Pathways From HIV-Related Stigma, Racial Discrimination, and Gender Discrimination to HIV Treatment Outcomes Among Women Living With HIV in Canada: Longitudinal Cohort Findings
Bibliographic record
Abstract
BACKGROUND: HIV-related stigma, gender discrimination, and racial discrimination harm mental health and hamper HIV treatment access for women living with HIV. Maladaptive coping strategies, such as substance use, can further worsen HIV treatment outcomes, whereas resilience can improve HIV outcomes. We examined resilience and depression as mediators of the relationship between multiple stigmas and HIV treatment outcomes among women living with HIV. SETTING: Ontario, British Columbia, and Quebec, Canada. METHODS: We conducted a longitudinal study with 3 waves at 18-month intervals. We used structural equation modeling to test the associations of multiple stigmas (HIV-related stigma, racial discrimination, and gender discrimination) or an intersectional construct of all 3 stigmas at wave 1 on self-reported HIV treatment cascade outcomes (≥95% antiretroviral treatment [ART] adherence, undetectable viral load) at wave 3. We tested depression and resilience at wave 2 as potential mediators and adjusted for sociodemographic factors. RESULTS: There were 1422 participants at wave 1, half of whom were Black (29%) or Indigenous (20%). Most participants reported high ART adherence (74%) and viral suppression (93%). Racial discrimination was directly associated with having a detectable viral load, while intersectional stigma was directly associated with lower ART adherence. Resilience mediated associations between individual and intersectional stigmas and HIV treatment cascade outcomes, but depression did not. Racial discrimination was associated with increased resilience, while intersectional and other individual stigmas were associated with reduced resilience. CONCLUSION: Race, gender and HIV-related stigma reduction interventions are required to address intersectional stigma among women living with HIV. Including resilience-building activities in these interventions may improve HIV treatment outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".