Formation of an intersectoral network to support trans survivors of sexual assault: A survey of health and community organizations
Bibliographic record
Abstract
BACKGROUND: To address the growing international recognition of the inequities faced by transgender (trans) persons and the lack of services that attend to the specific concerns of trans sexual assault survivors, we undertook the development of an intersectoral network of hospital-based violence treatment centers and trans-positive community organizations to enhance available supports. AIMS: To examine anticipated involvement of organizations in the network and determine network activities, deliverables, and values. METHODS: We developed a survey with guidance from an advisory group of trans community members and their allies. Items on the survey related to network activities, deliverables, and values, which were also informed by key insights from earlier network planning meetings, were rated on a 5-point Likert scale for their importance (1 = not important at all, 5 = very important). RESULTS: Sixty-four out of 93 organizations invited responded to the survey, giving a response rate of 69%. The highest prioritized network activities were: improve access to support services for trans survivors, educate trans survivors on their rights/what to expect when seeking supports and information on organizations, provide ongoing education/training for service providers on trans-affirming care, and inform guidelines on appropriate and sensitive standards of care/better practices for trans survivors (means = 4.6). The highest prioritized deliverables were: provision of standardized sensitivity training on violence against trans persons for professionals and development of an online directory/resource list of trans-affirming service providers and organizations that is continuously updated (means = 4.5). Trauma- and violence-informed and trans-guided were the most highly rated values (means = 4.8). CONCLUSION: These findings have implications for healthcare and community leaders seeking to collaborate across sectors to address the inequities faced by trans persons experiencing sexual assault.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".