Brief Report: Social Factors Associated With Trajectories of HIV-Related Stigma and Everyday Discrimination Among Women Living With HIV in Vancouver, Canada: Longitudinal Cohort Findings
Bibliographic record
Abstract
Abstract Introduction: Women living with HIV (WLHIV) experience stigma rooted in social inequities. We examined associations between social factors (food insecurity, housing insecurity, violence, sexual minority identity, and substance use) and HIV-related stigma and Everyday Discrimination trajectories among WLHIV. Methods: This community-based open longitudinal cohort study with WLHIV living in and/or accessing HIV care in Metro Vancouver, Canada, plotted semiannual averages (2015–2019) of recent (past 6-month) HIV-related stigma and Everyday Discrimination. We examined distinct trajectories of HIV-related stigma and Everyday Discrimination using latent class growth analysis (LCGA) and baseline correlates of each trajectory using multinomial logistic regression. Findings: Among participants (HIV-related stigma sample: n = 197 participants with n = 985 observations; Everyday Discrimination sample: n = 203 participants with n = 1096 observations), LCGA identified 2 distinct HIV-related stigma and Everyday Discrimination trajectories: sustained low and consistently high. In multivariable analysis, concurrent food and housing insecurity (adjusted odds ratio [AOR]: 2.15, 95% confidence interval [CI] 1.12–4.12) and physical/sexual violence (AOR: 2.57, 95% CI: 1.22–5.42) were associated with higher odds of the consistently high (vs. sustained low) HIV-related stigma trajectory. Sexual minority identity (AOR: 2.84, 95% CI: 1.49–5.45), concurrent food and housing insecurity (AOR: 2.65, 95% CI: 1.38–5.08), and non injection substance use (less than daily vs. none) (AOR: 2.04, 95% CI: 1.03–4.07) were associated with higher odds of the consistently high (vs. sustained low) Everyday Discrimination trajectory. Conclusions: Social inequities were associated with consistently high HIV-related stigma and Everyday Discrimination among WLHIV. Multilevel strategies can address violence, economic insecurity, intersecting stigma, and discrimination to optimize health and rights among WLHIV.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".