Determinants of cervical cancer screening among women living with HIV in Zimbabwe
Bibliographic record
Abstract
In sub-Saharan Africa (SSA), cervical cancer (CC) is the second leading cause of cancer-related deaths, with human immunodeficiency virus (HIV) seropositive women being particularly vulnerable. Despite the benefits of early CC screening in reducing HIV-related CC deaths, CC screening uptake remains limited, with wide disparities in access across SSA. As part of a larger study, this paper examines the determinants of CC screening among HIV-seropositive women of reproductive age (15-49 years) in Zimbabwe. Using the 2015 Zimbabwe Demographic and Health Survey, we conducted multilevel analyses of CC screening among 1490 HIV-seropositive women, nested in 400 clusters. Our findings revealed that, even though 74% of HIV-seropositive women knew about CC, only 17.6% of them reported ever screening for it. Women who held misconceptions about HIV (OR = 0.47, p = 0.01) were less likely to screen for CC compared to those with accurate knowledge about HIV and CC. HIV-seropositive women with secondary or higher education were more likely to screen (OR = 1.39, p = 0.04) for CC compared to those with a primary or lower level of education. Age was positively associated with screening for CC. Furthermore, locational factors, including province and rural-urban residence, were associated with CC screening. Based on these findings, we call for integrated care and management of HIV and non-communicable diseases in Southern Africa, specifically, Zimbabwe due to the legacy of HIV in the region.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".