HPV Sampling Options for Cervical Cancer Screening: Preferences of Urban-Dwelling Canadians in a Changing Paradigm
Bibliographic record
Abstract
Introduction: Of women in Canada diagnosed with invasive cervical cancer, 50% have not been screened according to guidelines. Interventions involving self-collected samples for human papillomavirus (hpv) screening could be an avenue to increase uptake. To guide the development of cervical cancer screening interventions, we assessed (1) preferred sample collection options, (2) sampling preferences according to previous screening behaviours, and (3) preference for self-sampling among women not screened according to guidelines, as a function of their reasons for not being screened. Methods: Data were collected in an online survey (Montreal, Quebec; 2016) and included information from female participants between the ages of 21 and 65 years who had not undergone hysterectomy and who had provided answers to survey questions about screening history, screening interval, and screening preferences (n = 526, weighted n = 574,392). Results: In weighted analyses, 68% of all women surveyed and 82% of women not recently screened preferred screening by self-sampling. Among women born outside of Canada, the United States, or Europe, preference ranged from 47% to 60%. Nearly all women (95%–100%) who reported fear or embarrassment, dislike of undergoing a Pap test, or lack of time or geography-related availability of screening as one of their reasons for not being screened stated a preference for undergoing screening by self-sampling. Conclusions: The results demonstrate a strong preference for self-sampling among never-screened and not-recently-screened women, and provides initial evidence for policymakers and researchers to address how best to integrate self-sampling hpv screening into both organized and opportunistic screening contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".