Factors associated with COVID-19 non-vaccination among children and adolescents with chronic health conditions in Canada: A national cross-sectional study
Bibliographic record
Abstract
COVID-19 vaccine rollout has prioritized high risk populations, including children with chronic health conditions (CHC), who are at greater risk of severe illness and hospitalization if infected. This study aims to identify the sociodemographic factors associated with COVID-19 non-vaccination among Canadian children with at least one CHC, and parental reasons for non-vaccination. The Childhood COVID-19 Immunization Coverage Survey is a nationally representative, cross-sectional survey of parents with children younger than 18 years old in Canada. Data was collected from April to July 2022 on COVID-19 immunization coverage and parental intentions to vaccinate their children. This study featured parents with children ages 5 to 17 years old who had at least one CHC. Unadjusted and adjusted weighted logistic regression models were built to explore factors of non-vaccination within this cohort. Of the 882 parents with children who have at least one CHC, 138 (16 %) reported that their child was unvaccinated against COVID-19. Children who were a visible minority (aOR: 2.66, 99 % CI: 2.48, 2.85) or who did not have asthma (aOR: 1.48, 99 % CI: 1.42–1.56) had greater odds of being unvaccinated, whereas adolescents 12–17 years old had lower odds (aOR: 0.10, 99 % CI: 0.09–0.11). Unvaccinated parents who were hesitant or refused to vaccinate their child cited vaccine safety (67.2 %), inadequate research on COVID-19 vaccines (57.7 %), and vaccine effectiveness (55.9 %) as their top 3 concerns on COVID-19 vaccination. Study findings may help inform policies and programs designed to address parental vaccine hesitancy and increase vaccination uptake especially among children of visible minority, low SES and/or children who do not have asthma, but have other CHCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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, 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".