Justice beliefs and cultural values predict support for COVID-19 vaccination and quarantine behavioral mandates: a multilevel cross-national study
Bibliographic record
Abstract
Understanding how individual beliefs and societal values influence support for measures to prevent SARS-CoV-2 transmission is vital to developing and implementing effective prevention policies. Using both Just World Theory and Cultural Dimensions Theory, the present study considered how individual-level justice beliefs and country-level social values predict support for vaccination and quarantine policy mandates to reduce SARS-CoV-2 transmission. Data from an international survey of adults from 46 countries (N = 6424) were used to evaluate how individual-level beliefs about justice for self and others, as well as national values-that is, power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, and indulgence-influence support for vaccination and quarantine behavioral mandates. Multilevel modeling revealed that support for vaccination and quarantine mandates were positively associated with individual-level beliefs about justice for self, and negatively associated with country-level uncertainty avoidance. Significant cross-level interactions revealed that beliefs about justice for self were associated more strongly with support for mandatory vaccination in countries high in individualism, whereas beliefs about justice for others were more strongly associated with support for vaccination and quarantine mandates in countries high in long-term orientation. Beliefs about justice and cultural values can independently and also interactively influence support for evidence-based practices to reduce SARS-CoV-2 transmission, such as vaccination and quarantine. Understanding these multilevel influences may inform efforts to develop and implement effective prevention policies in varied national contexts.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".