The feminization of HPV: How science, politics, economics and gender norms shaped U.S. HPV vaccine implementation
Bibliographic record
Abstract
Human papillomavirus (HPV) can cause a number of anogenital cancers (i.e., cervical, penile, anal, vaginal, vulvar) and genital warts. A decade ago, the HPV vaccine was approved, and has been shown to be a public health achievement that can reduce the morbidity and mortality for HPV-associated diseases. Yet, the mistaken over-identification of HPV as a female-specific disease has resulted in the feminization of HPV and HPV vaccines. In this critical review, we trace the evolution of the intersection of science, politics, economics and gender norms during the original HPV vaccine approval, marketing era, and implementation. Given the focus on cervical cancer screening, women were identified as bearing the burden of HPV infection and its related illnesses, and the group responsible for prevention. We also describe the consequences of the feminization of HPV, which has resulted primarily in reduced protection from HPV-related illnesses for males. We propose a multilevel approach to normalizing HPV vaccines as an important aspect of overall health for both genders. This process must engage multiple stakeholders, including providers, parents, patients, professional organizations, public health agencies, policymakers, researchers, and community-based organizations.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".