Assessing differences in healthcare access by HIV status to inform cervical cancer and HIV screening in rural Uganda
Bibliographic record
Abstract
Uganda has one of the highest incidence rates of cervical cancer in the world. Although this impacts all women, women living with human immunodeficiency virus (HIV) experience an increased risk for developing cervical cancer. This study aims to compare how HIV-positive and HIV-negative women in a remote sub-county in Uganda access health services to inform consideration of potential HIV and HPV-based cervical cancer screening integration at the community level. Women were recruited for this cross-sectional study door-to-door by village health teams if they had no prior screening or treatment of cervical cancer, no previous hysterectomy, were 30-49 years old residents of the South Busoga District Reserve, and could provide verbal informed consent. Participants completed a baseline survey, which included questions on HIV status, demographics, prior health history, past healthcare access and services recieved. The data was analyzed using bivariate descriptive statistics. Among the 1437 participants included in the analysis, 8.8% were HIV-positive. The majority of the respondents were between 30-34 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. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".