A Review of Ethical and Legal Aspects of Gender-Neutral Human Papillomavirus Vaccination
Bibliographic record
Abstract
While launching a campaign to eliminate cervical cancer, the World Health Organization called to halt human papillomavirus (HPV) gender-neutral vaccination (GNV) because of limited vaccine supply, raising ethical and legal questions about female-only vaccination versus GNV. We identified ethical and legal aspects of HPV GNV by searching MEDLINE for records up to February 19, 2021. We also provided an overview of HPV vaccines, the evolution of HPV vaccine recommendations in North America, and a timeline of male HPV vaccination introduction by searching PubMed, Google, and government websites. Four HPV vaccines are available: Cervarix, Gardasil, Gardasil9, and Cecolin. Vaccine recommendations in North America evolved from female only to eventually include males. Following the FDA's approval of the first HPV vaccine for males (2009), 35 countries began vaccinating males (2011-2020). On the basis of 59 eligible records out of 652, we identified the following constructs: lower male awareness of HPV and vaccination (n = 13), limited economic resources (n = 5), shared social responsibility (n = 18), unprotected groups from female-only HPV vaccination (n = 10), limited screening for HPV-associated noncervical cancers (n = 6), consideration of ethical principles (n = 17), and HPV vaccine mandates (n = 5). Ethical and legal aspects must be considered when recommending vaccination for females only or GNV.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".