HIV, Gender, Race, Sexual Orientation, and Sex Work: A Qualitative Study of Intersectional Stigma Experienced by HIV-Positive Women in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: HIV infection rates are increasing among marginalized women in Ontario, Canada. HIV-related stigma, a principal factor contributing to the global HIV epidemic, interacts with structural inequities such as racism, sexism, and homophobia. The study objective was to explore experiences of stigma and coping strategies among HIV-positive women in Ontario, Canada. METHODS AND FINDINGS: We conducted a community-based qualitative investigation using focus groups to understand experiences of stigma and discrimination and coping methods among HIV-positive women from marginalized communities. We conducted 15 focus groups with HIV-positive women in five cities across Ontario, Canada. Data were analyzed using thematic analysis to enhance understanding of the lived experiences of diverse HIV-positive women. Focus group participants (n = 104; mean age = 38 years; 69% ethnic minority; 23% lesbian/bisexual; 22% transgender) described stigma/discrimination and coping across micro (intra/interpersonal), meso (social/community), and macro (organizational/political) realms. Participants across focus groups attributed experiences of stigma and discrimination to: HIV-related stigma, sexism and gender discrimination, racism, homophobia and transphobia, and involvement in sex work. Coping strategies included resilience (micro), social networks and support groups (meso), and challenging stigma (macro). CONCLUSIONS: HIV-positive women described interdependent and mutually constitutive relationships between marginalized social identities and inequities such as HIV-related stigma, sexism, racism, and homo/transphobia. These overlapping, multilevel forms of stigma and discrimination are representative of an intersectional model of stigma and discrimination. The present findings also suggest that micro, meso, and macro level factors simultaneously present barriers to health and well being--as well as opportunities for coping--in HIV-positive women's lives. Understanding the deleterious effects of stigma and discrimination on HIV risk, mental health, and access to care among HIV-positive women can inform health care provision, stigma reduction interventions, and public health policy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".