Understanding the cervical cancer self-collection preferences of women living in urban and rural Rwanda
Bibliographic record
Abstract
Abstract PURPOSE Cervical cancer is a leading cause of cancer among women in low- and middle-income countries. Women in Rwanda have high rates of cervical cancer due to limited access to effective screening methods. Research in other low-resource settings similar to Rwanda has shown that HPV-based self-collection is an effective cervical cancer screening method. This study aims to compare the preferences of Rwandan women in urban and rural settings toward self-collection and to report on factors related to self-collection amenability. METHODS A cross-sectional survey was conducted from June 1-9, 2022. Women were recruited from one urban and one rural clinic in Rwanda. Women were eligible for the study if they were ≥ 18 years and spoke Kinyarwanda or English. The survey consisted of 51 questions investigating demographics and attitudes towards self-collection for cervical cancer screening. We reported descriptive statistics stratified by urban and rural sites. RESULTS In total, 169 urban and 205 rural women completed the survey. The majority of respondents at both sites had a primary school or lower education and were in a relationship. Both urban and rural respondents were open to self-collection; however, rates were higher in the rural site (79.9% urban and 95.6% rural; p-value<0.001). Similarly, women in rural areas were more likely to report feeling unembarrassed about self-collection (65.3% of urban, 76.8% of rural; p-value<0.001). Notably, almost all urban and rural respondents (97.6% urban and 98.5% rural) stated they would go for a cervical cancer pelvic examination to a nearby health center if their self-collected results indicated any concern (p-value=0.731). CONCLUSION Rwandan women in both urban and rural areas largely support self-collection for cervical cancer screening. Further research is needed to better understand how to implement self-collection screening services in Rwanda.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".