Uptake and feasibility of HPV self-sampling among patients attending a family medicine clinic in Edmonton, Alberta
Bibliographic record
Abstract
Context Human Papillomavirus self-sampling (HPV SS) is when a person with a cervix takes their own sample for cervical cancer screening. It is more accurate than the Pap test and reduces screening barriers. Few studies have examined the use of HPV SS in family medicine clinics. Objective To determine the uptake and feasibility of HPV SS when provided in a family medicine clinic. Study Design Implementation study. Population studied Patients 25-69 years old, with a cervix, who have not had a Pap test ≥2.5 years. Patients were excluded if they were pregnant, ongoing colposcopy care/discharged within 11 months, history of invasive cancer, abnormal vaginal bleeding/current menstrual bleeding, and last Pap test was abnormal. Setting Shifa medical clinic, a community medical clinic in Edmonton, Alberta with a cervical cancer screening rate of 80%. Intervention Eligible patients were opportunistically given the option to complete HPV SS from September-November 2024 as Phase 1 of the Alberta Cervical Cancer Screening Program’s HPV SS pilot (using the Copan FLOQ Swab and the Roche Cobas 6800 platform). Outcome Measures Proportion of patients that completed HPV SS, results of HPV SS, patient preference, physician perspective, and environmental costs. Results Two hundred and twenty-six patients were offered HPV SS; median age of 43 (interquartile range [IQR] 32–56); 89% urban; 14% immigrant; 4% Indigenous; 58% panelled (i.e., attached to a family physician); and mean Pampalon Material Deprivation Index was 2.7 (SD 1.4). Overall, 81% completed HPV SS; 165 were negative, 17 positive, and 2 indeterminate, resulting in 7 Pap tests and 10 direct colposcopy referrals. The most common reasons for declining HPV SS were lack of time, lack of interest in screening, and preferring the Pap test. Among patients that completed HPV SS, 98% preferred to have HPV SS as an option for future screening. Family physicians fully supported the use of HPV SS. There was a 20% carbon savings of HPV SS compared to the Pap test. Conclusion The study suggests that HPV SS may be the preferred cervical cancer screening approach for patients attending a family medicine clinic. Screening programs should consider improving access to HPV SS for this population.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".