Consistent condom utilization among sexually active HIV positive individuals in Sub-Saharan Africa: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract This study aimed to ascertain the pooled prevalence and trend of consistent condom use in Sub-Saharan Africa, addressing the fragmented and inconsistent research on its role in preventing HIV transmission. In this meta-analysis, we systematically searched electronic databases such as PubMed, Embase, Scopus, Web of Science, Global Index Medicus, ScienceDirect, Africa-Wide Information (via EBSCOhost), as well as clinical trial registries, and the search engine Google Scholar. All necessary data were extracted using a standardized data extraction format. The data were analyzed using STATA 17 statistical software. Heterogeneity among the studies was assessed using theI2test. A random-effect model was computed to estimate the pooled rate of consistent condom utilization. This meta-analysis, which included thirty-three full-text studies, found a pooled prevalence of 44.66% (95% CI 18.49–70.83;I2 = 0.00%) for consistent condom use in Sub-Saharan Africa. While the prevalence fluctuated between 2007 and 2022, the year-to-year variations were not statistically significant. The current study identified low rates of consistent condom use, with utilization fluctuating annually in the study area. Therefore, uncovering the underlying reasons and addressing barriers to consistent condom use is crucial 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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".