Intention to receive a COVID-19 vaccine: results from a population-based survey in Canada
Bibliographic record
Abstract
Abstract Background The success of any COVID-19 vaccine program ultimately depends on high vaccine uptake. This study determined overall intention to receive a COVID-19 vaccine and identified factors that predict intentions to be vaccinated against COVID-19 in Canada, specifically in key priority groups identified by the American Committee on Immunization Practice (ACIP) and the National Advisory Committee on Immunization (NACI) for early immunization. Methods Individuals from research cohorts from the general population of British Columbia aged 25–69 were invited complete an online survey based on validated scales and theoretical frameworks to explore intention to receive a COVID-19 vaccine. Two multivariable logistic regression models were conducted to determine factors associated with intention to receive the COVID-19 vaccine. Results Of 4948 respondents, 79.8% intended to receive a COVID-19 vaccine. In multivariable modeling, respondents who intended to receive the vaccine had higher vaccine attitudinal scores (p 60 years) were more likely to intend to receive the vaccine, while females (95%CI 0.57,0.93), those with less than high school education (95%CI 0.5,0.76), those who self-identified as non-white (95%CI 0.60,0.92), self-identified as Indigenous (95%CI 0.36,0.84) and essential non-health care workers (95%CI 0.59,0.86) had lower adjusted odds of intending to receive a COVID-19 vaccine. Conclusions To optimize vaccine coverage, public health should focus on key messages around vaccine safety and benefit, and leverage trusted practitioners for messaging. As certain key populations identified by NACI and ACIP for early immunization report a lower intention to vaccinate, there is a need for in-depth education and support for these communities to ensure optimal uptake.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".