Spirituality and religiosity contribute to ongoing COVID-19 vaccination rates: Comparing 195 regions around the world
Bibliographic record
Abstract
Vaccine hesitancy has taken global prominence with the rapid spread of COVID-19, but what factors are related to this considerable variation in vaccination rates globally? Three studies that encompass 195 unique regions from around the world found that the relative spirituality and religiosity of a region predict ongoing COVID-19 vaccination rates, such that those regions higher in spirituality and/or religiosity are regions with lower COVID-19 vaccination rates. In Study 1, data from 23 regions globally were obtained, and both spirituality and religiosity were negatively associated with vaccination rates. These effects held when applying two methods to account for vaccine supply issues. In Study 2, data from 144 regions globally were obtained, and once again religiosity negatively predicted COVID-19 vaccination rates. It remained a significant predictor of vaccination rates when controlling for GDP, population age, collectivism, general skepticism towards vaccinations, and previous inoculation history. In Study 3, data from all USA states and the District of Columbia were obtained, and religiosity and spirituality once again were negatively associated with COVID-19 vaccination rates. Effects held controlling for other factors. Across studies, spirituality and religiosity account for a large amount of the variance in vaccination rates. These results suggest that real-world behavior can be predicted by the relative spirituality and religiosity of a region.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".