‘It’s really embarrassing […] to ask for help:’ navigating invisible and intersecting inequities in barriers to getting vaccinated
Bibliographic record
Abstract
The arrival of COVID-19 vaccines in Canada marked the first time since the H1N1 influenza pandemic that adult vaccination became central in public health discourse, thus evoking feelings and discourse about individuals’ responsibility to get vaccinated. Public health institutions made efforts to ensure vaccines were accessible, and various local organizations worked to fill gaps in accessibility (e.g. vaccine clinics for equity-denied communities). However, despite knowledge of barriers to accessing healthcare, and specifically vaccines, public and health discourses emphasized individual objection as the main barrier to optimal vaccine uptake. This study explored how individuals who identified as a racialized minority or Indigenous person, perceived their personal responsibility to get vaccinated. We gathered perspectives in qualitative interviews (N = 27) when vaccines were first becoming available to adults living in Canada (May-June 2021). Theory about moral regulation, risk governance, and emotion informed our analysis. Findings suggest that individuals engaged in the governance of oneself and others towards getting vaccinated. However, participants also critiqued discourses that hold individuals responsible for accessing vaccines. They identified substantial barriers to accessing vaccines for individuals with fewer resources (e.g. travel time, missing work, childcare). When referencing individuals who do not know how to access information about vaccination, one participant stated, ‘it’s really embarrassing […] to ask for help.’ Feelings of individual responsibility may prevent some people uncertain about vaccines from asking for more information and support. We argue that the focus on vaccine hesitancy without attention to systemic barriers deflects responsibility for making vaccines accessible from centralized institutions to individuals.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".