An exploration of COVID-19 vaccination models for newcomer refugees and immigrants in Calgary, Canada
Bibliographic record
Abstract
BACKGROUND: The World Health Organization 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 newcomer refugees and immigrants, 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 newcomer refugees and immigrants. 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, and strategies of vaccine delivery models and the intersections with patient experiences. RESULTS: Newcomer refugee and immigrant focused vaccination models and strategies 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 other determinants of under-vaccination. Many vaccination models presented through interviews were not specific to refugees and included immigrants, temporary residents, ethnocultural community members, and other vulnerable populations in their design. CONCLUSIONS: Increasing COVID-19 vaccine uptake for newcomer refugees and immigrants, 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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".