Recent changes in cervical cancer screening guidelines: U.S. women's willingness for HPV testing instead of Pap testing
Bibliographic record
Abstract
Abstract Cervical cancer screening guidelines in the United States were revised in 2018 to include the option of primary human papillomavirus (HPV) testing. The transition to this screening method may face difficulties as Pap testing has been the primary screening modality in the United States. The objective of this study is to assess information, motivation, and behavioral skills associated with willingness to receive an HPV test instead of a Pap test among women. The sample included U.S. 812 women, ages 30 to 65 years. Participants completed an online survey in 2018. The Information, Motivation, and Behavioral Skills (IMB) model was used to measure predictors of willingness for HPV testing. The outcome variables were willingness to receive the HPV test instead of the Pap test, with and without time interval details. Logistic regression modeling was used with SAS 9.4. Over half of the sample (55%) were willing to receive the HPV test. For the information domain, HPV knowledge was significantly associated with willingness for HPV testing (OR = 1.08, 95%CI 1.04–1.13). Significant motivating factors included: positive attitudes, social norms, perceived benefits, worry about cervical cancer, and worry about abnormal HPV tests. For behavioral skills, women were significantly more willing to get the HPV test if a provider recommended it (OR = 2.43, 95%CI 1.53–3.87) and currently up-to-date on cervical cancer screening guidelines (OR = 1.52, 95%CI 1.52–2.26). Addressing barriers and facilitators to willingness to transition to primary HPV testing over Pap testing is needed as the United States has updated guidelines for cervical cancer screening.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".