Acceptance of human papillomavirus self-sampling in underserved and under-screened communities in Canada: a literature review
Bibliographic record
Abstract
Cervical cancer is the fourth most common cancer among women. In Canada, the disease accounted for roughly 400 deaths from 1,550 diagnoses in 2017 alone. Nearly all cases of cervical cancer are due to persistent infection with human papillomavirus (HPV). Most cervical cancer cases can be prevented with screening, and those who develop cervical cancer tend to be classified as under-screened or never-screened. Often, these people belong to underserved groups such as immigrants, racialized individuals, and those living in rural communities. Traditional cervical cancer screening involves Pap (Papanicolaou) tests while self-sampling allows people with a cervix to collect their own sample, when and where they choose. This literature review examined Canadian studies exploring the acceptance of self-sampling amongst underserved populations. The findings of this review support the idea that most underserved and underscreened populations accept and support the idea of HPV self-sampling. Common reasons for acceptance of self-sampling included convenience, privacy, and timesaving. The most commonly reported concern was a lack of confidence in performing the test correctly. While the studies included in this review do not represent every underserved population in Canada, the results suggest that with proper education and support, HPV self-sampling could be a useful addition to cervical cancer screening in Canada.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".