HIV serodiscordant and nondisclosure rates among married women living with HIV in a Southern Nigerian region
Bibliographic record
Abstract
Objectives: Intimate sexual partners’ disclosure of HIV positive status is vital in the control of HIV/AIDS pandemic globally. The disclosure rates vary from region to region. The aim of this study was to document the prevalence of HIV serodiscordance and partners disclosure rate; and also determine the associated factors among HIV positive married women living in Calabar region of Nigeria. Material and Methods: A cross-sectional survey was conducted among 260 married women, 18 years and above, receiving HIV care at various health institutions in the region. Data were analyzed using SPSS VERSION 23. Their demographic and health profile were presented in simple proportion and percentages while Chi-square test and logistic regression were used to determine the factors influencing patient’s HIV status disclosure with the level of significance set at 0.05. Results: A total of 254 compiled questionnaires were included in the analysis (response rate-97.7%). The serodiscordant rate was 50%. HIV status disclosure to partner was high, 89.4%. The main determinants of HIV status disclosure were good level of education (AOR = 2.2, 95% CI: = 1.75–2.53, P = 0.007) and long duration of ART use (AOR = 3.23, 95% CI = 2.78–4.15, P = 0.001) while women with high discordant rate were more likely to be divorced/separated (P = 0.012, OR = 1.67). Conclusion: Female education is an important factor in HIV control. Disclosure of HIV status is beneficial to the partner as it promotes safe sex practices and increases adherence to ART.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".