Initial Mix-and-Match COVID-19 Vaccination Perceptions, Concerns, and Side Effects across Canadians
Bibliographic record
Abstract
Research indicates that mixing the first two doses of COVID-19 vaccine types (i.e., adenoviral vector and mRNA) produces potent immune responses against the coronavirus, but it is unclear how individuals may perceive these benefits, or whether there are different concerns compared to individuals who received two doses of the same vaccine. This research examines the demographic characteristics, psychological perceptions, and vaccination-related opinions and experiences of a large Canadian sample (N = 1002) who had received two initial doses of any COVID-19 vaccine combination. Participants included 791 (78.9%) who received two doses of the exact same brand and type of vaccine, 164 (16.4%) who received two doses of the same type of vaccine (i.e., either mRNA or adenoviral vector) but from different brands (e.g., Pfizer-BioNTech + Moderna), and 47 (4.7%) who received two doses from different types and brands of vaccine (e.g., Oxford-AstraZeneca + Pfizer-BioNTech). Results showed that, after the first vaccine dose, participants who received an adenoviral vector vaccine (e.g., Oxford-AstraZeneca) experienced the highest number of common side effects, and more severe levels of each side effect compared to those who received an mRNA vaccine (e.g., Pfizer-BioNTech or Moderna). After the second dose, participants who received Moderna as their second vaccine experienced the highest number of and most severe side effects, regardless of whether they received Moderna, Pfizer-BioNTech, or Oxford-AstraZeneca as their first dose. Real-world implications of these findings are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".