Commentary and methodological insights: Reaching girls/women, boys/men and vulnerable groups to maximise uptake for the Human papillomavirus vaccine
Bibliographic record
Abstract
The human papillomavirus (HPV) vaccine has been shown to be an effective cancer-prevention vaccine against oncogenic types of the HPV virus implicated in cervical, anogenital, and oropharyngeal cancers. Since Covid-19, there are global suboptimal uptake rates for the HPV vaccine. In high-income countries, there are persistently lower uptake rates among boys/men and vulnerable groups despite many countries now offering the HPV vaccine to both girls and boys in gender-neutral vaccine campaigns. It is important to understand the nuances with vaccine hesitancy and qualitative research approaches can be valuable to understand rich, contextual understandings in public health communication among hard-to-reach groups. This commentary draws insights from previous literature and our own research including two studies submitted to this Special Edition on Vaccine Communication. We consider the cultural context, gender and specific hard-to-reach groups in Scotland including those with an intellectual disability, sexual minorities, and ethnically diverse groups to draw some insights. Such groups may experience taboos and stigma in various guises. It is important that public health communication in given contexts is gender-inclusive and can incorporate messages that reach vulnerable groups. Cancer prevention communication delivered by trusted healthcare providers and community leaders are important strategies to deliver trusted messages.
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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 | 0.108 | 0.450 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.025 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 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".