Models of COVID-19 Vaccine Delivery for Refugees in Calgary, Canada
Bibliographic record
Abstract
Context: Refugees and migrants globally face inequities to healthcare and COVID-19 vaccination access, calling for tailored approaches to ensure equitable vaccine allocation. This research explored refugee specific models of COVID-19 vaccine delivery. Objective: The purpose was to understand the barriers and strengths of each model to support access to COVID-19 vaccination for refugees. Study Design and Analysis: The project used a mixed method approach that included secondary vaccination data of refugees and primary interview data. A mixed method data analysis approach was adopted to explore the research questions, and thematic analysis was conducted on qualitative data. Setting or Dataset: This study examined COVID-19 vaccination systems for refugees in the Calgary, Canada area. Population Studied: Research participants were identified through purposive sampling and include settlement and healthcare organization staff involved in vaccination pathways for refugees, sponsors of refugees, and refugees that were processed in Calgary. Intervention/Instrument: A database of refugee COVID-19 vaccinations was used to inform findings. Structured and semi-structured interview data was collected with settlement and healthcare organizations stakeholders (N=13), refugee sponsors (N=3) and refugees (N=45). Results: The research explored COVID-19 models of vaccine delivery for refugees, including: mobile vaccine clinics, temporary based community clinics, on-site vaccination clinics in refugee processing hotels, mainstream vaccination clinics and pharmacies. Models of vaccination delivery were not static. They evolved as a result of contextual factors, such as refugee needs, shifts in demographics, changes in public health policy and funding mandates. As a result, the impact on refugee health also evolved. Most models provided services in a culturally responsive manner and also served newcomers. Models created positive and culturally safe contexts through partnerships where barriers were mitigated and patients could access vaccinations. Partnerships provided health navigators, outreach, translation, built trust and helped models form new partnerships to address the emerging needs of patients. Conclusions: This project demonstrated that public health systems can adapt through partnerships and provide culturally responsive ways delivering vaccines. This has implications for the approach to health care service delivery for specialized populations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".