Poverty stigma is associated with suboptimal HIV care and treatment outcomes among women living with HIV in the United States
Bibliographic record
Abstract
OBJECTIVE: To examine whether experienced poverty stigma is associated with worse HIV care and treatment outcomes. DESIGN: We analyzed cross-sectional data from 433 women living with HIV enrolled in the Women's Adherence and Visit Engagement substudy of the Women's Interagency HIV Study. METHODS: Exposure was experienced poverty stigma, measured using the Perceived Stigma of Poverty Scale. Outcomes were viral suppression, CD4 cell count at least 350 cells/μl, and attending all HIV care visits in the past 6 months. Multivariable logistic regression models adjusted for income, age, race/ethnicity, education, substance use, months taking antiretroviral therapy (ART), number of antiretroviral pills in ART regimen, unstable housing, relationship status, and exchanging sex for money, drugs, or shelter. We also explored whether self-reported at least 95% ART adherence mediated the relationship between poverty stigma and viral suppression and CD4 cell count at least 350 cells/μl. RESULTS: Experienced poverty stigma was associated with lower adjusted odds of viral suppression [adjusted odds ratio (aOR) 0.76; 95% confidence interval (CI) 0.61-0.96], CD4 cell count at least 350 cells/μl (aOR 0.69; 95% CI 0.52-0.91), and attending all HIV care visits (aOR 0.73; 95% CI: 0.54-0.98). Exploratory mediation analysis suggests that at least 95% ART adherence significantly mediates the relationship between experienced poverty stigma and viral suppression and CD4 cell count at least 350 cells/μl. CONCLUSION: Longitudinal research should assess these relationships over time. Findings support interventions and policies that seek to reduce poverty stigma among people living with HIV.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".