The promise of an intersectoral network in enhancing the response to transgender survivors of sexual assault
Bibliographic record
Abstract
OBJECTIVES: This study explores the promise of an intersectoral network in enhancing the response to transgender (trans) survivors of sexual assault. METHODS: One hundred and three representatives of healthcare and community organizations across Ontario, Canada were invited to participate in a survey. Respondents were asked to: 1) identify systemic challenges to supporting trans survivors, 2) determine barriers to collaborating across sectors, and 3) indicate how an intersectoral network might address these challenges and barriers. Descriptive statistics were used to summarize quantitative data and qualitative data were collated thematically. RESULTS: Sixty-seven representatives responded to the survey, for a response rate of 65%. Several themes capturing the challenges organizations face in supporting trans survivors were identified: Lack of knowledge and training among providers, Inadequate resources across organizations and institutions, and Limited access to and availability of appropriate services. Barriers to collaborating across sectors considered important by the overwhelming majority of respondents were: Lack of trans-positive service professionals (e.g., a paucity of sensitivity training), lack of resources (e.g., staff, staff time and workload, spaces to meet), and Institutional structures (e.g., oppressive policies, funding mandates). Four ways in which a network could address these challenges and barriers emerged from the data: Center the voices of trans communities in advocacy; Support competence of professionals to provide trans-affirming care; Provide the platform, strategies, and tools to aid in organizational change; and Create space for organizations to share ideas, goals, and resources. CONCLUSION: Our findings deepen our understanding of important impediments to enhancing the response to trans survivors of sexual assault and the role networks of healthcare and community organizations can play in comprehensively responding to complex health and social problems.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".