Women and antiretroviral therapy adherence: examining the impact of internalized stigma by gender
Bibliographic record
Abstract
Background: Antiretroviral therapy (ART) and ambitious treatment strategies such as Treatment as Prevention (TASP) and 90-90-90 have reduced HIV-related morbidity and mortality among people living with HIV (PLWH), and substantially decreased the likelihood of transmission of the virus to others.The benefits of ART are not felt equally among all PLWH, as PLWH may not be able to maintain ART adherence.This study examines gender differences in ART adherence and the sociostructural and psychosocial factors associated with such differences in British Columbia, Canada.Methods: Quantitative analyses assessed optimal ART adherence by gender, longitudinally and cross-sectionally, using bivariate, multivariate analyses, as well as structural equation modelling.All analyses used data collected from 2000 onward by the BC Centre for Excellence in HIV/AIDS from BC, Canada, with adherence measured using pharmacy refill compliance data.Findings: Gender-based disparities were evident in ART adherence in BC, with a significantly lower proportion of women attaining optimal (≥95%) adherence relative to men (57.0%versus 77.1; p<0.001) regardless of subgroup.Women also experienced far greater sociostructural and psychosocial disparity than men, however food insecurity was the only variable examined that was independently associated with suboptimal adherence among women (adjusted odds ratio [AOR]: 0.364; 95% confidence interval [CI]: 0.181 to 0.734).Psychosocial variables exhibited different effects and interactions for men and women, however, they explained only a low proportion of the variability observed in ART adherence (1.6% and 3.7%, respectively).Disclosure worries was the only psychosocial variable to affect women's adherence (standardized regression weight [r]: -0.192; p=0.015).Personalized stigma (r: 0.299; p<0.001) and internalized stigma (r: 0.321; p<0.001), however, did have a significant explanatory affect on depression experienced by women.Discussion: Gender-based disparities must be addressed if women living with HIV are to attain optimal health and if we are to achieve the goals of 90-90-90.Addressing aspects of poverty and the greater adversity that women face in this regard would result in a more salient improvement to ART adherence than interventions addressing stigma.v
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".