“We need to protect each other”: COVID-19 vaccination intentions and concerns among Racialized minority and Indigenous Peoples in Canada
Bibliographic record
Abstract
People may choose to receive vaccines in response to pressures that outweigh any concerns that they have. We explored Racialized minority and Indigenous Peoples' motivations for, perceptions of choice in, and concerns about, COVID-19 vaccination. We used a sequential explanatory mixed methods approach, including a national survey administered around the time vaccines were first authorized (Dec 2020) followed by qualitative interviews when vaccines were becoming more readily available to adults (May-June 2021). We analyzed survey data using descriptive statistics and interviews using critical feminist methodologies. Survey respondents self-identified as a Racialized minority (n = 1488) or Indigenous (n = 342), of which 71.4% and 64.6%, respectively, intended to receive a COVID-19 vaccine. Quantitative results indicated perceptions of COVID-19 disease were associated with vaccination intention. For instance, intention was associated with agreement that COVID-19 disease is severe, risk of becoming sick is great, COVID-19 vaccination is necessary, and vaccines available in Canada will be safe (p < 0.001). COVID-19 vaccines were in short supply in Canada when we subsequently completed qualitative interviews with a subset of Racialized minority (n = 17) and Indigenous (n = 10) survey respondents. We coded interview transcripts around three emergent themes relating to governmentality and cultural approaches to intersectional risk theories: feelings of collective responsibility, choice as privilege, and remaining uncertainties about COVID-19 vaccines. For example, some mentioned the responsibility and privilege to receive a vaccine earlier than those living outside of Canada. Some felt constraints on their freedom to choose to receive or refuse a vaccine from intersecting oppressions or their health status. Although all participants intended to get vaccinated, many mentioned uncertainties about the safety and effectiveness of COVID-19 vaccination. Survey respondents and interview participants demonstrated nuanced associations of vaccine acceptance and hesitancy shaped by perspectives of vaccine-related risks, symbolic associations of vaccines with hope, and intersecting social privileges and inequities (including racialization).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".