Sociodemographic disparities in COVID-19 vaccine uptake and vaccination intent in Canada
Bibliographic record
Abstract
Introduction: This study's objective was to examine sociodemographic disparities in COVID-19 vaccine uptake and vaccination intent in the Canadian provinces by identifying factors associated with vaccine uptake in seniors prioritized for vaccination at the time of the survey and vaccination intent in all adults. Data and methods: A cross-sectional survey of Canadian adults was conducted in all provinces from mid-April to mid-May 2021. In addition to sociodemographic characteristics, respondents (n=10,678) provided information on their COVID-19 vaccination status or their intent to get vaccinated. Logistic regression models were fitted using sociodemographic factors as explanatory variables and vaccination status (unvaccinated vs at least one dose) or vaccination intent (unlikely versus likely or already vaccinated) as outcomes. To account for vaccine prioritization groups, multiple regression models were adjusted for province of residence, age, Indigenous identity and health care worker status. Results: Seniors with a lower household income (less than $60,000) and those living in smaller communities (fewer than 100,000 inhabitants) had higher odds of being unvaccinated. Among Canadian adults, the odds of being unlikely to get vaccinated were higher for males (adjusted odds ratio [AOR] 1.3), individuals younger than 60 (AOR between 3.3 and 5.1), non-health care workers (AOR 3.3), those with less than a high school education (AOR 3.4) or a household income of less than $30,000 (AOR 2.7) and individuals who do not identify as South Asian, Chinese, Black, Filipino, Arab, Latin American, Southeast Asian, West Asian, Korean or Japanese (AOR 1.7). Interpretation: COVID-19 vaccine uptake (80%) and vaccination intent (95%) were high among Canadians; however, relative disparities were observed among specific groups. Continued efforts targeted toward these groups are essential in reducing potential inequity in access or service provision.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".