Building a research agenda on preventing and addressing sexual assault and intimate partner violence against trans people: a two-stage priority-setting exercise
Bibliographic record
Abstract
BACKGROUND: Transgender (trans) people experience high rates of sexual assault (SA) and intimate partner violence (IPV) and seldom receive the care and supports they need post-victimization. However, there is little to no research that aids in the development or improvement of related interventions. We undertook a study to build a novel Canadian research agenda on SA/IPV against trans people to guide future work and address these profound gaps in knowledge. METHODS: Guided by the Child Health and Nutrition Research Initiative (CHNRI) method for research priority-setting, we developed and circulated two consecutive surveys to a multi-stakeholder group of government decision makers; mental health, health and social service providers, researchers and trans communities, among others, who proposed research questions related to preventing and addressing SA/IPV against trans persons. The initial survey launched March 2021 garnered responses from 213 stakeholders. These items were cleaned and collated into 20 final questions that fell within seven thematic areas. The refined research questions were evaluated in August 2021 on predefined criteria for answerability, feasibility, impact and equity by 79 of 95 survey 1 respondents who agreed to participate in the second survey (response rate = 83.2%). The questions were ranked using a research priority score calculated by dividing the sum of all the answers for each question across the four criteria by the number of answers received. RESULTS: All questions were highly rated on each individual criterion and each had an overall research priority score of above 80%, with the most highly ranked question falling within the theme, "improving quality and implementation of education and training: How can training (e.g., for university/college students, educators, nurses, physicians, social workers, police, lawyers, security guards) be improved to better support trans survivors of sexual assault and intimate partner violence?". CONCLUSIONS: These questions form Canada's first research agenda on SA/IPV against trans people. Together, they reflect the insights of stakeholder groups who have been historically excluded from research priority-setting processes and will guide future and much-needed work on the topic. Actionable information on preventing and addressing SA/IPV against trans persons will help reduce negative outcomes associated with being victimized.
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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 | 0.178 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.009 | 0.012 |
| 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".