COVID-19 vaccine confidence, concerns, and uptake in children aged 5 and older in Calgary, Alberta: a longitudinal cohort study
Bibliographic record
Abstract
Objectives: Beginning early in the pandemic, there was a worldwide effort to develop effective vaccines against the SARS-CoV-2 virus. Before and after the approval and implementation of vaccines, there were concerns about their need as well as their safety and rapid development. We explored child demographic characteristics and parental concerns to identify factors associated with the decision to vaccinate. Methods: A cohort of 1035 children from Calgary was assembled in 2020 to participate in 5 visits every 6 months for survey completion and blood sampling for SARS-CoV-2 antibodies. Visits 1 to 2 occurred before approval of vaccines for children; Visits 3 to 5 occurred after vaccine approval for different age groups. We described vaccine concerns and utilized logistic regression to examine factors associated with the decision to vaccinate in children ≥5 years of age. Results: Children ≥12 years of age, of non-white or non-black ethnicity, and who had received previous influenza vaccines had higher odds of being vaccinated against SARS-CoV-2. Children with previous SARS-CoV-2 infection had lower odds of being vaccinated. The most common concerns in early 2021 were about vaccine safety. By summer 2022, the most common concern was a belief that vaccines were not necessary. Through the study 88% of children were vaccinated. Conclusions: Age, ethnicity, previous infections, and vaccine attitudes were associated with parental decision to vaccinate against SARS-CoV-2. For children who remained unvaccinated, parents continued to have safety concerns and questioned the necessity of the vaccine. Complacency about the need for vaccination may be more challenging to address and overcome than concerns about safety alone.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".