Ethnic Disparities in COVID-19 Vaccine Mistrust and Receipt in British Columbia, Canada: Population Survey
Bibliographic record
Abstract
BACKGROUND: Racialized populations in the United States, Canada, and the United Kingdom have been disproportionately affected by COVID-19. Higher vaccine hesitancy has been reported among racial and ethnic minorities in some of these countries. In the United Kingdom, for example, higher vaccine hesitancy has been observed among the South Asian population and Black compared with the White population, and this has been attributed to lack of trust in government due to historical and ongoing racism and discrimination. OBJECTIVE: This study aimed to assess vaccine receipt by ethnicity and its relationship with mistrust among ethnic groups in British Columbia (BC), Canada. METHODS: We included adults ≥18 years of age who participated in the BC COVID-19 Population Mixing Patterns Survey (BC-Mix) from March 8, 2021, to August 8, 2022. The survey included questions about vaccine receipt and beliefs based on a behavioral framework. Multivariable logistic regression was used to assess the association between mistrust in vaccines and vaccine receipt among ethnic groups. RESULTS: The analysis included 25,640 adults. Overall, 76.7% (22,010/28,696) of respondents reported having received at least 1 dose of COVID-19 vaccines (Chinese=86.1%, South Asian=79.6%, White=75.5%, and other ethnicity=73.2%). Overall, 13.7% (3513/25,640) of respondents reported mistrust of COVID-19 vaccines (Chinese=7.1%, South Asian=8.2%, White=15.4%, and other ethnicity=15.2%). In the multivariable model (adjusting for age, sex, ethnicity, educational attainment, and household size), mistrust was associated with a 93% reduced odds of vaccine receipt (adjusted odds ratio 0.07, 95% CI 0.06-0.08). In the models stratified by ethnicity, mistrust was associated with 81%, 92%, 94%, and 95% reduced odds of vaccine receipt among South Asian, Chinese, White, and other ethnicities, respectively. Indecision, whether to trust the vaccine or not, was significantly associated with a 70% and 78% reduced odds of vaccine receipt among those who identified as White and of other ethnic groups, respectively. CONCLUSIONS: Vaccine receipt among those who identified as South Asian and Chinese in BC was higher than that among the White population. Vaccine mistrust was associated with a lower odds of vaccine receipt in all ethnicities, but it had a lower effect on vaccine receipt among the South Asian and Chinese populations. Future research needs to focus on sources of mistrust to better understand its potential influence on vaccine receipt among visible minorities in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".