COVID-19 vaccine acceptance and preference for future delivery among language minority, newcomer, and racialized peoples in Canada: a national cross-sectional and longitudinal study
Bibliographic record
Abstract
BACKGROUND: Despite high COVID-19 vaccine coverage in Canada, vaccine acceptance and preferred delivery among newcomers, racialized persons, and those who primarily speak minority languages are not well understood. This national study explores COVID-19 vaccine acceptance, access to vaccines, and delivery preferences among ethnoculturally diverse population groups. METHODS: We conducted two national cross-sectional surveys during the pandemic (Dec 2020 and Oct-Nov 2021). Binary logistic regression analysis investigated the association between newcomer, language, and racialized minority respondents' perceptions and acceptance of COVID-19 vaccines, experiences of discrimination when accessing health services, and sociodemographic characteristics. McNemar-Bowker tests were used to assess changes in responses collected at two time points. RESULTS: Among 1630 respondents, 30.8% arrived in Canada within the last five years, 87.4% self-identified as a racialized minority, and 37.2% primarily spoke languages other than English or French. Although single dose COVID-19 vaccine uptake was at 92.7% among respondents, 14.8% experienced difficulty accessing vaccines, citing a need for translated resources or multi-lingual personnel. In longitudinal analysis, respondents were increasingly motivated over time to overcome barriers to accessing vaccines (61.4% to 69.6%, p = <.001). Fifty-nine percent (59.9%) of respondents would accept annual vaccination and over half would accept co-administration with routine (56.2%) or influenza (52.3%) vaccines. Experiences of racism/discrimination upon health service access were reported by 12.3% of respondents, who recommended increasing culturally safe practices and community involvement at vaccination sites. CONCLUSIONS: Understanding how newcomers, racialized peoples, and minority language speakers perceive and access COVID-19 vaccines will support vaccination campaigns to optimize equitable access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".