An Exploration of COVID-19 Vaccination Models for Refugees and Newcomer Immigrants in a Canadian City
Bibliographic record
Abstract
Abstract Background The World Health Organizations stresses the need for tailored COVID-19 models of vaccination to meet the needs of diverse populations and ultimately reach high rates of vaccination. However, little evidence exists on how COVID-19 models of vaccination operated in the novel context of the pandemic, how vulnerable populations, such as refugees, experience COVID-19 vaccination systems in high-income countries, and what lessons may be learned from vaccination efforts with vulnerable populations. To address this gap, this study explored COVID-19 vaccine delivery models available to refugees and newcomers, and refugee experiences across different COVID-19 vaccine delivery models in Calgary, Canada, and surrounding area in 2021 and 2022, to understand the barriers, strengths, and strategies of models to support access to COVID-19 vaccination for newcomers (with a focus on refugees). Methods Researchers conducted structured interviews with Government Assisted Refugees (n = 39), and semi-structured interviews with Privately Sponsored Refugees (n = 6), private refugee sponsors (n = 3), and stakeholders involved in vaccination systems (n = 13) in 2022. Thematic analysis was conducted to draw out themes related to barriers, strengths, strategies of vaccine delivery models, and intersections with patient experiences. Results Newcomer-specific and mainstream vaccination models were explored. They demonstrated how partnerships between organizations, multi-pronged approaches, and culturally responsive services were crucial to navigate ongoing and emergent factors, such as vaccine hesitancy, mandates, and determinants of under-vaccination. Many vaccination models were not specific to refugees and included newcomers and established immigrants. Conclusions Increasing COVID-19 vaccine uptake for refugees and newcomers is complex and requires trust, ongoing information provision, and local partnerships to address ongoing and emerging factors. Three key policy implications were drawn. First, findings demonstrated the need for flexible funding to offer outreach, translation, cultural interpretation, and to meet the basic needs of patients prior to engaging in vaccinations. Second, the research showed that embedding culturally responsive strategies within services ensures community needs are met. Finally, collaborating with partners that reflect the diverse needs of communities is crucial for the success of any health efforts serving newcomers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".