Patterns of Violence Victimization and Disclosure Among Transgender/Gender Nonconforming and Cisgender Latinx College Students
Bibliographic record
Abstract
Although research on sexual violence experiences of minoritized college students has increased in recent years, little is known about the experiences of Latinx students. Even less knowledge is available on whether such violence and disclosure experiences vary by gender identity. The present study addressed this gap by exploring the rates of sexual violence and disclosure patterns after experiences of sexual violence among Latinx college students. Using data from the 2019–2020 Healthy Minds Study, a national sample of Latinx college students was analyzed ( n = 6,690). Descriptive statistics, χ 2 tests, and one-way analyses of variance were conducted to explore differences in gender identity, sexual violence victimization, and disclosure type. Multiple logistic regression analyses were used to estimate the association between (1) gender identity and sexual violence victimization and (2) gender identity and sexual violence disclosure while controlling for various sociodemographic measures. Results indicated sexual violence victimization occurred at significantly higher rates among transgender/gender nonconforming and cisgender women Latinx students, compared to cisgender men. Transgender/gender nonconforming students who experienced sexual violence were significantly less likely to make any disclosure of sexual violence when compared to cisgender men. These findings highlight the extent to which experiences of sexual violence among Latinx students differ based on their gender identity. It also brings attention to the need for campus programming to attend to students who often face structural barriers because of their intersecting identities, specifically transgender/gender nonconforming Latinx students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".