Experiences of discrimination or violence and health outcomes among Black, Indigenous and People of Colour trans and/or nonbinary youth
Bibliographic record
Abstract
AIMS: Identify the relationship between experiences of discrimination or violence and health outcomes for transgender and nonbinary Black, Indigenous and People of Colour (BIPOC) compared to their white trans and nonbinary peers. DESIGN: A national online survey, the 2019 Canadian Trans and Nonbinary Youth Health Survey, was conducted among youth ages 14-25, in English and French. METHODS: Participants were recruited from November 2018 to May 2019 (N = 1519). BIPOC youth comprised 25.7% of the sample (n = 390). Questions about six types of discrimination (e.g. racism and sexism) and violence (physically threatened or injured), plus foregone health care, self-harm and suicidality were drawn from existing validated measures. Analyses involved cross-tabulations with chi-square tests and logistic regressions. RESULTS: Trans and non-binary BIPOC reported significantly higher prevalence of suicide attempts (24.9% vs. 19.5%) and violence victimization compared to white youth. They had significantly higher odds of self-harm and foregone health care when experiencing discrimination by ethnicity or culture. All types of violence were significantly associated with higher odds of foregone physical health care, self-harm, suicide ideation and suicide attempt. CONCLUSION: In Canada, trans and nonbinary youth who are BIPOC face disparities in health outcomes and experiences of violence and discrimination compared to white trans and nonbinary youth. IMPACT: Nurses should assess for violence exposure and discrimination among trans and/or nonbinary youth of colour, and promote health equity by advocating for policies to reduce violence and discrimination, including racism, for trans and nonbinary young people.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".