Predictors of Vaccine Acceptance, Confidence, and Hesitancy in General, and COVID-19 Vaccination Refusal in the Province of Quebec, Canada
Bibliographic record
Abstract
Introduction: A surge of COVID-19 variants is a major concern, and literatures that support developing an optimum level of herd immunity are meaningful. This study aimed to examine the factors associated with vaccine acceptance, confidence, and hesitancy in general, and COVID-19 vaccination refusal in the general population of Quebec, Canada. Methods: A web-based cross-sectional survey was conducted in October and November 2020 among French-speaking participants above 18 years of age employing quota sampling technique. The questionnaire included socio-demographic and attitudinal variables towards vaccination. Logistic regression analyses were conducted to examine the association between independent and outcome variables. Results: Of total 1599 participants, 88.9%, 87.5%, 78.5%, and 18.2%, respectively, indicated vaccine acceptance, high level of vaccine confidence, low level of vaccine hesitancy, and COVID-19 vaccination refusals. Participants having higher education, income, and fear of COVID-19 (FCV-19S) were more likely to get vaccinated, while smokers were less likely to get vaccinated. Similarly, age groups (40-59, and ≥60 years), higher education, income, permanent resident in Canada, country of parents from Canada, ever faced acute disease in the family, higher sense of coherence, and FCV-19S scores were predictors of high levels of vaccine confidence. Higher education, income, sense of coherence and FCV-19S scores, and higher health-related quality of life (CORE-6D) produced lower levels of vaccine hesitancy. Conversely, those acting as caretaker, other essential worker, smoker, and those with financial losses were more likely to have higher vaccine hesitancy. Additionally, ≥60 years of age, higher education and income, country of parents from Canada, higher scores of willingness to take risk and FCV-19S were less likely to have high level of COVID-19 vaccination refusal. Conclusion: Over three quarters of the participants indicated positive attitudes toward vaccination. Some socio-demographic and health-related quality of life factors were associated with the outcome variables, and these should be sought while designing interventions to improve COVID-19 vaccination rates.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".