Factors Impacting COVID-19 Vaccine Uptake and Confidence Among Immigrant and Refugee Populations in Canada
Bibliographic record
Abstract
OBJECTIVE: This study examines the barriers and facilitators to COVID-19 vaccination among immigrant and refugee populations, with a focus on informing primary healthcare stakeholders on effective strategies to address the health needs of these groups. Although conducted in Canada, the findings are relevant to countries facing similar challenges in promoting vaccine uptake among migrant communities. METHODS: As part of an evaluation of best practices in COVID-19 vaccination promotion and provision, data were collected using in-depth key informant interviews with a cross-section of primary care stakeholders (n = 11). MAIN FINDINGS: Key barriers to vaccine promotion and provision included distrust of health and government services, misinformation, lack of vaccine confidence, and access or systems-level barriers. Effective facilitators were relationship-building and equity-driven approaches, such as community engagement and development, culturally and linguistically effective communication, one-on-one supports, and collaboration with community members as valued partners and staff. These strategies were identified as best practices that enhanced vaccine confidence and uptake. CONCLUSION: The risk and impacts of COVID-19 are disproportionately distributed worldwide, affecting migrant populations in many countries. Primary healthcare stakeholders must understand the barriers and facilitators to vaccine promotion to effectively address health inequalities. Increasing vaccine uptake and confidence among immigrant and refugee populations requires targeted and tailored approaches that are culturally responsive and equity-informed. These findings provide valuable insights for health systems globally, supporting efforts to reduce health inequities by using inclusive vaccination strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".