Self-Screening for Cervical Cancer Offered through a Digital Platform in a Region of British Columbia with Lower Screening Rates
Bibliographic record
Abstract
Cervical cancer is highly preventable through vaccination, early detection, and treatment, yet is the fourth most common cancer globally. HPV testing is superior to cytology for the detection of cervical pre-cancer, and jurisdictions around the world are implementing HPV primary screening, which offers the opportunity for self-screening, an important self-care intervention. Digital health solutions are also increasingly important components of self-care. In this study, we assessed the acceptability and completion of self-screening for cervical cancer offered through a digital platform within a low screening uptake region of British Columbia. The primary objective of this study was to evaluate the acceptability of self-screening for cervical cancer offered through a digital platform as measured by return rates of self-screening kits. Patients due or overdue for cervix screening were invited to participate. Eligible participants registered online to receive a self-screening kit, which included a device for vaginal self-screening, instructions, and a return envelope, sent to their home. After self-screening using the vaginal device, HPV testing was conducted. HPV-negative participants were returned to routine screening, and HPV-positive participants were recommended for cytology or colposcopy. Attendance rates at follow-up were evaluated. Participants were invited to complete an acceptability survey. From April 2019 to December 2023, 283 participants were sent kits, with 207 kits returned for a completion rate of 73%. Of valid samples (n = 202), 15 were HPV positive, and 93% attended follow-up care. Most respondents found the CervixCheck website easy to use, informative, and secure and were satisfied with receiving their results online. CervixCheck had a high completion rate among participants who were sent a self-screening kit. High compliance with recommended follow-up and high acceptability of self-screening for cervical cancer was observed. Most participants indicated they would self-screen again in the future. Innovative approaches to cervical screening, including self-screening and the use of digital health interventions, are ways to enhance equity and improve uptake of cervical screening.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".