Intersectional stigmas are associated with lower viral suppression rates and antiretroviral therapy adherence among women living with HIV
Bibliographic record
Abstract
OBJECTIVES: To explore the associations between intersectional poverty, HIV, sex, and racial stigma, adherence to antiretroviral therapy (ART), and viral suppression among women with HIV (WHIV). DESIGN: We examined intersectional stigmas, self-report ART adherence, and viral suppression using cross-sectional data. METHODS: Participants were WHIV ( N = 459) in the Women's Adherence and Visit Engagement, a Women's Interagency HIV Study substudy. We used Multidimensional Latent Class Item Response Theory and Bayesian models to analyze intersectional stigmas and viral load adjusting for sociodemographic and clinical covariates. RESULTS: We identified five intersectional stigma-based latent classes. The likelihood of viral suppression was approximately 90% lower among WHIV who experienced higher levels of poverty, sex, and racial stigma or higher levels of all intersectional stigmas compared with WHIV who reported lower experiences of intersectional stigmas. ART adherence accounted for but did not fully mediate some of the associations between latent intersectional stigma classes and viral load. CONCLUSION: The negative impact of intersectional stigmas on viral suppression is likely mediated, but not fully explained, by reduced ART adherence. We discuss the research and clinical implications of our findings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".