Dynamic role of personality in explaining COVID-19 vaccine hesitancy and refusal
Bibliographic record
Abstract
Vaccine hesitancy and refusal are threats to sufficient response to the COVID-19 pandemic and public health efforts more broadly. We focus on personal characteristics, specifically personality, to explain what types of people are resistant to COVID-19 vaccination and how the influence of these traits changed as circumstances surrounding the COVID-19 pandemic evolved. We use a large survey of over 40,000 Canadians between November 2020 and July 2021 to examine the relationship between personality and vaccine hesitancy and refusal. We find that all five facets of the Big-5 (openness to experience, conscientiousness, extraversion, agreeableness, and negative emotionality) are associated with COVID-19 vaccine refusal. Three facets (agreeableness, conscientiousness, and openness) tended to decline in importance as the vaccination rate and COVID-19 cases grew. Two facets (extraversion and negative emotionality) maintained or increased in their importance as pandemic circumstances changed. This study highlights the influence of personal characteristics on vaccine hesitancy and refusal and the need for additional study on foundational explanations of these behaviors. It calls for additional research on the dynamics of personal characteristics in explaining vaccine hesitancy and refusal. The influence of personality may not be immutable.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".