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

Partner Support

The term partner support traditionally refers to the process of responding with helping acts (behavioral as well as psychological) to a difficulty or problem of one\u2019s partner in a couple relationship. More recently, this definition was extended as to include responses apt to sustain the partner when facing positive events and life opportunities as well. Historically, from the interest for general social support, researchers have recognized the relevant role of the specific relationships on the effects produced by social support. Couple relationship scholars, in particular, have drawn attention on the fact that intimate partners are especially important sources of support, who cannot be easily substituted. Indeed, social epidemiology has often used marital status as a measure of social support availability

Social Exclusion and Sexual Objectification Among 18- to 30-Year-Old Men in Kosovo

It was predicted that higher levels of gender-based rejection sensitivity would be related to higher tendencies to objectify women (that is, higher tendencies to perceive women as lacking in human mental states and uniquely human emotions). It was also predicted that an enhanced tendency to perceive women as objects would increase men's tendencies to engage with myth rape acceptance. In a study involving 94 Kosovo men, however, the rejection sensitivity index did not correlate with any outcome variable. The tendency to objectify women did not correlate with myth rape acceptance. Hurt proneness or anxiety in close relationships was positively correlated with the tendency to perceive women as human beings (rather than as objects) and to attribute them human emotions or human mental states. These latter correlations clearly emerged among male participants currently involved in romantic relationships but not in those not involved in romantic relationships