Lessons Learned Developing Client Navigation for People who are Trans and Gender Diverse
Bibliographic record
Abstract
BACKGROUND: People who are trans and gender diverse (PTGD) are underserved regarding healthcare in Canada, including the province of Saskatchewan. OBJECTIVES: Design and conduct a research project that will address immediate and pressing community-identified needs related to improving access to healthcare for PTGD in Saskatchewan. METHODS: A multidisciplinary, community-based collaboration was established to address the self-identified obstacles to accessing healthcare of PTGD in Saskatchewan. This resulted in a pilot study creating and evaluating a healthcare navigation program. LESSONS LEARNED: The project led to four key lessons: 1) prioritizing team building and the well-being of team members; 2) committing to community-based participatory approaches from the outset; 3) taking language seriously; and 4) acknowledging and addressing power imbalances in our team. CONCLUSIONS: The lessons we learned have enabled us to sustain a large, diverse, research team that centers the experience of PTGD in Saskatchewan and is responsive to community need.
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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 | 0.004 | 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 | 0.002 | 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".