Uptake of cervical cancer screening among sex workers living with HIV in Nairobi, Kenya: a cross-sectional study
Bibliographic record
Abstract
Background: Hospitals within Nairobi County, Kenya, offer cervical cancer screening services. However, most female sex workers do not seek this service. Objective: To determine uptake of cervical cancer screening among female sex workers living with HIV in Nairobi, Kenya. Design: A descriptive cross-sectional study. Methods: Computerized simple random sampling was used to select 75 study participants who met the inclusion criteria; data were collected using a structured questionnaire. The study was carried out among female sex workers living with HIV in Nairobi, Kenya, attending the Sex Workers Outreach Program. Results: 40% ( n = 30) of respondents were aged 18–25 years. Only 45.3% (34) had been screened for cervical cancer within the last 1 year. 65.3% ( n = 49) of respondents knew that cervical cancer affects the cervix but were not aware of what caused the disease. 77.6% ( n = 58) found the 8 am–5 pm health facility opening hours a hinderance to seeking services and 66.7% ( n = 50) found the screening method uncomfortable. Cultural practices and beliefs fostered stigma in 39.2% ( n = 29) of the sex workers; hence, they did not seek out services. Conclusion: Lack of information, cultural barriers, and facility operating hours prevent female sex workers living with HIV from getting tested for cervical cancer. These barriers once addressed could improve cervical cancer screening uptake among this high-risk 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".