Exploring Parent-Driven Determinants of COVID-19 Vaccination in Indigenous Children: Insights from a National Survey
Bibliographic record
Abstract
Background: Globally and in Canada, Indigenous populations have faced heightened vulnerability during pandemics, with historical inequities exacerbated by multigenerational colonial policies. This study aimed to identify parental factors influencing COVID-19 vaccination among Indigenous children in Canada. Methods: Data from a nationally representative, cross-sectional survey of parents/guardians with children under 18 years of age were analyzed. The study focused on Indigenous children, examining vaccine uptake, parental hesitancy, and related sociodemographic factors. Multivariable logistic regression models were employed to identify key predictors of COVID-19 vaccination. Results: COVID-19 vaccine coverage among Indigenous children was 61.8%, with higher uptake among Inuit (74.4%) children compared to Métis (61.2%) and First Nations (59.6%) children. Nearly half of Indigenous parents (53.4%) expressed hesitancy, primarily due to perceived concerns about insufficient research on the vaccine in children. Higher vaccine uptake was associated with parental education, adherence to routine vaccinations, and urban residence. Conversely, parental hesitancy, particularly related to medical concerns, significantly decreased the likelihood of vaccine uptake. Conclusions: The study highlights the complexity of vaccine hesitancy among Indigenous parents. Targeted interventions, including culturally adapted educational initiatives, community engagement, and healthcare provider advocacy, are essential to improve vaccine uptake.
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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 | 0.000 | 0.001 |
| 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".