Advancing Trans-Affirming Practice to Recognize, Account for, and Address the Unique Experiences and Needs of Transgender Sexual Assault Survivors
Bibliographic record
Abstract
Transgender (trans) persons are sexually assaulted at high rates and often encounter barriers to equitable services and supports. The receipt of timely and appropriate postassault care, provided increasingly by specialized forensic nurses around the world, is critical in ameliorating the harms that accompany sexual assault. In order to adequately respond to the acute health care needs of trans clients and attend to longer term psychosocial difficulties that some experience, forensic nurses not only require specialized training but must also cultivate collaborative relationships with trans-positive health and social services in their communities. To meet this need, we describe our strategy to advance trans-affirming practice in the sexual assault context. We outline the design and evaluation of a trans-affirming care curriculum for forensic nurses. We also discuss the planning, formation, and maturation of an intersectoral network through which to disseminate our curriculum, foster collaboration, and promote trans-affirming practice across health care and social services in Ontario, Canada. Our approach to advancing trans-affirming practice holds the potential to address systemic barriers experienced by trans survivors and transform the response to sexual assault across other sectors and jurisdictions.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".