‘I'm not interested in research; i'm interested in services': How to better health and social services for transgender women living with and affected by HIV
Bibliographic record
Abstract
This paper presents results of a research priority setting process focused on trans women living with and affected by HIV across Canada. It features data from semi-structured interviews and focus groups conducted with a diverse group of 76 trans women in five urban centers across the country on how they have navigated health and social service programming within their geographic context. The results focus on the structure and types of services. Respondents offered simple, yet creative ways to address barriers to vital services based on their individual and collective experiences. Notably, participants stressed the need for 1) trans-friendly and trans-specific services, 2) integrated health services, and aid in navigating complex, overlapping systems, and 3) comprehensive community-based services. They also suggest employing trans women as care coordinators or case managers in order to foster more trans-friendly environments and empower community members. We identify concrete ways to improve health and social services at the level of service delivery and program design, as well as recommendations for future participatory research. We close with an interrogation of trans people, and trans women living with and affected by HIV in particular, as 'hard to reach' populations.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".