Gender Perspective of Risk Factors Associated with Disclosure of HIV Status, a Cross-Sectional Study in Soweto, South Africa
Bibliographic record
Abstract
BACKGROUND: Human Immunodeficiency Virus (HIV) status disclosure has been shown to provide several benefits, both at the individual and societal levels. AIM: To determine risk factors associated with disclosing HIV status among antiretroviral therapy (ART) recipients in South Africa. SETTING: A cross-sectional study on risk factors for viremia and drug resistance took place at two outpatient HIV clinics in 2008, at a large hospital located in Soweto, South Africa. METHODS: We conducted a secondary data analysis on socio-economic characteristics and HIV status disclosure to anyone, focusing on gender differences. Descriptive and multivariable logistic regression analyses were performed to model the associations between risk factors and HIV status disclosure. Additionally, descriptive analysis was conducted to describe gender differences of HIV status disclosure to partner, parents, parents in law, partner, child, family, employer, and other. PATIENTS: A total of 883 patients were interviewed. The majority were women (73%) with median age of 39 years. RESULTS: Employed patients were less likely to disclose than unemployed (odds ratio (OR) 0.36; 95% confidence interval (CI) 0.1-1.0; p = 0.05)). Women with higher income were more likely to disclose (OR 3.25; 95% CI 0.90-11.7; p = 0.07) than women with lower income, while men with higher income were less likely (OR 0.20; 95% CI 0.02-1.99; p = 0.17) than men with lower income. Men were more likely than women to disclose to their partner (p<0.01), and to partner and family (p<0.01), women were more likely than men to disclose to child and family (p<0.01), to child, family and others (p = 0.01). CONCLUSION: Being employed imposed a risk factor for HIV status disclosure, additionally we found an interaction effect of gender and income on disclosure. Interventions designed to reduce workplace discrimination and gender-sensitive interventions promoting disclosure are strongly recommended.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".