Big-5 Personality Traits and their Dynamic and Conditional Effects on COVID-19 Attitudes and Behaviours
Bibliographic record
Abstract
Much has been learned about who tends to follow public health advice during the COVID-19 pandemic. But there remains important questions about how personality can shape risk perceptions, willingness to engage in protective behaviours, and policy preferences. We use a survey of over 40,000 Canadian adults, fielded between November 2020 and July 2021, to evaluate the associations between Big-5 personality traits and COVID-19 beliefs and behaviours, the stability of these associations as pandemic conditions changed, and the how these traits moderate the effects of left-right ideology and expert trust on these attitudes and behaviours. We find moderate associations between risk perceptions and negative emotionality and agreeableness, and, as well as between each of the Big-5 traits and our respondents’ willingness to take protective behaviours and support government restrictions. These associations are mostly stable over time for protective behaviour, with instability particularly pronounced for lockdown support where agreeableness and conscientiousness diminishing in importance as pandemic conditions improved. We also show that negative emotionality, conscientiousness, and agreeableness reduce differences between the political left and right and between those who do and do not trust experts, while extraversion intensifies these divides. Our work calls for greater attention to how the correlates of COVID-19 attitudes and behaviours may change with an evolving pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".