HIV disclosure without consent linked to increased violence against women living with HIV in a Canadian setting
Bibliographic record
Abstract
Our study examined the association between HIV disclosure without consent and verbal and/or physical violence due to HIV status among women living with HIV (WLWH). This study draws on baseline data of a sample (N = 316) from SHAWNA, a longitudinal community-based open cohort with WLWH in Metro Vancouver, Canada (2010-2019). Bivariate and multivariable logistic regression was used to investigate factors associated with physical and/or verbal violence due to HIV status. Adjusted odds ratios (AOR) and 95% confidence intervals [95%CIs] are reported. In total, 46.5% experienced non-consensual disclosure of HIV status without consent and 34.2% experienced physical and/or verbal violence related to HIV status in their lifetime. In multivariable analysis, HIV disclosure without consent was associated with increased odds of experiencing HIV-related physical and/or verbal violence (AOR: 7.46[4.21-13.21]). Lifetime exposure to homelessness was also associated with increased odds of physical and/or verbal violence due to HIV status (AOR: 2.15[1.03-4.49]). This research underscores the reality of HIV stigmatization and criminalization and suggests a critical need to remove HIV disclosure from the reach of criminal law and ensure women's rights to confidentiality. Governments and organizations must work to identify and address the drivers of various levels of stigma and gender-based violence and invest in inclusive, trauma-informed, culturally safe support and care programs and policies designed in collaboration with WLWH.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 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".