The Prevalence and Consequences of Gender-Based Violence Among Trans and Gender Diverse University Students in Ontario
Bibliographic record
Abstract
Community studies documenting gender-based violence (GBV) experienced by trans and gender diverse (TGD) people often find differences in prevalence across TGD subgroups. In contrast, studies with university students tend to treat TGD students as a homogenous group, leaving differences across subgroups unknown. Using data from TGD Ontario university students, we examined the prevalence and impacts of GBV across the spectrum of nonbinary and gender queer, trans women and trans feminine (TWTF), and trans men and trans masculine (TMTM) students. Specifically, we explored the frequency of subtle and overt forms of GBV (trans environmental microaggressions, trans interpersonal microaggressions, victimization) and their relationship with psychological (positive mental health, psychological distress, perceived stress) and social (campus belonging) well-being among each subgroup. TMTM students reported experiencing both microaggression types significantly more frequently than TWTF; no other differences in prevalence were found. Consistent with minority stress theory, all but one statistically significant result suggested that experiences of GBV are associated with poorer outcomes. Specifically, GBV can negatively impact TGD students' well-being, although its impacts are not identical across TGD subgroups. The findings highlight the importance of considering TGD students as a heterogeneous group when examining GBV and its consequences. Implications for research, policy, practice, and the training of practitioners are offered.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".