Emerging role of HPV self-sampling in cervical cancer screening for hard-to-reach women: Focused literature review.
Bibliographic record
Abstract
OBJECTIVE: To provide a focused critical review of the literature on the acceptability, feasibility, and uptake of human papillomavirus (HPV) self-sampling among hard-to-reach women. QUALITY OF EVIDENCE: . Only studies that focused on never-screened or underscreened populations were included in this review. MAIN MESSAGE: Human papillomavirus self-sampling was found to be highly acceptable and feasible among these hard-to-reach women across most studies. Mailing of self-sampling kits has been shown to increase participation among hard-to reach women. Some concerns remain regarding adherence to further follow-up among high-risk women with positive test results for HPV after screening. CONCLUSION: There is a strong body of evidence to support the usefulness of HPV self-sampling in increasing participation of hard-to-reach women in screening programs (level I evidence). Convenience, privacy, ease of use, and, likely, cost-effectiveness of HPV self-sampling are driving forces in its emerging role in cervical cancer screening among hard-to-reach women. Key barriers to participation could be addressed by overcoming disparities in HPV-related knowledge and perceptions about 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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".