Differences in Rape Acknowledgment and Mental Health Outcomes Across Transgender, Nonbinary, and Cisgender Bisexual Youth
Bibliographic record
Abstract
The purpose of this study was to document the rates of rape acknowledgment (labeling rape as rape rather than using a minimizing label) and the corresponding mental health correlates using the minority stress framework in a unique and vulnerable sample: racially diverse sexual and gender minority young adults. Participants were 245 young adults who identified their sexual orientation as under the bisexual umbrella. A total of 159 of these participants (65.2%) identified their gender identity as nonbinary. All participants completed a series of online questionnaires regarding their sexual victimization history, mental health outcomes (depression, anxiety, and posttraumatic stress disorder [PTSD]), and constructs relevant to minority stress theory (level of outness, internalized bisexual negativity, connection to LGBTQ [lesbian, gay, bisexual, transgender, questioning] community). Rape acknowledgment was significantly greater among gender nonbinary participants (79.9%) than among trans and cisgender male participants (17.9%). Lack of rape acknowledgment was associated with increased anxiety, depression, and PTSD. Outness was significantly associated with greater rape acknowledgment. Despite the highly increased vulnerability for sexual violence among sexual and gender minorities, very little is understood about the mechanisms of this increased vulnerability or their unique needs for recovery. The results of this study strongly suggest the importance of a minority stress framework for understanding this increased vulnerability and for designing sexual violence prevention and recovery interventions for sexual and gender minority populations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".