Addressing the Need to Educate Service Providers on Trans-Affirming Postsexual Assault Care: An E-Learning Curriculum
Bibliographic record
Abstract
Transgender (trans) survivors of sexual assault are often seen by health care providers who lack the necessary training to provide inclusive care and supports. To foster trans-affirming care provision postsexual assault, in 2019–2020, we developed and successfully evaluated an e-learning curriculum for forensic nurses working across Ontario, Canada. The curriculum, entitled Providing Trans-Affirming Care for Sexual Assault Survivors, was later broadened for use by various types of service providers and made freely accessible. Since this time, there has been good uptake of the curriculum across a diverse range of professionals and organizations. Our curriculum is one important and novel initiative to advance the provision of trans-affirming care and supports for trans survivors of sexual assault. The curriculum can be adapted, as necessary, to local contexts and used in other jurisdictions.
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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 | 0.001 |
| Science and technology studies | 0.004 | 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".