Human papillomavirus vaccine uptake among 9‐ to 17‐year‐old girls
Bibliographic record
Abstract
BACKGROUND: Since 2006, the human papillomavirus (HPV) vaccine has been routinely recommended for preadolescent and adolescent girls in the United States. Depending on uptake patterns, HPV vaccine could reduce existing disparities in cervical cancer. METHODS: HPV vaccination status and reasons for not vaccinating were assessed using data from the 2008 National Health Interview Survey. Households with a girl aged 9-17 years were included (N = 2205). Sociodemographic factors and health behaviors associated with vaccine uptake were assessed using multivariate logistic regression. RESULTS: Overall, 2.8% of 9- to 10-year-olds, 14.7% of 11- to 12-year-olds, and 25.4% of 13- to 17-year-olds received at least 1 dose of HPV vaccine; 5.5% of 11- to 12-year-olds and 10.7% of 13- to 17-year-olds received all 3 doses. Factors associated with higher uptake in multivariate analysis included less than high school parental education, well-child check and influenza shot in the past year, and parental familiarity with HPV vaccine. Parents' primary reasons for not vaccinating were beliefs that their daughters did not need vaccination, that their daughters were not sexually active, or had insufficient vaccine knowledge. More parents with private insurance (58.0%) than public (39.8%) or no insurance (39.5%) would pay $360-$500 to vaccinate their daughters. CONCLUSIONS: Less than one quarter of girls aged 9-17 years had initiated HPV vaccination by the end of 2008. Efforts to increase HPV uptake should focus on girls in the target age group, encourage providers to educate parents, and promote access to reduced-cost vaccines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".