Knowledge, attitudes, and practices towards Human Papilloma Virus and uptake of HPV vaccine: A protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Despite a high burden of Human Papilloma Virus (HPV)-associated diseases, HPV vaccine uptake is disparate globally. The objective of this systematic review is to summarize the existing evidence on knowledge, attitudes, and practices (KAP) regarding HPV and the uptake of the HPV vaccine. METHODS AND ANALYSIS: We will conduct a systematic review of observational studies that report data on HPV KAP and vaccine uptake among people aged 16 and above. We will search MEDLINE, CINAHL, Embase, Emcare, Web of Science, Cochrane Library, Global Health, and PsycInfo. We will conduct screening, data extraction, and assessment of the methodological quality of the included studies in duplicate. A random-effects model will be used to pool data. Subgroup analysis will be done for age younger adults (≤ 26 years old) and older adults (> 26 years old), sex (men and women), income level (as per World Bank), and WHO region. This systematic review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The PROSPERO registration number for the review is CRD42024532230. ETHICS AND DISSEMINATION: Ethical approval is not necessary as this study will review secondary published data. Our findings will be disseminated as part of a doctoral thesis and through peer-reviewed journal publications and conferences.
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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 | 0.091 | 0.098 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.010 |
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".