Toward Affirming Care: An Initial Evaluation of a Sexual Violence Treatment Network’s Capacity for Addressing the Needs of Trans Sexual Assault Survivors
Bibliographic record
Abstract
There is a global call to action to improve transgender (trans) health to achieve health equity for people of all gender identities. Trans persons experience high rates of sexual assault and have historically had limited or no access to health care that meets their needs. As an initial step in addressing this, we evaluated a sexual assault treatment network's capacity for addressing the needs of trans sexual assault survivors. Working with an Advisory Group comprising trans community members and their allies who have expertise in trans health, a short online questionnaire was developed and distributed to the program leaders of Ontario's 35 hospital-based Sexual Assault/Domestic Violence Centres (SA/DVTCs). A total of 27 program leaders completed the questionnaire for a response rate of 77%. The majority of respondents reported that their program collaborates with trans-positive services within their community (70.4%). However, only two in five (40.7%) program leaders indicated that the patient bill of rights at their hospital included a statement pledging nondiscrimination on the basis of gender, gender identity, and/or gender expression. All (100%) program leaders felt that the nurses and physicians working within their programs would benefit from (further) training in the care of trans persons who have been sexually assaulted. This study represents an important step in a research program aimed at enhancing Ontario SA/DVTCs' response to trans persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".