Effectiveness of an educational intervention to increase human papillomavirus knowledge in high-risk populations: A systematic review
Bibliographic record
Abstract
Background: HPV vaccine has been described as a very efficacious and safe vaccine, making it highly acceptable for the prevention of potential cervical cancer. However, rejection of vaccine use is on the rise. This paper aims to showcase the benefit of educational intervention as an avenue to increase the rate of vaccination among the high-risk population. Hypothesis: Parental education on HPV vaccine will improve the rate of vaccination acceptance.Methods: Databases including PubMed, Google Scholar, Science-Direct and Web of Science were searched for studies pertaining to the effects of educational intervention on HPV vaccine receipt. Results: A total of 11 articles were included in this review. Eight studies targeted high-risk minors of recommended ages for the HPV vaccine. The parents of these minors were then assessed pre- and post-intervention to study the effect of educational intervention on intent to vaccinate against HPV. Three studies focused on the effect of educational intervention targeted to adolescents and young adults ages 18–22 on intent to vaccinate against HPV. Results compared the intent to vaccinate in groups exposed to the educational intervention versus groups without intervention. Percentage increase in the intent to vaccinate was reported for all groups exposed to educational intervention versus groups without intervention. Conclusion: The increase in the percentage of participants with intent to vaccinate reflects the importance of individual knowledge. Future studies should focus on methods that improve dialogue and acceptance with groups of different ethnicity and cultures.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".