Using focus groups to inform a peer health navigator service for people who are transgender and gender diverse in Saskatchewan, Canada
Bibliographic record
Abstract
BACKGROUND: This study investigated healthcare access and quality for people who are transgender and gender-diverse (PTGD) in Saskatchewan (SK), Canada, to inform a larger project that was piloting two peer health navigators for PTGD. METHODS: Two online focus groups were held. Nineteen participants were recruited to represent a broad range in age, gender and location in SK. Transcripts of the focus groups were analyzed using a thematic approach. RESULTS: The core theme that was identified was participants' desire for culturally safe healthcare. This core theme had two component themes: (1) systemic healthcare factors and (2) individual healthcare provider (HCP) factors. The healthcare system primarily acted as a barrier to culturally safe healthcare. HCPs could be either barriers or facilitators of culturally safe care; however, negative experiences outweighed positive ones. CONCLUSIONS: PTGD in SK face discrimination, with delays and barriers to care at all levels of the healthcare system. Peer health navigators can address some of these discrepancies; however, greater support is required for PTGD to be able to access culturally safe healthcare. PATIENT OR PUBLIC CONTRIBUTION: People with lived experience/PTGD were involved in all stages of this project. They were included on the team as community researchers and co-developed the research project, conducted the focus groups, participated in the analyses and are co-authors. As well, both navigators and all the participants in the focus groups were also PTGD.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".