Discriminatory experiences among gay, bisexual, and other men who have sex with men, and transgender and non-binary individuals: a cross sectional analysis
Bibliographic record
Abstract
Background Several systems of oppression combine in complex ways to impact the lives of minority populations. Following an intersectionality framework, we assessed the frequency and perceived reasons for discrimination among gay, bisexual, and other cisgender men who have sex with men (MSM) and transgender and non-binary individuals (TGNB), stratified by race. Methods Online survey among MSM and TGNB ≥18 years living in Brazil, between November/2021 and January/2022. We used the 18-item Explicit Discrimination Scale to assess day-to-day experiences of differential treatment, and perceived discrimination. For each item, participants indicated their perceived reasons for differential treatment using 14 pre-defined options. Negative binomial regression models assessed if race was a significant predictor of discrimination. Subsequent models, stratified by race, examined associations of perceived reasons and number of reasons with perceived discrimination. Findings Of 8464 MSM and TGNB, 4961 (58.6%) were White, 2173 (25.7%) Pardo (Brazil's official term for admixed populations), and 1024 (12.1%) Black. Black participants' scores for perceived discrimination (mean, standard deviation) were higher (10.2, 8.8) [ Pardo (6.5, 6.8), White (5.2, 5.7)], and race was both the main reason for and the strongest predictor of perceived discrimination. The number of reasons participants used to interpret their discriminatory experiences was also a predictor of discrimination score among White, Pardo , and Black participants. Interpretation LGBTQIA+phobia was highly prevalent among all participants. Additionally, our results indicated that Black MSM and TGNB participants were more frequently discriminated against than other racial groups, with racial discrimination uniquely contributing these experiences. Funding Fundação Oswaldo Cruz, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".