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The spread of the contagious COVID-19 virus was quickly followed by an outbreak of explanations and discourses trying to make sense of the crisis. The goal of this paper is to track the changing dynamics of blame attribution and scapegoating in the Canadian population as the COVID-19 pandemic unfolds, with a particular emphasis on the influence of evolving public health measures. The study uses data from a longitudinal survey conducted with a representative sample of 3617 Canadians between April 2020 and May 2021 following a longitudinal design. Latent Dirichlet allocation (LDA), a computational approach to analyze text, was applied to data coming from an open-ended question on who or what should be held responsible for the COVID-19 pandemic. Nine topics were identified, six of which were recurring overtime. Canadians mostly blame distant collectives in the early months of the pandemic, especially China and wet markets. Over time, they increasingly blame local collectives, such as individuals who do not comply with sanitary measures. Blame attribution evolves with the proximity of the threat and the risk of international spread

The COVID-19 pandemic has produced unprecedented changes in the lives of many people. Although research has documented associations between concerns related to COVID-19 and poor mental health indicators, fewer studies have focused on positive factors that could help people better cope with this stressful social context. To fill this gap, the present research investigated the trajectories of self-compassion facets in times of dramatic social change. Using a longitudinal research design, we described the trajectories of self-kindness, common humanity, and mindfulness during the first eight months of the COVID-19 pandemic, in a representative sample of Canadian adults (N = 3617). Relying on a multi-trajectory group-based approach, we identified clusters of individuals following persistently low (4.0%), moderate-low (39.3%), moderate-high (46.7%), and high (10.0%) levels of self-kindness, common humanity, and mindfulness. Interestingly, we found that compassionate self-responding trajectories were mainly stable over time with minor fluctuations for some groups of individuals, in line with the epidemiological situation. In terms of covariates, we observed that older women were more likely to follow trajectories of high compassionate self-responding, as compared to the other age and gender groups. In terms of mental health indicators, we demonstrated that trajectory groups with high levels of compassionate self-responding were associated with greater life satisfaction, more happiness, better sleep quality, higher sleep quantity, and fewer negative emotions, as compared to lower trajectory groups. The results supported the idea that self-compassion during the COVID-19 pandemic could have favored better mental health indicators and could possibly be promoted as a psychological intervention in the general population.

In the context of public health crises such as the COVID-19 pandemic, it is essential that individuals cooperate by complying with preventive measures (e.g., wearing a mask). The current research examines how high trust in close others is linked to less cooperation—that is, less compliance with measures—and thus, undermines collective interests. Specifically, we test whether individuals are less willing to comply with preventive measures when interacting with close others they trust. We conducted two experiments in which participants read a vignette depicting a social interaction with either close others (e.g., family) or strangers. Participants had to report the extent to which they would (1) trust the other people in the situation and (2) comply with the mask wearing and physical distancing measures during this interaction. In both experiments, we find that when individuals are considering an interaction with close others, they report experiencing higher trust which is then linked to lower compliance with preventive measures. In Experiment 2, we further demonstrate that participants report less compliance with preventive measures around close others, even when they perceive non-compliance with the measures as morally “wrong”. Our findings shed light on the challenges that compliance with preventive measures poses during social interactions in a context of high trust

Previous studies have highlighted the importance of promoting health literacy and minimizing misinformation to encourage higher adherence to key public health measures during the COVID-19 pandemic. This study explores how one’s self-reported understanding of information and types of sources used to get information regarding COVID-19 can hinder adherence to public health measures implemented by the Canadian government. Data was collected following a longitudinal design of 11 time points for April 2020 to April 2021. The sub-sample used for this study included 2659 Canadians who completed the survey for at least four time points. Using Latent Class Growth Analysis, we modelled typical trajectories of adherence to three key public health measures: staying home, social distancing and mask wearing. Overall, a lower level of understanding was associated with lower adherence trajectories to public health measures, and vice-versa. Adjusted odds ratio (AOR) showed that the higher the level of understanding, the higher were the chances of following a high adherence trajectory. The type of used sources also showed a significant statistical association with adherence trajectories for social distancing and staying home (AOR: between 1.1 and 3.4). These results are discussed considering future policy implications.

StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper., Comment: Sacha Morin and Robin Legault contributed equally