Canadian health care providers' and education workers' hesitance to receive original and bivalent COVID-19 vaccines
Bibliographic record
Abstract
BACKGROUND: The demand for COVID-19 vaccines has diminished as the pandemic lingers. Understanding vaccine hesitancy among essential workers is important in reducing the impact of future pandemics by providing effective immunization programs delivered expeditiously. METHOD: Two surveys exploring COVID-19 vaccine acceptance in 2021 and 2022 were conducted in cohorts of health care providers (HCP) and education workers participating in prospective studies of COVID-19 illnesses and vaccine uptake. Demographic factors and opinions about vaccines (monovalent and bivalent) and public health measures were collected in these self-reported surveys. Modified multivariable Poisson regression was used to determine factors associated with hesitancy. RESULTS: In 2021, 3 % of 2061 HCP and 6 % of 3417 education workers reported hesitancy (p < 0.001). In December 2022, 21 % of 868 HCP and 24 % of 1457 education workers reported being hesitant to receive a bivalent vaccine (p = 0.09). Hesitance to be vaccinated with the monovalent vaccines was associated with earlier date of survey completion, later receipt of first COVID-19 vaccine dose, no influenza vaccination, and less worry about becoming ill with COVID-19. Factors associated with hesitance to be vaccinated with a bivalent vaccine that were common to both cohorts were receipt of two or fewer previous COVID-19 doses and lower certainty that the vaccines were safe and effective. CONCLUSION: Education workers were somewhat more likely than HCP to report being hesitant to receive COVID-19 vaccines but reasons for hesitancy were similar. Hesitancy was associated with non-receipt of previous vaccines (i.e., previous behaviour), less concern about being infected with SARS-CoV-2, and concerns about the safety and effectiveness of vaccines for both cohorts. Maintaining inter-pandemic trust in vaccines, ensuring rapid data generation during pandemics regarding vaccine safety and effectiveness, and effective and transparent communication about these data are all needed to support pandemic vaccination programs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".