Perceptions of COVID-19 risk, vaccine access and confidence: a qualitative description of South Asians in Canada
Bibliographic record
Abstract
OBJECTIVES: In the first full year of the COVID-19 pandemic (2020), South Asians living in the Greater Toronto and Hamilton Area (GTHA) and Greater Vancouver area (GVA) experienced specific barriers to accessing SARS-CoV-2 testing and reliable health information. However, between June 2021 and February 2022, the proportion of people having received at least one COVID-19 vaccine dose was higher among this group (96%) than among individuals who were not visible minorities (93%). A better understanding of successful approaches and the challenges experienced by those who remain unvaccinated among this highly vaccinated group may improve public health outreach in subsequent waves of the current pandemic or for future pandemic planning. Using qualitative methods, we sought to explore the perceptions of COVID-19 risk, vaccine access, uptake and confidence among South Asians living in Canada. DESIGN: Semistructured interviews conducted with 25 participants analysed using thematic analysis. Throughout this process, we held frequent discussions with members of the study's advisory group to guide data collection (community engagement, recruitment and data analysis). SETTING: Communities of the GTHA and GVA with interviews conducted virtually over Zoom or telephone. PARTICIPANTS: 25 participants (15 from Ontario and 10 from British Columbia) were interviewed between July 2021 and January 2022. 10 individuals were community members, 9 were advocacy group leaders and 6 were public health staff. RESULTS: Access to and confidence in the COVID-19 vaccine was impacted by individual risk perceptions; sources of trusted information (ethnic and non-ethnic); impact of COVID-19 and the pandemic on individuals, families and society; and experiences with COVID-19 mandates and policies (including temporal and generational differences). Approaches that include community-level awareness and tailored outreach (language and cultural context) were considered successful. CONCLUSIONS: Understanding factors and developing strategies that build vaccine confidence and improve access can guide approaches that increase vaccine acceptance in the current and future pandemics.Visual abstract can be found at https://drive.google.com/file/d/1iXdnJj9ssc3hXCllZxP0QA9DhHH-7uwB/view.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".