Intersecting Inequities in COVID-19 Vaccination: A Discourse Analysis of Information Use and Decision-Making Among Ethnically Diverse Parents in Canada
Bibliographic record
Abstract
BACKGROUND: Little is known about how intersecting social privilege and disadvantage contribute to inequities in COVID-19 information use and vaccine access. This study explored how social inequities intersect to shape access to and use of COVID-19 information and vaccines among parents in Canada. METHODS: We conducted semi-structured interviews on COVID-19 vaccination information use with ethnically diverse parents of children ages 11 to 18 years from April to August 2022. We purposefully invited parents from respondents to a national online survey to ensure representation across diverse intersecting social identities. Five researchers coded transcripts in NVivo using a discourse analysis approach informed by intersectionality. Our analysis focused on use of vaccine information and intersecting privileges and oppressions, including identifying with equity-denied group(s). RESULTS: Interview participants (N = 48) identified as ethnically diverse non-Indigenous (n = 40) and Indigenous (n = 8) Peoples from seven Canadian provinces. Racialized minority or Indigenous participants reflected on historical and contemporary events of racism from government and medical institutions as barriers to trust and access to COVID-19 information, vaccines, and the Canadian healthcare system. Participants with privileged social locations showed greater comfort in resisting public health measures. Despite the urgency to receive COVID-19 vaccines, information gaps and transportation barriers delayed vaccination among some participants living with chronic medical conditions. CONCLUSION: Historicization of colonialism and ongoing events of racism are a major barrier to trusting public health information. Fostering partnerships with trusted leaders and/or healthcare workers from racialized communities may help rebuild trust. Healthcare systems need to continuously implement strategies to restore trust with Indigenous and racialized populations.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".