Faith and vaccination: a scoping review of the relationships between religious beliefs and vaccine hesitancy
Bibliographic record
Abstract
BACKGROUND: Throughout history, vaccines have proven effective in addressing and preventing widespread outbreaks, leading to a decrease in the spread and fatality rates of infectious diseases. In a time where vaccine hesitancy poses a significant challenge to public health, it is important to identify the intricate interplay of factors exemplified at the individual and societal levels which influence vaccination behaviours. Through this analysis, we aim to shed new light on the dynamics of vaccine hesitancy among religious groups, contributing to the broader effort to promote vaccine uptake, dispel misunderstandings, and encourage constructive dialogue with these groups. METHODS: We used the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) using the 20-point checklist to guide this review. The inclusion criteria for our study were that the literature should be in English, concerned with vaccine hesitancy as the focus of study, study the impact religiosity or religious beliefs as either an outcome or control variable, concerning population levels, and be peer-reviewed. RESULTS: We analysed 14 peer-reviewed articles that included components related to religiosity or religious beliefs and their impact on vaccine hesitancy published until September 2023. All the articles were published in approximately the last decade between 2012 and 2023, with only 4 of the articles published before 2020. Out of the 14 studies included in our review, twelve utilized quantitative methods, while the remaining two employed qualitative approaches. Among the studies included in our analysis, we found various approaches to categorizing religious belief and identity. In most studies when religion is uniformly regarded as the sole determinant of vaccine hesitancy, it consistently emerges as a significant factor in contributing to vaccine hesitancy. All studies in our review reported sociodemographic factors to some degree related to vaccine hesitancy within their sample populations. Our analysis underscored the need for nuanced approaches to addressing vaccine hesitancy among religious groups. CONCLUSION: Vaccine hesitancy is a complex issue and driven by a myriad of individual and societal factors among which religious beliefs is commonly associated to be a driver of higher levels among populations.
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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 | 0.019 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".