Intersecting gender, ethnicity, and sexual orientation identities and HIV stigma: results from the People Living with HIV Stigma Index study in three provinces in Canada
Bibliographic record
Abstract
Stigma remains a significant burden for people living with HIV and while studies have examined the impacts of gender, ethnicity, and sexual orientation on stigma separately, little is known about how these factors may intersect and potentially exacerbate levels of stigma. This study examines how these intersecting social positions may relate to levels of internalised, enacted and anticipated HIV stigma. Participants were recruited in Ontario, Alberta, and Québec (n = 1040) as part of the People Living with HIV Stigma Index study in Canada. Three-way interaction models were constructed by creating interaction terms from the product of gender, ethnicity, and sexual orientation variables that predicted each type of stigma. Levels of internalised, enacted and anticipated stigma were consistent across most intersecting groups; however, people occupying certain intersections experienced significantly higher levels of stigma. Three-way interaction analyses showed that for internalised stigma, people at the intersection of African/Caribbean/Black, lesbian, cis-women identities had significantly higher scores (b = 0.90, p = 0.06), while people at the intersection of Indigenous, lesbian, and cis-women identities had higher scores for enacted stigma (b = 1.21, p = 0.01) compared to the White, heterosexual, cis-men reference group. Interventions designed for populations that take intersectionality into account may be effective in reducing HIV stigma, although more quantitative intersectionality work must be done to understand these implications fully.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".