Cross-sectional evaluation of a peer educator-led empowerment initiative for improving participation in cervical precancer screening among female sex workers in Ghana
Bibliographic record
Abstract
Purpose: Despite having an increased risk of HPV infection, female commercial sex workers (FCSWs) have low uptake of cervical screening. Among FCSWs in the Volta Region of Ghana referred for screening following peer education, we examined the prevalence of high-risk human papillomavirus (hr-HPV) and cervical lesions and modelled factors associated with these screening outcomes. Patients and methods: As part of the mPharma Ten Thousand Women Initiative, implemented between September and October 2022, 340 FCSWs in Ho and Aflao were recruited by peer educators. Screening involved MA-6000 testing (with full genotyping using the AmpFire HPV platform) and enhanced visual assessment (EVA) mobile colposcopy. Results: -value = 0.002). HIV infection (aOR, 9.95; 95% CI, 1.19-83.37) and engagement in oral-genital contact (aOR, 5.56; 95% CI, 1.35-22.94) were associated with higher odds of clinically significant EVA findings. Conclusion: Our study underscores the urgent need for comprehensive and targeted interventions to reduce the burden of hr-HPV and the potential for cervical precancer/cancer among FCSWs in Ghana. We further highlight the importance of promoting continuous condom use among FCSWs during interactions with patrons and considering HIV status in the development and implementation of cervical precancer screening programs for FCSWs.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".