounded beliefs and biases grouped under the term irrational mindset). A community sample (N = 224) tracked daily engagement in iNAR and TCAM use for 14 days, resulting in 3136 data points. We observed a high rate of questionable health practices over the 14 days; daily engagement rates roughly corresponded to lifetime ones. Both iNAR and TCAM were weakly, but robustly positively related. Independent of the assessment method, an irrational mindset was the most important predictor of TCAM use. For iNAR, however, psychological predictors emerged as relevant only when assessed retrospectively. Our study offers insight into questionable health behaviors from both a within and between-person perspective and highlights the importance of their psychological roots. © 2023, Springer Nature Limited

Lives versus Livelihoods? Perceived economic risk has a stronger association with support for COVID-19 preventive measures than perceived health risk

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive

Disgust sensitivity relates to attitudes toward gay men and lesbian women across 31 nations

Previous work has reported a relation between pathogen-avoidance motivations and prejudice toward various social groups, including gay men and lesbian women. It is currently unknown whether this association is present across cultures, or specific to North America. Analyses of survey data from adult heterosexuals (N = 11,200) from 31 countries showed a small relation between pathogen disgust sensitivity (an individual-difference measure of pathogen-avoidance motivations) and measures of antigay attitudes. Analyses also showed that pathogen disgust sensitivity relates not only to antipathy toward gay men and lesbians, but also to negativity toward other groups, in particular those associated with violations of traditional sexual norms (e.g., prostitutes). These results suggest that the association between pathogen-avoidance motivations and antigay attitudes is relatively stable across cultures and is a manifestation of a more general relation between pathogen-avoidance motivations and prejudice towards groups associated with sexual norm violations

So gross and yet so far away: psychological distance moderates the effect of disgust on moral judgment

People morally evaluate norm violations that occur at various distances from the self (e.g., a corrupt politician vs. a cheating spouse). Yet, distance is rarely studied as a moderator of moral judgment processes. We focus on the influence of disgust on moral judgments, as evidence here has remained inconclusive. Based on feelings as information theory and the notion that disgust evolved as a pathogen avoidance mechanism, we argue that disgust influences moral judgment of psychologically distant (vs. near) norm violations. Studies 1 and 3 show that trait disgust sensitivity (but not trait anger and fear) more strongly predicts moral judgment of distant than near violations. Studies 2 and 4 show that incidental disgust affects moral judgment of distant (vs. near) violations and that the moderating role of distance is mediated by involvement of others (vs. the self) in the evaluator's conceptualization of the violation

Gender gap in parental leave intentions: Evidence from 37 countries

This is the final version. Available from Wiley via the DOI in this record. Despite global commitments and efforts, a gender-based division of paid and unpaid work persists. To identify how psychological factors, national policies, and the broader sociocultural context contribute to this inequality, we assessed parental-leave intentions in young adults (18–30years old) planning to have children (N = 13,942; 8,880 identified as women; 5,062 identified as men) across 37 countries that varied in parental-leave policies and societal gender equality. In all countries, women intended to take longer leave than men. National parental-leave policies and women’s political representation partially explained cross-national variations in the gender gap. Gender gaps in leave intentions were paradoxically larger in countries with more gender-egalitarian parental-leave policies (i.e., longer leave available to both fathers and mothers). Interestingly, this cross-national variation in the gender gap was driven by cross-national variations in women’s (rather than men’s) leave intentions. Financially generous leave and gender-egalitarian policies (linked to men’s higher uptake in prior research) were not associated with leave intentions in men. Rather, men’s leave intentions were related to their individual gender attitudes. Leave intentions were inversely related to career ambitions. The potential for existing policies to foster gender equality in paid and unpaid work is discussed.SSHRC Insight Development GrantSSHRC Insight GrantEconomic and Social Research CouncilState Research AgencyGuangdong 13th-five Philosophy and Social Science Planning ProjectNational Natural Science Foundation of ChinaSwiss National Science FoundationSwiss National Science FoundationCenter for Social Conflict and Cohesion StudiesCenter for Intercultural and Indigenous ResearchSSHRC Postdoctoral FellowshipSlovak Research and Development AgencySwiss National Science FoundationCanada Research ChairsSocial Sciences and Humanities Research Council of CanadaOntario Ministry of Research and InnovationHSE University, RFFaculty of Arts, Masaryk Universit

Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis

Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies

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