Factors associated with COVID-19 vaccine confidence among Arab, Asian, Black, Indigenous, and White individuals in Canada: Latent profile analyses
Bibliographic record
Abstract
BACKGROUND: Stark disparities in COVID-19 infection, mortality and vaccine uptake have been observed between racial groups. However, differences in COVID-19 vaccine confidence between racialized groups and contextual factors that account for such differences have not been explored. We sought to determine socioeconomic profiles associated with COVID-19 vaccine confidence using both conventional and latent profile analyses (LPA). METHODS: A representative sample of 4220 Arab, Asian, Black, Indigenous, Mixed and White adults completed a survey conducted to examine COVID-19 vaccine confidence. We determined vaccine confidence by racial groups, and analyzed factors associated with vaccination confidence between different racialized groups. Regression analysis and LPA were used to determine profiles of vaccine confidence by race. RESULTS: Arab, Black, Indigenous and Mixed group respondents had lower vaccine confidence compared to White individuals, while Asian respondents had higher vaccine confidence compared to other racial groups. Vaccine confidence varied by age, gender, conspiracy beliefs, health literacy and experiences of racial discrimination. LPA produced Profile 1 with high vaccine confidence and health literacy, with low experience of discrimination and conspiracy beliefs; Profile 2 had low vaccine confidence and health literacy, with greatest experiences of discrimination and conspiracy beliefs. Compared to White respondents, Arab (odds ratio = 2.86;95 % CI 2.33-3.52), Indigenous (odds ratio = 2.29; 95 % CI, 1.88-2.78), and Black (odds ratio = 2.19; 95 % CI 1.80-2.66) respondents were more likely to belong to Profile 2. CONCLUSIONS: Vaccine confidence profiles for COVID-19 converge at the intersection of health literacy, experience of discrimination and conspiracy beliefs. COVID-19 vaccine confidence is heterogenous between racialized communities, with lowest confidence among Arab, Black and Indigenous individuals and highest confidence among Asian individuals. Understanding the source of this heterogeneity is crucial to design public health approaches that equitably ensure vaccine coverage among populations at highest risk of COVID-19 and its complications.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".