Predictors of misperceptions, risk perceptions, and personal risk perceptions about COVID-19 by country, education and income
Bibliographic record
Abstract
Government interventions, such as mandating the use of masks and social distancing, play crucial roles in controlling the spread of pandemic infection. Adherence depends on public perceptions about pandemic risk. The goal was to explore the roles of education, income, and country on misperceptions, risk perceptions and personal risk perceptions about COVID-19. Data were extracted from 3 preregistered surveys. Binary logistic regressions were conducted to investigate the roles country, education, and income had on outcome variables. Across the USA, Canada, and UK, individuals in the highest income quartile were significantly less likely to hold misperceptions (OR=0.61, 95% CI 0.45 to 0.83) and to perceive personal risk (OR=0.38, 95% CI 0.20 to 0.75) regarding COVID-19 compared with individuals in the lowest income quartile. When comparing these income quartiles in the USA, the difference in perceived risk was heightened (OR=0.21, 95% CI 0.07 to 0.57). Citizens of the UK were more likely to have risk perceptions compared with citizens of the USA (OR=1.50, 95% CI 1.20 to 1.88). Citizens of Canada were less likely to perceive personal risk compared with US citizens (OR=0.40, 95% CI 0.23 to 0.69). Proper risk perception and understanding of COVID-19 are necessary for adherence to public health initiatives. The lowest income quartile was shown to have more misperceptions and personal risk perceptions across all 3 countries, highlighting the disproportionate impact of COVID-19 in this group. Our findings support the importance of education and income in affecting health perceptions and outcomes. Further research is needed to explore interventions to minimize misperceptions, accurately shape risk perception, and effectively communicate science.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".