An equitable vaccine delivery system: Lessons from the COVID-19 vaccine rollout in Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic exacerbated existing health disparities and disproportionately affected vulnerable individuals and communities (e.g., low-income, precariously housed or in institutional settings, racialized, migrant, refugee, 2SLBGTQ+). Despite their higher risk of infection and sub-optimal access to healthcare, Canada's COVID-19 vaccination strategy focused primarily on age, as well as medical and occupational risk factors. METHODS: We conducted a mixed-methods constant comparative qualitative analysis of epidemiological data from a national database of COVID-19 cases and vaccine coverage in four Canadian jurisdictions. Jurisdictional policies, policy updates, and associated press releases were collected from government websites, and qualitative data were collected through 34 semi-structured interviews of key informants from nine Canadian jurisdictions. Interviews were coded and analyzed for themes and patterns. RESULTS: COVID-19 vaccines were rolled out in Canada in three phases, each accompanied by specific challenges. Vaccine delivery systems typically featured large-venue mass immunization sites that presented a variety of barriers for those from vulnerable communities. The engagement and targeted outreach that featured in the later phases were driven predominantly by the efforts of community organizations and primary care providers, with limited support from provincial governments. CONCLUSIONS: While COVID-19 vaccine rollout in Canada is largely considered a success, such an interpretation is shaped by the metrics chosen. Vaccine delivery systems across Canada need substantial improvements to ensure optimal uptake and equitable access for all. Our findings suggest a more equitable model for vaccine delivery featuring early establishment of local barrier-free clinics, culturally safe and representative environment, as well as multi-lingual assistance, among other vulnerability-sensitive elements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".