COVID-19 Vaccine Mandates and Vaccine Hesitancy among Black People in Canada
Bibliographic record
Abstract
OBJECTIVES: COVID-19 vaccine mandates increased vaccination rates globally. Implemented as a one-size-fits-all policy, these mandates have unintended harmful consequences for many, including Black Canadians. This article reports findings on the interconnectedness of vaccine mandates and vaccine hesitancy by describing a range of responses to mandatory COVID-19 vaccination policies among Black people in Canada. METHODS: Using qualitative research methods, semi-structured interviews with 36 Black people living in Canada aged 18 years and over across 6 provinces in Canada were conducted. Participants were selected across intersectional categories including migration status, income, religion, education, sex, and Black ethnicity. Thematic analysis informed the identification of key themes using Foucauldian notions of biopower and governmentality. RESULTS: Our results show how the power relations present in the ways many Black people actualize vaccine intentions. Two main themes were identified: acceptance of the COVID-19 vaccine in the context of governmentality and resistance to vaccine mandates driven by oppression, mistrust, and religion. CONCLUSION: COVID-19 vaccine mandates may have reinforced mistrust of the government and decreased confidence in the COVID-19 vaccine. Policy makers need to consider non-discriminatory public health policies and monitor how these policies are implemented over time and across multiple sectors to better understand vaccine hesitancy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".