Assessing how women access healthcare to inform cervical cancer and HIV screening in rural Uganda
Bibliographic record
Abstract
ABSTRACT Objective This study aims to compare how HIV-positive and HIV-negative women in a remote sub-country in Uganda access health services to inform consideration of potential HIV and HPV-based cervical cancer screening integration at the community level. Methods This cross-sectional study recruited women living in the South Busoga District Reserve from January to August 2023. Women were eligible if they were aged 30 to 49 years old, had no history of cervical cancer screening or treatment, had no previous hysterectomy, and could provide informed consent. Participants completed a survey administered by village health teams, which included questions on HIV status, demographics, healthcare access, and services received. The data was analyzed using bivariate descriptive statistics, including chi-square and Fisher’s exact tests. Results Among the 1437 participants included in the analysis, 8.8% were HIV-positive. The majority of the respondents were between 30-39 years of age, were married, had received primary education or higher, and were farmers. The majority of women in both groups had accessed outreach visits (HIV-positive = 89.0%, HIV-negative = 85.8%) and health centres (HIV-positive = 96.1%, HIV-negative = 80.2%). The most commonly received services among both groups of women at outreach visits and health centres were immunization and antenatal care, respectively. Conclusion Our study demonstrated that there were no significant differences in healthcare access between HIV-positive and HIV-negative women in rural Uganda. Additionally, the high usage of healthcare services by women living with HIV suggests that the integration of cervical cancer and HIV screening may facilitate early detection and prevention of cervical cancer among this population. This can reduce the burden of disease in Uganda and further contribute to the World Health Organization’s initiative to eradicate cervical cancer.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".