The Prevalence and Consequences of Gender-based Violence Among Trans and Non-Binary University Students in Ontario
Bibliographic record
Abstract
Community studies documenting gender-based violence (GBV) experienced by trans and non-binary (TNB) people often find differences in prevalence across TNB subgroups. Studies of TNB university students tend to treat them as a homogenous group, thus gender differences in terms of subgroups are unknown. Using data collected from TNB Ontario university students, I examined the prevalence and impacts of GBV across three TNB subgroups (trans man spectrum, trans women spectrum, and gender queer/non-binary). Specifically, reflecting subtle and overt forms of GBV, among each subgroup I explored the frequency of trans environmental microaggressions, trans interpersonal microaggressions, and victimization, and their relationship with positive mental health, psychological distress, perceived stress, and campus belonging. Trans man spectrum students reported experiencing both microaggression types significantly more frequently than the trans woman spectrum. No other differences in prevalence were found. Consistent with minority stress theory, at all levels of analysis statistically significant relationships between GBV forms and wellbeing outcomes, with one exception, were in expected direction, suggesting that experiences of GBV are associated with poorer wellbeing outcomes. Exploratory and explanatory analyses suggest that GBV can negatively impact students’ wellbeing, but these impacts are not identical across TNB subgroups. This study highlights the importance of considering TNB students as a heterogeneous group when examining GBV and its consequences. Implications for policy, practice, and the training of social work students 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 | 0.002 | 0.001 |
| 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".