Beliefs, behaviors and HPV vaccine: Correcting the myths and the misinformation
Bibliographic record
Abstract
OBJECTIVE: Human papillomavirus (HPV) vaccine uptake in many countries has been sub-optimal. We examine several issues associated with non-vaccination that have received particular attention, including fears about sexual risk compensation, concerns about vaccine safety, inadequate vaccination recommendations by health care providers (HCPs), and distrust due to the perceived "newness" of HPV vaccines. METHODS: Selective review of behavioral and social science literature on HPV vaccine attitudes and uptake. RESULTS: There is no evidence of post-vaccination sexual risk compensation, HPV vaccines are quite safe, and they can no longer be considered "new". Nonetheless, research findings point to these issues and, most importantly, to the failure of HCPs to adequately recommend HPV vaccine as major drivers of non-vaccination. CONCLUSION: Most fears related to HPV vaccine are more related to myth than reality. In the absence of major health policy initiatives, such as those implemented in Canada, the U.K., and Australia, a multi-level, multi-faceted approach will be required to achieve high rates of HPV vaccination. It will be essential to focus on the education of HCPs regarding indications for HPV vaccination and approaches to communicating most effectively with parents and patients about the safety and benefits of vaccination and the risks associated with non-vaccination.
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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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".