Predictors of Compliance with COVID-19 Guidelines Across Countries: The role of social norms, moral values, trust, stress, and demographic factors
Bibliographic record
Abstract
Despite the devastating impacts of the COVID-19 pandemic, it provided the opportunity to investigate factors associated with compliance with public health measures that could inform responses to future pandemics. We analysed cross-country data (k = 121, N = 15,740) collected one year into the COVID-19 pandemic to investigate factors related to compliance with COVID-19 guidelines. These factors include social norms, moral values, trust, stress, and demographic factors. We found that social norms to follow preventive measures were positively correlated with compliance with local prevention guidelines. Compliance was also predicted by concern about the moral value of harm and care, trust in government and the scientific community, stress, and demographic factors. Finally, we discuss country-level differences in the associations between predictors and compliance. Overall, results indicate that the harm/care dimension of moral foundations and trust are critical to the development of programs and policies aimed at increasing compliance with measures to reduce the spread of disease.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".