Willingness to Self-Collect a Sample for HPV-Based Cervical Cancer Screening in a Well-Screened Cohort: HPV FOCAL Survey Results
Bibliographic record
Abstract
Self-collection may provide an opportunity for innovation within population-based human papillomavirus (HPV) cervical cancer screening programs by providing an alternative form of engagement for all individuals. The primary objective was to determine willingness to self-collect a vaginal sample for primary HPV screening and factors that impact willingness in individuals who participated in the Human Papillomavirus For Cervical Cancer (HPV FOCAL) screening trial, a large randomized controlled cervical screening trial. A cross-sectional online survey was distributed between 2017 and 2018 to 13,176 eligible participants exiting the FOCAL trial. Bivariate and multivariable logistic regression assessed factors that influence willingness to self-collect on 4945 respondents. Overall, 52.1% of respondents indicated willingness to self-collect an HPV sample. In multivariable analysis, the odds of willingness to self-collect were significantly higher in participants who agreed that screening with an HPV test instead of a Pap test was acceptable to them (odds ratio (OR): 1.45 (95% confidence interval (CI): 1.15, 1.82), those who indicated that collecting their own HPV sample was acceptable to them (p < 0.001), and those with higher educational ascertainment (OR: 1.31, 95% CI: 1.12, 1.54). The findings offer insight into the intentions to self-collect in those already engaged in screening, and can inform cervical cancer screening programs interested in offering alternative approaches to HPV-based 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".