Prevalence and Correlates of Incarceration Among Trans Men, Nonbinary People, and Two-Spirit People in Canada
Bibliographic record
Abstract
In the United States, sexual and gender minority populations are known to experience both higher rates of incarceration and more harmful experiences while incarcerated. However, little is known about incarceration rates or experiences among these populations in Canada or among trans men, nonbinary people, and Indigenous Two-Spirit people. This community-based research study analyzed anonymous self-completed survey data from gay, bisexual, trans, and queer men, and nonbinary and Two-Spirit people to determine the prevalence and correlates of lifetime incarceration among trans men, nonbinary, and Two-Spirit participants. Overall, 5.7% of trans participants, 10.6% of nonbinary participants, and 19.7% of Two-Spirit participants reported being incarcerated in their lifetime, all higher than the prevalence among cisgender non-Two-Spirit participants (3.7%). Multivariable logistic regression models revealed both similar and different correlates of incarceration for trans, nonbinary, and Two-Spirit participants, including older age, less education, experiences of forced sex as a minor, hepatitis C virus (HCV) and HIV diagnoses, substance use, and being less out about one's sexuality. Our findings highlight the disproportionate and inequitable incarceration of trans men, nonbinary, and Two-Spirit people and underscore the need for access to gender diverse, culturally competent HCV and HIV screening, prevention, treatment, and harm reduction in correctional facilities.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".