Cervical cancer screening preference among Inuit women in Nunavik, Quebec
Bibliographic record
Abstract
Self-sampling for oncogenic human papillomavirus (HPV) offers an alternative to the Papanicolaou (Pap) test for cervical cancer screening. We aimed to assess Inuit women’s cervical cancer screening preferences. Eligible Inuit women aged between 25 and 65 in 2022–2023 were given the choice between self-sampling, sampling performed solely by the nurse or the Pap test and were administered a questionnaire on screening preference. Thematic analysis was performed on qualitative data collected through a questionnaire asking if and why women prefer HPV self-sampling or the Pap test for cervical cancer screening. A total of 103 women agreed to participate. Of these, 12 (11.6%) chose to have the nurse perform the HPV test rather than self-collect. Among the 91 left for analysis, 80.2% (73) of women who self-sampled preferred self-sampling to the Pap test and 82.4% (75) would prefer self-sampling in the future. The most common reason given was comfortability (54.9%) and privacy (25.2%). Participants that did not prefer self-sampling (n = 7) expressed desire for a physical exam by the nurse or a lack of confidence in their ability to collect the sample. Eleven either did not indicate a preference, were unsure, or indifferent to cervical cancer screening method. This represents an improvement from a previously conducted study in 2012 among the same population who reported a preference for HPV self-sampling of 56%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 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".