Factors Associated with COVID-19 Vaccine Hesitancy among Visible Minority Groups from a Global Context: A Scoping Review
Bibliographic record
Abstract
Vaccine hesitancy is one of the top ten greatest threats to global health. During the COVID-19 era, vaccine hesitancy poses substantial risks, especially in visible minorities, who are disproportionately affected by the pandemic. Although evidence of vaccine hesitancy exists, there is minimal focus on visible minorities and the reasons for hesitancy in this group are unclear. Identifying these populations and their reasons for vaccine hesitancy is crucial in improving vaccine uptake and curbing the spread of COVID-19. This scoping review follows a modified version of the Arksey and O'Malley strategy. Using comprehensive search strategies, advanced searches were conducted on Medline, CINAHL, and PubMed databases to acquire relevant articles. Full-text reviews using inclusion and exclusion criteria were performed to extract themes of vaccine hesitancy. Themes were grouped into factors using thematic qualitative analysis and were objectively confirmed by principal component analysis (PCA). To complement both analyses, a word cloud of titles and abstracts for the final articles was generated. This study included 71 articles. Themes were grouped into 8 factors and the top 3 recurring factors were safety and effectiveness of the vaccine, mistrust, and socioeconomic characteristics. Shedding light on these factors could help mitigate health inequities and increase overall vaccine uptake worldwide through interventions and policies targeted at these factors. Ultimately, this would help achieve global herd immunity.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".