Human papillomavirus vaccination acceptance and hesitancy in South Africa: Research and policy agenda
Bibliographic record
Abstract
Cervical cancer is responsible for one-quarter of a million deaths per year worldwide. In South Africa (SA), cervical cancer is the leading cause of cancer deaths among women aged 15 - 44 years. Human papillomavirus (HPV) vaccines provide a safe and highly effective means to reduce the burden of cervical cancer. The World Health Organization initiated a plan for the elimination of cervical cancer; the programme's success relies on the introduction and high uptake of HPV vaccines globally. SA introduced a school-based HPV vaccination programme in 2014, but uptake is not as high as expected. Suboptimal HPV vaccination coverage may result from various factors, including vaccine hesitancy. Vaccine-hesitant parents may delay or refuse HPV vaccination for their daughters. Tailored interventions are needed to address this. However, knowledge regarding vaccine hesitancy and policies to address this hesitancy in SA are currently limited. While SA has taken commendable steps in cervical cancer prevention by implementing and financing the HPV vaccination programme, it is imperative that there are clear policies in place to help strengthen the programme. These policies need to clarify areas of uncertainty that may lead to mistrust, and pre-empt factors that will cause hesitancy. Equally important is that local research should be conducted to better understand HPV vaccination hesitancy and other determinants of uptake to further inform and shape national policies.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".