Factors That Determine Utilization of the Canadian School-Based Human Papilloma Virus Vaccine Programs
Bibliographic record
Abstract
Human Papilloma Virus (HPV) is the most common sexually transmitted infection in the world and is the known causative agent of many HPV-associated cancers in both males and females. HPV vaccination rates in Canada are significantly lower than other developed countries and this finding is poorly understood as Canadian adolescents have access to free-of-charge HPV vaccines through school-based vaccination programs. This quantitative descriptive study used an online survey to collect data from 992 eligible respondents. This study identified predisposing, enabling, and need factors characterized by the Andersen behavioral model of health services use which facilitate or impede the use of this HPV vaccine program. The study aimed to understand the relationship between health services utilization factors that were associated with parents’ immunizer status (HPV immunizer or HPV nonimmunizer) and what factors were predictive of a parent being an HPV immunizer. Results from descriptive and inferential statistical analysis demonstrated that there was an association between key predisposing, enabling and need factors. Having a primary care health provider was highly predictive of parents being an HPV-immunizer (74; 95% CI 23.6 - 232.4) This is aligned with findings in the literature which indicated that parents are more likely to accept immunizations for their children when directly supported by a primary health care professional. This study identified prioritized opportunities to improve the uptake of the HPV vaccine in the Canadian school-based public vaccine programs. Increasing HPV vaccine uptake may impact social change by improving health outcomes and decreasing the burden of illness of HPV-related infections and cancers.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".