Implicit and explicit COVID-19-vaccine harmfulness/helpfulness associations predict vaccine beliefs,intentions, and behaviors

We investigated the role of implicit and explicit associations between harm and COVID-19 vaccines using a large sample (N = 4668) of online volunteers. The participants completed a brief implicit association test and explicit measures to evaluate the extent to which they associated COVID-19 vaccines with concepts of harmfulness or helpfulness. We examined the relationship between these harmfulness/helpfulness COVID-19 vaccine associations and vaccination status, intentions, beliefs, and behavior. We found that stronger implicit and explicit associations that COVID-19 vaccines are helpful relate to vaccination status and beliefs about the COVID-19 vaccine. That is, stronger pro-helpful COVID-19 vaccine associations, both implicitly and explicitly, related to greater intentions to be vaccinated, more positive beliefs about the vaccine, and greater vaccine uptake.     

Stop the blame game: An analysis of blaming on Weibo during the early days of the COVID-19 pandemic in rice and wheat areas in China

In the pandemic era, social media has provided the public with a platform to make their voice heard. One of the most important public opinions online during a pandemic is blame. Blame can lead to stigma towards patients as well as potential patients and decrease social cooperation, which might impede prevention and control measures during epidemics. Thus, studying online blame during the early days of COVID-19 can facilitate the management and control of future pandemics. By analyzing 3791 posts from one of the most popular social media sites in China (Weibo) over the 10 days immediately after COVID-19 was declared to be a communicable disease, we found that there were four main agents blamed online: Individuals, corporations, institutions, and the media. Most of the blame targeted individual agents. We also found that there were regional-cultural differences in the detailed types of blamed individual agents, that is, between rice- and wheat-farming areas in China. After controlling influence of distance from the epicenter of Wuhan, there were still stable differences between regions: people in wheat areas had a higher probability of blaming agentic, harmful individuals, and people in rice areas had a higher probability of blaming individuals with low awareness of social norms for preventive health behavior. Findings have implications for preventing and predicting blame across cultures in future pandemics.     

Coping with COVID: Use,factor structure,and health behavior implications of specific emotion regulation strategies during the coronavirus pandemic

This series of studies examined U.S. individuals' use of specific emotion regulation/coping strategies during the early months of the COVID-19 pandemic, investigated the factor structure among strategies during this universally experienced stressor, and the extent to which these factors predicted engagement in COVID-related health-promoting behaviors. In Study 1, participants (N = 520) rated their use of 17 strategies for coping with pandemic-related stress during the past 24 h. Differences emerged in strategy use across demographic groups (age, race, income). Results of exploratory factor analysis suggest a factor structure grouping strategies in terms of goals beyond emotion regulation per se, rather than phases of the emotion process or a binary adaptive versus maladaptive distinction. In Study 2 (N = 264), participants reported daily on their coping strategy use and weekly on their engagement in COVID-specific health behaviors for 22 days. Results of confirmatory factor analysis replicate the factor structure found in Study 1. Some significant associations of coping strategy use with health-promoting behaviors were observed, but these were sporadic and largely involved baseline measures rather than predicting change over time. Theoretical and practical implications are discussed.     

The relationship between numerical mapping abilities,maths achievement and socioeconomic status in 4- and 5-year-old children

Background

Early numeracy skills are associated with academic and life-long outcomes. Children from low-income backgrounds typically have poorer maths outcomes, and their learning can already be disadvantaged before they begin formal schooling. Understanding the relationship between the skills that support the acquisition of early maths skills could scaffold maths learning and improve life chances.

Aims

The present study aimed to examine how the ability of children from different SES backgrounds to map between symbolic (Arabic numerals) and non-symbolic (dot arrays) at two difficulty ratios related to their math performance.

Sample

Participants were 398 children in their first year of formal schooling (Mean age = 60 months), and 75% were from low SES backgrounds.

Method

The children completed symbolic to non-symbolic and non-symbolic to symbolic mapping tasks at two difficulty ratios (1:2; 2:3) plus standardized maths tasks.

Results

The results showed that all the children performed better for symbolic to non-symbolic mapping and when the ratio was 1:2. Mapping task performance was significantly related to maths task achievement, but low-SES children showed significantly lower performance on all tasks.

Conclusion

The results suggest that mapping tasks could be a useful way to identify children at risk of low maths attainment.     

Factor analysis by generalized least squares

Aitken's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are seale free and asymptotically efficient. The estimates are computed by a rapidly converging Newton-Raphson procedure. A new technique is used to deal with Heywood cases effectively.The work on this project was done when the first author was Research Statistician at Educational Testing Service, Princeton, N. J. The second author was in part supported by a grant from the Research Committee of the University of Wisconsin Graduate School. The authors wish to thank Michael Browne for many helpful comments and Marielle van Thillo for valuable assistance in the numerical computations.