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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".