Routine childhood vaccination among ethnocultural groups in Canada during the COVID-19 pandemic: A national cross-sectional study
Bibliographic record
Abstract
Some ethnocultural groups in Canada experience low routine childhood vaccination, with social locations and discriminations contributing to inequities. This study aimed to characterize routine childhood vaccination in the context of the COVID-19 pandemic, including the influence of discriminatory experiences when accessing health services. We conducted a cross-sectional national survey to assess parents' acceptance of routine vaccines for their children ≤ 17 years in Oct/Nov 2021. Descriptive statistics were used to explore differences among ethnocultural groups and logistic regression to assess associations with parents' low acceptance. Of 2531 parents, 21.8 % self-identified as Racialized minorities, 7.7 % Indigenous, 23.3 % newcomers, 10.0 % spoke minority languages most often, and 69.6 % belonged to a reference group who did not report these characteristics. Statistically significant findings included 36.6 % of Indigenous parents reporting that the pandemic made them realize that routine vaccines were more important compared to 16.7 % of newcomers. Discrimination/racism when accessing health services was most often experienced by Indigenous (27.8 %) and Racialized minorities (20.2 %), compared to the reference group (4.8 %). Racialized minorities were more likely to report low acceptance of routine vaccination (aOR = 2.19, 95 % CI: 1.18-4.05), and younger parents and those with only preschool-aged children were less likely to have low acceptance (aOR = 0.59, 95 % CI: 0.37-0.94; aOR = 0.53, 95 % CI: 0.36, 0.79). Low acceptance was associated with everyday stress preventing vaccination (aOR = 2.18, 95 % CI: 1.41-3.38). Public health decision-makers should ensure equitable access to routine childhood vaccination that targets the inclusion of ethnocultural groups, who may experience disproportionate barriers and low acceptance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".