Enhancing care for transgender and gender diverse survivors of intimate partner violence: an Ontario-wide survey examining health and social service providers’ learning needs
Bibliographic record
Abstract
OBJECTIVES: To better understand healthcare and social/community service providers' learning needs associated with supporting transgender and gender diverse (trans) persons who have experienced intimate partner violence (IPV). SETTING: An online survey was distributed through the trans-LINK Network in Ontario, Canada. RESPONDENTS: 163 of 225 healthcare and social/community service providers completed the survey (72.4% response rate) between November 2022 and February 2023. MAIN OUTCOME MEASURES: Expertise, training, workplace practices and learning needs related to supporting trans survivors of IPV. METHOD: Quantitative survey results were analysed descriptively and open-ended responses were organised thematically. In March 2022, survey results were shared with 33 stakeholders who helped define goals and objectives for an e-learning curriculum using Jamboard, data from which were collated and organised into themes. RESULTS: Most (66.3%) survey respondents described having provided professional support to trans survivors of IPV, but only one-third (38.0%) reported having received relevant training, and many of the trainings cited were in fact focused on other forms of violence or trans health generally. The majority reported a mid (44.9%) or low-mid (28.5%) level of expertise and almost unanimously agreed that they would benefit from (further) training (99.4%). The most commonly recommended goal/objective for a curriculum emerging from the stakeholder consultation was to facilitate collaboration, knowledge sharing and (safe) referrals among organisations. CONCLUSIONS: The results of this study highlight the critical need for an IPV curriculum specific to trans survivors and responsive to the needs of providers. As no one profession can address this complex issue in isolation, it is important that the curriculum aims to facilitate collaboration across sectors. In the absence of appropriate training and referrals, practitioners may perpetuate harm when caring for trans survivors of IPV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".