Understanding COVID-19 vaccine hesitancy in vasculitis patients
Bibliographic record
Abstract
Objective To identify the factors that impact COVID-19 vaccine decision-making in vaccine-hesitant vasculitis patients, and compare their perceptions with other rheumatology patients, given existence of data suggesting rheumatology patients may have disease-specific factors that influence their COVID-19 vaccine decision-making. Methods This cross-sectional study surveyed adult rheumatology patients from the Kaye Edmonton Clinic Rheumatology Clinic, in Canada, between June and August 2021, using an anonymous online questionnaire. Survey responses were analyzed for statistical differences using chi-square analysis. Results The COVID-19 Vaccine Perceptions Survey had a response rate of 70.9%. Of the total 231 respondents, 103 patients were diagnosed with vasculitis. At the time of the survey, 10.6% of vasculitis patients refused to receive a COVID-19 vaccine compared to 6.3% for other rheumatology patients. Compared to other rheumatology patients, vaccine-hesitant vasculitis patients were significantly more concerned about almost every aspect of available COVID-19 vaccines [e.g., safety (p < 0.001), components (p < 0.001)], and feared that they could contract SARS-CoV-2 from a vaccine (p < 0.001). These vaccine-hesitant patients were also significantly less pleased with the government's pandemic response, less confident in healthcare team-provided information (p < 0.001), and more likely to report that healthcare providers had no role in their COVID-19 vaccine decision-making (p < 0.001). Conclusion Vaccine-hesitant vasculitis patients may have multiple considerations influencing COVID-19 vaccine hesitancy, including vaccine and disease-specific concerns, along with unfavorable perceptions of the healthcare system (government and healthcare providers). Healthcare providers can address some of these concerns by initiating patient-centered discussions around immunizations to help support educated decision-making.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".