Initial Stages of Development for an Intersectoral Network on Trans-Affirming Practice to Better Support Sexual Assault Survivors in Ontario
Bibliographic record
Abstract
Background: Sexual violence against transgender (trans) persons is a complex public health issue that requires the coordinated effort of multiple sectors to address. A 2017 survey of Sexual Assault Nurse Examiners (SANEs) working within Ontario’s 36 Sexual Assault/Domestic Violence Treatment Centres (SA/DVTCs) revealed a need for training in the provision of trans-affirming care and highlighted a gap in knowledge related to local trans-positive organizations. In response, the successful design, pilot, and evaluation of a curriculum on trans-affirming care for SANEs was completed in 2018. However, there remained a pressing need to connect SANEs with trans-positive service providers across sectors to enhance the provision of care to trans survivors throughout Ontario.
 Goals and Objectives: To initiate the development a provincial intersectoral network on trans-affirming practice to better support sexual assault survivors by mobilizing knowledge on the new curriculum and connecting SA/DVTCs with local trans-positive community organizations.
 Approach: Guided by the Lifecycle Model of Network Development, seven regional meetings across the province were facilitated with leaders from SA/DVTCs and local trans-positive organizations. Key insights from meeting activities were transcribed and analyzed.
 Results: 106 representatives from 96 SA/DVTCs and trans-positive organizations attended a meeting between 7 June and 11 July 2019. 93 organizations expressed interest in being a part of the ongoing development of the network, in addition to 31 organizations unable to attend the meetings. 18 themes related to regional and provincial intersectoral collaboration to address sexual violence against trans communities were identified.
 Implications: As indicated by high meeting attendance and ongoing interest in developing a network, sexual violence against trans persons is a timely issue relevant to the enhancement of public health policy and practice across sectors. Informed by data gathered across the meetings, we aim to further consolidate the network, including working toward its maturation and sustainability.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".