Discussions about self-obtained samples for HPV testing as an alternative for cervical cancer prevention
Bibliographic record
Abstract
OBJECTIVES: Patient-collected samples for human papillomavirus (HPV) testing have shown promise, thus opening up a new possibility for cervical cancer screening. The purpose of this study was to explore women's beliefs about collecting their own samples for HPV testing instead of participating in conventional Pap testing. METHODS: Three focus groups were conducted in diverse cities in Ontario, Canada. One group included women from a small under-serviced northern city, one included culturally diverse women from a large urban city, and one included culturally diverse women from a medium sized under-serviced city. Transcripts were coded using open and axial coding as well as focused coding procedures and were organized using qualitative software. The Health Belief Model (HMB) was used as a framework for designing the focus group guide and interpreting the results. RESULTS: Six overriding themes were identified in the analysis: (1) need (and desire) for information about cervical cancer and HPV, (2) concerns about self-sampling, (3) perceived potential of self-sampling, (4) logistics remain unanswered, (5) need for education and promotion of self-sampling, and (6) need for options. CONCLUSION: The six themes were connected to some or all of the HBM components. In particular, self-sampling provides a different benefits-minus-barriers equation, which might make it a preferred screening option for some women.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".