Improving HPV Vaccination Rates in a Pediatric Group: A Pilot Project
Bibliographic record
Abstract
Background: Human Papillomavirus (HPV) is the most common sexually transmitted infection in the United States. HPV vaccines are both safe and effective, providing long-lasting protection. Some organizations recommend the HPV vaccine should be initiated at age 9 years. Specific Aims: The purpose of this pilot project was to improve HPV vaccination rates by 10% in patients ages 9-10 years at a large pediatric group. This purpose was accomplished through pediatric healthcare provider (HCP) education. Methods: A quantitative and open-response questionnaire was used to determine HCPs’ confidence levels regarding the HPV vaccine at baseline. HCPs attended one evidence-based educational session. HPV vaccination rates from 4th quarter 2022 were compared to 4th quarter 2023. Results: The number of HPV vaccines delivered to 9-10-year-old patients increased by 149 (61%) doses in 1 year. The total HPV vaccination, including all doses and among all patients, increased by 811 in 1 year. HCP confidence in HPV vaccine increases as children age. Factors influencing delay of HPV vaccination included parental hesitations and clinic expectations. Discussion: There are multiple benefits to initiating HPV vaccination at the age of 9 years and HCP-focused education can improve vaccination rates in this age group. HCP education increases HPV vaccine confidence and, as a result, vaccination rates improve. Conclusion: Pediatric Nurse Practitioners can and should conduct quality improvement projects to improve patients’ health by focusing on HPV vaccination rates, thus dramatically transforming HPV vaccine uptake and preventing HPV-related cancers for years to come.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".