Mobilizing COVID-19 Vaccination Partnerships for Newcomer Refugees and Immigrants in the Calgary, Canada
Bibliographic record
Abstract
Context: The COVID-19 public health emergency significantly strained public health systems in the Calgary, Canada, area, and hindered vaccination efforts for newcomer refugee and immigrant communities. In response, community-health partnerships emerged in 2021-2022 to provide accessible, culturally responsive, and adapted services for community members. Objective: This qualitative research focuses on community mobilization and partnerships’ role in COVID-19 vaccine delivery for newcomer refugee and immigrants, challenges faced, and lessons learned. Study Design and Analysis: Researchers conducted structured interviews with Government Assisted Refugees (GARs) (n=39), and semistructured interviews with Privately Sponsored Refugees (PSRs) (n=6), private refugee sponsors (n=3), and stakeholders involved in vaccination systems (n=13). Thematic analysis was conducted to draw out themes related to community-based partnerships, strategies and actions of partnerships, alignment of vaccination efforts with the World Health Organization (WHO) recommendations to increase vaccine demand and uptake, and partnership challenges. Results: Partnerships varied in membership, funding, and capacity, and focused on vaccinating diverse communities. Their actions included information translation and transmission, outreach, and advocacy. They also drew on the expertise and relationships of community actors to make inroads in hard-to-reach communities. Partnerships faced challenges, including existing infrastructure and policies for COVID-19 vaccine distribution, which required substantial advocacy to resolve. Conclusions: As public health systems in the Calgary area did not adroitly address community needs during early vaccination drives, this became the catalyst for the community mobilization of COVID-19 vaccinations and ultimately drove community-health partnerships to form. These partnerships achieved high rates of vaccinations for newcomer refugees and immigrants, and empowered partners. This changed the dynamic between partners, health officials, and government officials, and effectively contributed to more equitable decision making within vaccine delivery groups. However, a key concern remains that health system changes which led to more equitable vaccinations for newcomer refugees and immigrants were short-term and COVID-19 specific, limiting community input on broader health service delivery changes.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".