COVID-19 Vaccinations, Trust, and Vaccination Decisions within the Refugee Community of Calgary, Canada
Bibliographic record
Abstract
Refugee decisions to vaccinate for COVID-19 are a complex interplay of factors which include individual perceptions, access barriers, trust, and COVID-19 specific factors, which contribute to lower vaccine uptake. To address this, the WHO calls for localized solutions to increase COVID-19 vaccine uptake for refugees and evidence to inform future vaccination efforts. However, limited evidence engages directly with refugees about their experiences with COVID-19 vaccinations. To address this gap, researchers conducted qualitative interviews (N = 61) with refugees (n = 45), sponsors of refugees (n = 3), and key informants (n = 13) connected to local COVID-19 vaccination efforts for refugees in Calgary. Thematic analysis was conducted to synthesize themes related to vaccine perspectives, vaccination experiences, and patient intersections with policies and systems. Findings reveal that refugees benefit from ample services that are delivered at various stages, that are not solely related to vaccinations, and which create multiple positive touch points with health and immigration systems. This builds trust and vaccine confidence and promotes COVID-19 vaccine uptake. Despite multiple factors affecting vaccination decisions, a key reason for vaccination was timely and credible information delivered through trusted intermediaries and in an environment that addressed refugee needs and concerns. As refugees placed trust and relationships at the core of decision-making and vaccination, it is recommended that healthcare systems work through trust and relationships to reach refugees. This can be targeted through culturally responsive healthcare delivery that meets patients where they are, including barrier reduction measures such as translation and on-site vaccinations, and educational and outreach partnerships with private groups, community organizations and leaders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".