Implementation of a Cervical Cancer Screening Intervention for Under- or Never-Screened Women in Ontario, Canada: Understanding the Acceptability of HPV Self-Sampling
Bibliographic record
Abstract
With appropriate screening, cervical cancer can be prevented. In Ontario, Canada, some groups of women have low screening rates. South Asian, Middle Eastern and North African women are particularly at risk of under-screening. Currently, cytology-based screening is used in Ontario, although the growing evidence and adoption of HPV testing for cervical screening has encouraged many jurisdictions around the world to move towards HPV testing, with the option of self-sampling. We conducted an intervention beginning in June 2018, where we recruited over 100 under- or never-screened (UNS) women who identify as South or West Asian, Middle Eastern or North African from the Greater Toronto Area, to understand the uptake and acceptability of HPV self-sampling as an alternative to a Pap test. Participants self-selected if they tried the kit or not and completed both quantitative and qualitative research activities. This paper focuses on the qualitative arm of the study, where follow-ups and five focus groups were conducted with those who tried the kit (three groups) and those who did not (two groups), as well as eight key informant interviews with community champions and others who were involved in our recruitment. We used the Consolidated Framework for Implementation Research (CFIR) to guide our data collection and analysis. Major themes around convenience, privacy and comfort came from the data as important drivers of the uptake of the intervention. The role of community champions and peers in engaging and educating UNS women, as well as having self-confidence to collect the sample, also came out as factors impacting uptake and plans for continued use. Overall, the intervention showed that HPV self-sampling is an acceptable alternative to a Pap test for some but not all UNS women in Ontario.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".