Abstract B35: Working to close the gap: Identifying predictors of HPV vaccine initiation among young African American women
Bibliographic record
Abstract
Abstract Background: HPV (human papillomavirus) vaccines are recent innovations that have provided a new avenue for cervical cancer prevention. The role of HPV vaccines among groups of women who experience excess cervical cancer incidence and mortality requires further study. The purpose of this study was to assess health beliefs associated with HPV vaccine initiation among young African American (AA) women. Methods: Three hundred sixty-three African American female college students aged 18-26 were recruited from three Historically Black Colleges/Universities in the southeastern United States. Participants were asked to complete a self-administered, anonymous survey to assess knowledge and beliefs related to HPV and the HPV vaccine. Results: One-quarter (24%) of participants initiated the HPV vaccine. Women who initiated the HPV vaccine had significantly higher HPV knowledge (p=0.01), lower perceived barriers to vaccination (p<0.01), and were younger (p<0.01) compared to women who had not initiated vaccination. Factors significantly associated with HPV vaccine initiation included: HPV knowledge (OR=1.22), perceived severity of HPV health outcomes, (OR=0.48), perceived barriers to vaccination, (OR=0.49), cues to action (OR=1.94), and age (OR=0.68). The model explained 19% of the variance in HPV vaccine initiation. Conclusions: Study findings can be used to inform the development of targeted health education and promotion HPV vaccine programs for AA college-aged women to prevent continued disparities in cervical cancer incidence and mortality. Addressing health beliefs will be central to efforts to promote widespread vaccine uptake and future reduction in disease. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):B35.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".