Estimates and determinants of HPV non-vaccination in 14-year-old Canadians: Results from the childhood national immunization coverage survey, 2019
Bibliographic record
Abstract
Human papillomavirus (HPV) infections, the most common sexually transmitted infections, are associated with various health outcomes including anogenital warts and cancers. Despite significant investments in HPV vaccination programs, ensuring adequate vaccination coverage for adolescents remains a challenge in Canada. This analysis used data collected through the 2019 Childhood National Immunization Coverage Survey (CNICS) to determine national estimates of HPV non-vaccination and investigate determinants of HPV non-vaccination for adolescents aged 14-years old in Canada, both overall and stratified by gender. The primary outcome of interest was HPV vaccination status, categorized as vaccinated with at least one dose or unvaccinated. Simple and multiple logistic regression models were used to investigate determinants of HPV non-vaccination. In 2019, an estimated 19.8% of the 14-year-olds in Canada were unvaccinated for the HPV vaccine, with males having higher non-vaccination rates than females (27.0% compared to 12.9%). In the unstratified analysis, factors associated with HPV non-vaccination for 14-year-olds were gender and region of residence. These factors differed by gender - for males, region of residence and respondent's age were significant factors, whereas for females, total household income was a significant factor. These results could help public health officials and policymakers develop and implement tailored interventions to enhance the delivery of HPV vaccination programs for male and female adolescents. By targeting populations that are under-vaccinated, vaccine uptake could be better facilitated to help reduce inequalities in access to the HPV vaccine, which could also potentially reduce disparities in HPV-related health outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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".