Canadian parents’ perceptions of COVID-19 vaccination and intention to vaccinate their children: Results from a cross-sectional national survey
Bibliographic record
Abstract
BACKGROUND: Vaccinating children (≤17 years old) is important for controlling the COVID-19 pandemic. As parents are primary decision makers for their children, we aimed to assess parents' perceptions and intentions regarding COVID-19 vaccination for their children, including for some underserved populations (e.g., newcomers, Indigenous peoples, and visible minority groups). METHODS: We conducted a cross-sectional national survey of Canadian parents in December 2020, just as COVID-19 vaccines were approved for adults, to assess intention to vaccinate their children (aged 0-17 years) against COVID-19, perceptions of COVID-19 disease and vaccines, previous uptake of influenza and routine vaccines, and sociodemographic characteristics. Binomial logistic regression was used to assess the association between parents' lack of COVID-19 vaccination intention for their children and various independent variables. RESULTS: Sixty-three percent of parents (1074/1702) intended to vaccinate their children against COVID-19. Those employed part-time (compared to full-time) had lower intention to vaccinate their children (aOR = 1.73, 95% CI: 1.06-2.84), while those who spoke languages other than English, French, or Indigenous languages were less likely to have low intention (aOR = 0.55, 95% CI: 0.32-0.92). Low vaccination intention was also associated with children not receiving influenza vaccine pre-pandemic (aOR = 1.51, 95% CI: 1.04-2.21), parents having low intention to vaccinate themselves against COVID-19 (aOR = 9.22, 95% CI: 6.43-13.34), believing COVID-19 vaccination is unnecessary (aOR = 2.59, 95% CI: 1.72-3.91) or unsafe (aOR = 4.21, 95% CI: 2.96-5.99), and opposing COVID-19 vaccine use in children without prior testing (aOR = 3.09, 95% CI: 1.87-5.24). INTERPRETATION: Parents' COVID-19 vaccination intentions for their children are better predicted by previous decisions regarding influenza vaccination than routine childhood vaccines, and other perceptions of COVID-19 vaccine-related factors. Public communication should highlight the safety and necessity of COVID-19 vaccination in children to support a return to normal activities. Further research should assess actual COVID-19 vaccination uptake in children, particularly for underserved populations.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".