Bibliographic record
Abstract
Human papillomavirus (HPV) vaccination prevents cervical, head and neck, and anogenital cancers. However, global HPV vaccine coverage falls short of global targets and has seen unexpected and dramatic declines in some countries. This paper synthesizes the impact of HPV on the global burden of cancer and the potential benefit of HPV vaccination. Approximately 5% of the world's cancers are specifically attributed to HPV. While the greatest global burden of HPV is cervical cancers in low- and middle-income countries, HPV-associated head and neck cancers are increasing in high-income countries and have surpassed cervical cancer as the primary HPV-associated cancer in some countries. Therefore, it is also critical to improve gender-neutral HPV vaccination. Understanding the modifiable drivers of vaccine acceptance and uptake is important for increasing HPV vaccination. The Behavioural and Social Drivers of Vaccination framework is broadly applied to identify key factors associated with HPV vaccination including domains concerning practical issues, motivation, social processes, and thinking and feeling. Among the behavioural strategies available to reduce the incidence and mortality of cancer, increasing HPV vaccination stands out as having unrealized potential to prevent disease, financial cost, and psychological distress. An understanding of the shifting burden of HPV and the factors associated with vaccination can be leveraged to regularly measure these factors, develop interventions to promote vaccine uptake, and improve global HPV vaccine coverage. Future research in diverse contexts is necessary to investigate the barriers and facilitators of global HPV vaccination.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".