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 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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".