Risk perception and conspiracy theory endorsement predict compliance with <scp>COVID</scp> ‐19 public health measures
Bibliographic record
Abstract
Public health measures such as spatial distancing and physical hygiene have been found effective in mitigating the spread of the coronavirus. However, there is considerable variability in individual compliance with such public health measures and factors contributing to these interindividual differences are currently still understudied. The present study set out to determine the role of risk perception and conspiracy theory endorsement on compliance with COVID-19 public health measures and explored variations in these associations across participant age and the developmental status of a country, leveraging a large multi-national data set (N = 45,772) across 66 countries/territories, collected via online survey during the early phase of the COVID-19 pandemic (between April and May 2020). Human Development Index (HDI), developed by the United Nations Development Program, was used as a proxy of a country's achievement in key dimensions of human development. Overall, higher risk perception was associated with greater compliance, particularly in individuals with greater conspiracy theory endorsement. Specifically, people from more developed countries who perceived themselves less at risk but showed stronger conspiracy theory endorsement reported the lowest compliance with COVID-19 public health measures. Findings from this study advance understanding of the interplay between risk perception and conspiracy theory endorsement in their effect on compliance with COVID-19 public health measures, under consideration of both individual-level and country-level demographic variables and have potential to inform the design of tailored interventions to fight the current and future global pandemics.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| 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".