Intersecting impacts of transphobia and racism on HIV risk among trans persons of colour in Ontario, Canada
Bibliographic record
Abstract
Purpose – Minority stress theory suggests enhanced HIV risk for those experiencing social marginalization, while an intersectionality framework posits that forms of marginalization may interact. The purpose of this paper is to understand how race/ethnicity- and gender-based discrimination may impact HIV risk among transgender or transsexual (trans) people. Design/methodology/approach – The Trans PULSE project – a community-based research study in Ontario, Canada – used respondent-driven sampling to survey 433 trans participants, including 35 Aboriginal persons and 62 non-Aboriginal persons of colour. Descriptive and regression analyses were weighted to adjust for recruitment probabilities. Findings – Most Aboriginal persons (65 per cent, 95 per cent CI: 37-90) and persons of colour (90 per cent, 95 per cent CI: 74-100) reported at least one experience of racism or ethnicity-based discrimination, and the vast majority had experienced transphobia (90 and 92 per cent, respectively). Among non-Aboriginal trans persons of colour, experiences of racism and transphobia interacted in increasing odds of engagement in high-risk sex. Increases in experience of one type of discrimination had strongest effects on HIV risk when coupled with high levels of the other. Research limitations/implications – Persons of colour were ethno-racially diverse; it is possible that different experiences of racism, with divergent effects, were collapsed. Odds ratios may overestimate actual risk ratios. Originality/value – While some sub-groups of trans people of colour have been identified as highly vulnerable to HIV, few studies have explored the impact of discrimination. This paper explores the roles of two types of discrimination in engendering HIV-related risk, and suggests potential limits to resiliency in the face of high levels of discrimination targeting multiple facets of identity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".