Using self-collection HPV testing to increase engagement in cervical cancer screening programs in rural Guatemala: a longitudinal analysis
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is a leading cause of death in low- and middle-income countries. Self-collection testing for human papillomavirus (HPV) is an alternative form of cervical cancer screening that can be completed privately and at home. Understanding how the use of HPV testing influences follow-up care in low-resourced settings is crucial before broad implementation. This study aimed to identify if access to self-collection HPV testing impacts participation in established cervical cancer screening programs among women in two rural communities in Guatemala. METHODS: A cohort of 956 women was recruited in 2016 and followed for 2 years for the HPV Multiethnic Study (HPV MES). At baseline, women answered a questionnaire assessing cervical cancer screening history and were offered self-collection HPV testing. Women were re-contacted yearly to determine receipt of additional screening. Statistical changes in screening behavior before and throughout study participation, stratified by self-collection status, were assessed using McNemar pair tests for proportions. Alluvial plots were constructed to depict changes in individual screening behavior. The odds of changes in Pap-compliance (screened in past 3 years), given collection status, were assessed using multivariate logistic regressions. RESULTS: Reported screening rates increased 2 years after enrollment compared to rates reported for the 3 years before study entry among women who collected a sample (19.1% increase, p < 0.05), received results of their test (22.1% increase, p < 0.05), and received positive (24.2% increase, p < 0.1) or negative results (21.7% increase, p < 0.05). However, most increases came from one community, with minimal changes in the other. The adjusted odds of becoming Pap compliant were higher for women who collected a sample vs. did not (OR: 1.48, 95% CI: 0.64, 3.40), received their result vs. did not (OR: 1.29, 95% CI: 0.52, 3.02), and received a positive result vs. negative (OR: 2.43, 95% CI: 0.63, 16.10). CONCLUSIONS: Participation in self-collection HPV testing campaigns may increase likelihood of involvement in screening programs. However, results varied between communities, and reporting of screening histories was inconsistent. Future work should identify what community-specific factors promote success in HPV testing programs and focus on improving education on existing cervical cancer interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".