Strengthening access to and confidence in COVID-19 vaccines among equity-deserving populations across Canada: An exploratory qualitative study
Bibliographic record
Abstract
INTRODUCTION: There is a need to reflect on the COVID-19 vaccine distribution plans across Canada and the extent to which they considered equity-deserving populations, as lessons from the rollout can inform future emergency responses and foster trust in public health. This paper examined and compared strategies implemented by six Canadian provinces to increase access and promote the uptake of COVID-19 vaccines among selected priority populations. We also explored the factors that impacted the implementation of these strategies. METHODS: In six provinces (Alberta, British Columbia, Manitoba, Nova Scotia, Ontario, and Quebec), we conducted an environmental scan of provincial rollout documents and media sources reporting vaccine distribution among selected priority populations: First Nations, Inuit, and Métis; Black communities; essential workers; people experiencing homelessness; and people with disabilities. We subsequently interviewed 39 key informants to validate the environmental scan results, identify additional strategies to increase COVID-19 vaccine uptake, and uncover perceptions of the facilitators and challenges that influenced the strategies implementation. RESULTS: We identified that provincial health authorities employed several strategies to overcome structural, geographical, and attitudinal barriers to COVID-19 vaccines experienced by the priority populations. Most provinces implemented walk-in, mobile, and pop-up vaccination clinics, mobilized their public and private health workforce, and designed multilingual communication materials. Facilitators in implementing COVID-19 vaccination strategies included harmonizing communication efforts, leveraging existing relationships and networks, and ensuring representation and leadership of community partners. Challenges to implementing COVID-19 vaccination strategies included uncoordinated communication efforts, inadequate distribution of vaccines to areas with the greatest need, mistrust in the government and healthcare system, vaccine hesitancy, and lack of cultural competence by vaccine providers. CONCLUSIONS: This study highlights the divide between well-intentioned strategies and interventions and the reality of on-the-ground implementation. The findings offer valuable insights and can inform the implementation of strategies to distribute vaccines equitably in future large-scale vaccination efforts in Canada and globally.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".