Global challenges of implementing human papillomavirus vaccines
Bibliographic record
Abstract
Human Papillomavirus vaccines are widely hailed as a sweeping pharmaceutical innovation for the universal benefit of all women. The implementation of the vaccines, however, is far from universal or equitable. Socio-economically marginalized women in emerging and developing, and many advanced economies alike, suffer a disproportionately large burden of cervical cancer. Despite the marketing of Human Papillomavirus vaccines as the solution to cervical cancer, the market authorization (licensing) of the vaccines has not translated into universal equitable access. Vaccine implementation for vulnerable girls and women faces multiple barriers that include high vaccine costs, inadequate delivery infrastructure, and lack of community engagement to generate awareness about cervical cancer and early screening tools. For Human Papillomavirus vaccines to work as a public health solution, the quality-assured delivery of cheaper vaccines must be integrated with strengthened capacity for community-based health education and screening.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".