Recommendations to Improve the Nature and Extent of Relationships Among Organizations Within a Network to Enhance Supports for Transgender Survivors of Sexual Assault
Bibliographic record
Abstract
CONTEXT: To enhance the provision of comprehensive supports to transgender (trans) survivors of sexual assault, a structurally marginalized group with complex care needs, we developed an intersectoral network of trans-positive health care and community organizations in Ontario, Canada. OBJECTIVE: As a baseline evaluation of the network, we conducted a social network analysis to determine the extent and nature of collaboration, communication, and connection among members. DESIGN: Relational data (eg, activities of collaboration) were collected from June to July 2021, and analyzed using a validated survey tool, Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER). We shared findings in a virtual consultation session with key stakeholders and facilitated discussion to generate action items. Consultation data were synthesized into 12 themes through conventional content analysis. SETTING: An intersectoral network in Ontario, Canada. PARTICIPANTS: Of the 119 representatives of trans-positive health care and community organizations invited to participate in this study, 78 (65.5%) completed the survey. MAIN OUTCOME MEASURES: Proportion/count of organizations collaborating with other organizations. Network scores for value and trust. RESULTS: Almost all (97.5%) invited organizations were listed as collaborators, representing 378 unique relationships. The network achieved a value score of 70.4% and trust score of 83.4%. The most prominent themes were "Communication and knowledge exchange channels," "Clearer roles and contributions," "Indicators of success," and "Client voices at the centre." CONCLUSION: As key antecedents of network success, high value and trust indicate that network member organizations are well positioned to further foster knowledge sharing, define their roles and contributions, prioritize the integration of trans voices in all activities, and, ultimately, achieve common goals with clearly defined outcomes. There is great potential to optimize network functioning and advance the network's mission to improve services for trans survivors by mobilizing these findings into recommendations.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".