Predictors of never testing for HIV among sexually active individuals aged 15–56 years in Rwanda
Bibliographic record
Abstract
Human Immunodeficiency Virus (HIV) testing services are known as the primary step in preventing the spread of HIV. However, access to these crucial services varies across regions within continents due to disparities in healthcare infrastructure, resources, and awareness. Approximately one in every five people living with HIV (PLWH) encounters obstacles in accessing HIV testing, notably in Eastern and Southern Africa, where geographical, resource, awareness, and infrastructure limitations prevail. Consequently, HIV remains a significant public health concern in these regions, necessitating expanded testing efforts to combat the HIV/AIDS disaster. Despite these challenges, there is a lack of scientific evidence on the prevalence of HIV testing and its determining factors in Rwanda. This study determined the prevalence of never being tested for HIV and its associated factors among sexually active individuals aged 15-56 who participated in the Rwanda AIDS Indicators and HIV Incidence Survey (RAIHIS). This cross-sectional study enrolled 1846 participants. The variables were extracted from the RAIHIS dataset and statistically analyzed using STATA software version 13. Bivariate and multivariate logistic regression models were employed to identify predictors of never having undergone HIV testing, with a 95% confidence interval and a 5% statistical significance level applied. The prevalence of non-testing for HIV was 17.37%. Being aged 15-30 years (aOR 2.57, 95%CI 1.49-4.43, p < 0.001) and male (aOR 2.44, 95%CI 1.77-3.36, p < 0.001) was associated with an increase in the odds of never testing for HIV. Further, those from urban area were less likely than those living in rural areas to have never tested for HIV (aOR 0.31; 95% CI 0.38-0.67; p < 0.001). Participants who were not aware of HIV test facilitates were more likely to have never undergone HIV testing (aOR 1.75; 95% CI 1.25-2.47; p = 0.031) than their counterparts. While the prevalence of HIV non-testing remains modest, the significance of youth, male gender, lack of awareness, and rural residence as influential factors prompts a call for inventive strategies to tackle the reasons behind never having undergone HIV testing. Further exploration using mixed methodologies is advocated to better comprehend socio-cultural impacts and causation relating to these identified factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".