Coverage and correlates of COVID-19 vaccination among children aged 5-11 years in Alberta, Canada
Bibliographic record
Abstract
Abstract Background and Objectives In Alberta, Canada, the COVID-19 vaccination program for children aged 5-11 years opened on November 26, 2021. Our objectives were to determine the cumulative vaccine coverage, stratified by age, during the first seven months of vaccine availability, and investigate factors associated with vaccine uptake. Methods This retrospective cohort study used population-based administrative health data to assess COVID-19 vaccination coverage among children aged 5-11 years in Alberta, Canada. We determined cumulative vaccine coverage since the time of vaccine availability and used a modified Poisson regression to evaluate factors associated with vaccine uptake. Results Of 377,753 eligible children, 43.8 % (n=165,429) received one or more doses of COVID-19 vaccine during the study period (11.2% received only one dose, while 32.5 % received 2 doses). Almost 90% of initial doses were received within the first two months of vaccine availability. Of those eligible for a second dose, only 75.1% (n=122,973) received it during the study time period. We found a step-wise relationship between increasing child age and higher vaccine coverage. Other factors associated with higher vaccine coverage included living in a neighborhood with higher income, in a more densely populated area, and in certain geographic health zones. Registration in a private school was associated with lower vaccine coverage. Conclusions Messaging around COVID-19 vaccine safety and need should be tailored to child age, rather than uniform across the 5-11 year age range. Opportunities for targeted vaccination interventions should be considered.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".