Bridging Gaps in Collaboration Between Community Organizations and Hospital-Based Violence Treatment Centers Serving Transgender Sexual Assault Survivors
Bibliographic record
Abstract
Community and healthcare organizations have not historically collaborated effectively, leaving gaps in the continuum of care for survivors of sexual assault. These gaps are particularly acutely felt by transgender (trans) survivors, who experience additional barriers to care and face higher rates of sexual assault. To bridge these gaps and enhance the provision of comprehensive support for trans people, we developed an intersectoral network of trans-positive community and hospital-based organizations in Ontario, Canada. As part of a baseline evaluation of the network, we conducted a social network analysis to determine the extent and nature of collaboration between members within and across these two sectors. Using a validated social network analysis tool (PARTNER survey), data were collected from June 22 to July 22, 2021. The extent of collaboration was examined by relationship type: intrasectoral (same sector) and intersectoral (different sectors). The nature of collaboration was examined using relational scores (value: power, level of involvement, potential resource contribution; trust: reliability, mission congruence, openness to discussion). Fifty-four community organizations (65.9% of 82 invited) and 24 hospital-based violence treatment centers (64.9% of 37 invited) responded. The majority of collaborations were within, rather than across, the two sectors: of all 378 collaborations described, 70.9% ( n = 268) were intrasectoral collaborations and 29.1% ( n = 110) were intersectoral collaborations. Intersectoral relationships were characterized by lower scores for level of involvement, trust, reliability, and mission congruence than intrasectoral relationships, but higher scores for power. These findings were shared in a virtual consultation session of key stakeholders, in which some participants expressed “surprise” and concern for the lack of collaboration and character of relationships across sectors. Recommendations to increase intersectoral collaboration, which included intersectoral program planning and service design and supporting increased opportunities for intersectoral training and knowledge exchange, are presented.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".