Religious engagement and antibody response to the COVID‐19 vaccine
Bibliographic record
Abstract
Abstract This research examined religious engagement and subsequent antibody responses to the COVID‐19 vaccine. Using publicly available data from the Understanding Society survey, we employed a longitudinal design. Between January 2016 and May 2018, respondents completed measures of religious belonging, frequency of attending religious services (i.e., extrinsic religiosity), and the difference religion made to their lives (i.e., intrinsic religiosity). A COVID‐19 survey wave was collected in March 2021 and measured antibody responses to the COVID‐19 vaccine via blood draw. A final sample of 746 adults [462 (61.9%) females, M age = 61.94, SD = 19.07] was achieved. Mediation analyses (PROCESS, Model 4; Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression‐based approach , The Guildford Press, 2022; Introduction to mediation, moderation, and conditional process analysis: A regression‐based approach ; The Guildford Press) revealed one pathway through which religion and antibody responses to the COVID‐19 vaccine are associated, namely via extrinsic factors—attendance at religious services. In contrast, intrinsic religious factors which is the difference religion can make to one's life, was not a significant mediator. Overall, this analysis provides evidence that behavioural enactment of religion matters to the effectiveness of vaccination and the management of public health crises. It also highlights the value of social resources associated with engagement in valued social groups—and in particular religious social groups—for public health.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".