Improving Human Papillomavirus (HPV) Vaccine Uptake in Canada: A Call for Equity and Inclusivity
Bibliographic record
Abstract
Human Papillomavirus (HPV) is the most common sexually transmitted infection worldwide. Specifically, HPV is responsible for a large proportion of anal, cervical, vaginal, vulvar, penile and oropharyngeal cancers, highlighting the importance of optimizing the prevention of this public health issue. To date, vaccination is the most effective method for preventing HPV-related infections and associated diseases; however, vaccine uptake remains well below national targets. In Canada, gender-neutral HPV vaccination is recommended for all individuals between nine and 26 years, but can also be administered to adults until the age of 45. Despite widespread adoption of publicly-funded school-based vaccination programs, some populations report disproportionately lower rates of HPV vaccine uptake, including young adults, transgender peoples and men who have sex with men (MSM), rendering them vulnerable to morbidity and mortality. Addressing HPV-related disparities requires a coordinated, multi-level call to action involving collaboration between academic and community partners to normalize inclusive, gender-neutral vaccination. This paper explores opportunities for optimizing HPV vaccine uptake in Canada by emphasizing the importance of healthcare provider recommendation, improved access to community-based vaccination services, and representation of diverse populations (e.g., young adults, transgender peoples, MSM) in the development and delivery of vaccine communication/messaging. The time is now to normalize inclusive HPV vaccination in order to mitigate the persistence of vaccine-related disparities and strive toward global initiatives of health equity.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".