O10.3 Predictors of Women’s Intentions to Receive Cervical Cancer Screening with Primary HPV Testing
Bibliographic record
Abstract
Background Primary HPV testing for cervical cancer screening (HPV-CCS) could result in significant CCS programme changes including extended screening intervals, later age to start of screening and use of a test for a sexually acquired infection. We examine the predictors of women’s intentions to undergo HPV-CCS compared to screening with Pap smears in different screening scenarios. Methods Participants from a Canadian trial of primary HPV CCS completed a survey which determined women’s intentions to attend CCS in three different models - (a): HPV-CCS conducted annually; (b): HPV-CCS conducted every 4 years; and (c): HPV-CCS conducted every 4 years and starting after age 25. Demographic and health data were assessed, and scales for attitudes about HPV testing (AT), perceived behavioural control (PBC) and direct and indirect subjective norms (SND, SNI) were created. Three logistic regression models were created, to determine predictors of women’s intentions to attend HPV-CCS in each scenario. Results 981 of 2016 emailed surveys were completed. Eighty four percent of women intend to be screened with HPV, which decreased to 54.2% with an extended screening interval, and 51.4% with a delayed start of age 25. Predictors of intention to undergo HPV-CCS screening in Model A were attitudes (OR 1.22; 95% CI 1.15, 1.30), SNI (OR 1.02; 95% CI 1.01, 1.03) and PBC (OR 1.16; 95% CI 1.10; 1.22). In Model B, predictors were attitudes (OR 1.32; 95% CI 1.28; 1.37), and in Model C, predictors were attitudes (OR 1.26; 95% CI 1.23; 1.30), education (OR 0.59; 95% CI 0.37; 0.93), and PBC (OR 1.06; 95% CI 1.02; 1.10). Discussion Women’s intentions to be screened for cervical cancer with HPV decreases substantially with an extended screening interval and delayed screening start. CCS programmes considering primary HPV screening must ensure robust planning to mitigate any negative impact on screening attendance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".