HPV Detection by Self-Sampling in Nunavik, Quebec: Inuit Women’s Sampling Method Preferences
Bibliographic record
Abstract
There is a higher incidence of cervical cancer and prevalence of genital human papillomavirus (HPV) infection among the Inuit in Canada than the general population.Self-sampling of cervicovaginal cells for HPV testing has the potential to increase cervical cancer screening coverage in this population, but only if it is acceptable to women.We sought to determine acceptance of and preference for self-collection of cervicovaginal samples for HPV testing in comparison with provider-collection, and to explore demographic characteristics of preference for self-collection among a sample of Inuit women from Nunavik, Quebec.Women aged 18-69 years were recruited from a previously formed cohort on the natural history of HPV in Nunavik.Both self-collected and provider-collected specimens were collected with polyester-tipped swabs, and women completed a short written questionnaire immediately after specimen collection.Logistic regression was used to estimate predictors of preference.Of the 109 eligible women who were approached to participate, 93 (85%) accepted.Self-sampling was preferred by 56% of the women over provider-sampling.Education was the only predictor of preference for self-sampling, where having at least a grade 9 education was inversely associated with preference for self-sampling (OR = 0.29, 95% CI [0.09, 0.92]).Self-sampling has the potential to increase cervical cancer screening coverage, but any implementation of self-sampling should be concurrent with an education campaign on the importance of cervical cancer screening, the relationship between HPV virus and cervical cancer, and the accuracy of self-sampling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".