Factors associated with youth vaccine acceptance during the COVID-19 pandemic: Coordinated analyses across 5 Canadian datasets
Bibliographic record
Abstract
Vaccines are essential for preventing infectious diseases, yet vaccine hesitancy—particularly among youth—remains a growing concern. This study investigated factors influencing COVID-19 vaccine acceptance among Canadian youth (aged 12–29 years) across three pandemic stages using data from five rapid-response surveys. Multivariable logistic regression analyses identified sociodemographic, pandemic-related impacts, and mental health factors associated with vaccine acceptance. Results showed increasing vaccine acceptance over time across samples (i.e., Stage 1: 52.3 %–65.4 %; Stage 2: 73.8 %–83.2 %; and, Stage 3: 85.3 %–96.0 %). Although findings varied across samples, overall, parental education (significant adjusted odds ratios [aOR] range across samples and Stages = 0.16 to 2.07), living area (i.e., rural/urban; aORs range = 2.07 to 2.18), and COVID-19 stress (aOR range = 1.06 to 2.34) emerged as consistent factors across time. Other factors, such as being older (Stage 1 aOR = 1.15 to 3.21; Stage 3 aOR = 0.58), White (Stage 1 aOR = 1.55 to 1.69; Stage 2 aOR = 1.48), female (Stage 1 a OR = 0.60 to 0.72) or having a family member diagnosed with COVID-19 (Stage 1 aOR = 1.89; Stage 2 aOR = 0.55; Stage 3 aOR = 0.52) appeared as potential context-specific factors related to vaccine acceptance. Mental health had limited influence. These findings underscore the need for targeted vaccination campaigns addressing stable and dynamic sociodemographic and stress-related factors among youth. • Vaccine acceptance ranged from 52.3 % to 96.0 % across three stages of the pandemic. • Parental education, living area, and COVID-19 stress were key factors in vaccine acceptance. • Anxiety and depressive symptoms generally did not affect vaccine acceptance. • Targeted strategies are needed for youth with lower parental education, those living in rural areas, and those experiencing COVID-19-related distress.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".