COVID-19 Vaccine Intentions and Perceptions Among Public School Staff of the Greater Vancouver Metropolitan Area, British Columbia, Canada
Bibliographic record
Abstract
Background The purpose of this study was to explore factors associated with COVID-19 vaccine intentions among school staff as high vaccine uptake is essential to ensure schools return to normal activities. Methods Staff (e.g., teachers, administrators, student support workers) from three urban school districts in the Greater Vancouver Area of British Columbia, Canada completed a survey between February and June 2021 (n = 2,393) on COVID-19 vaccine intentions and perceptions (i.e., acceptance of routine vaccines, benefits and risks of vaccination, susceptibility to, and severity of COVID-19, recommendation by authority figures, information mistrust and conspiracy beliefs) as part of a COVID-19 seroprevalence study. Confirmatory factor analysis followed by multiple logistic regression models adjusting for relevant covariates were used to identify vaccine perceptions uniquely associated with (a) intention to get the COVID-19 vaccine (intention), and (b) intention to get vaccinated right away (urgency). Results In total, 95.4% of participants of the seroprevalence study completed the vaccine questionnaire, corresponding to 17.7% of the target population. Vaccine intention was associated with staff who valued expert recommendations (AOR = 10.5, 95% CI = 7.39–14.90) accepted routine vaccines (AOR = 1.94, 95% CI = 1.26–2.98) and perceived higher benefits (AOR = 1.29, 95% CI = 1.01–1.65) and lower safety risks of vaccination (AOR = 0.40, 95% CI = 0.29–0.54). Comparable associations were found with vaccine urgency. Perceived susceptibility to the COVID-19 virus was uniquely associated with vaccine urgency (AOR = 1.30, 95% CI = 1.05–1.61). A significant interaction effect (p = 0.01) revealed that staff who expressed mistrust in COVID-19 information intended to get vaccinated only if they also perceived high benefits of vaccination. Conclusions Education about the risks and benefits of COVID-19 vaccines from a trusted source had the strongest relationship with vaccine intentions among this occupational group. Notably, those who expressed mistrust in information still intended to get vaccinated if they also perceived strong benefits of the vaccine.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".