Interviews to Assess a Peer Health Navigator Service for People Who Are Transgender or Gender Diverse
Bibliographic record
Abstract
PURPOSE: People who are transgender or gender diverse (PTGD) often experience difficulties navigating the health care system due to a variety of factors such as lack of knowledgeable and/or culturally competent clinicians, discrimination, and structural and/or socioeconomic barriers. We sought to determine whether a peer health navigator service in the Canadian province of Saskatchewan helped connect transgender and gender-diverse clients and health care practitioners (HCPs) to resources, and how this service changed their health care experiences. METHODS: Semistructured interviews were conducted with 9 clients and 9 HCPs. Interview transcripts were then analyzed by researchers using an interpretative phenomenological approach, with qualitative data analysis software. RESULTS: The most prevalent theme that emerged from interview data, from both clients and HCPs, was support for the navigators' work and a desire that the service should continue. It was reinforced by 3 subthemes: the importance that the navigators were PTGD, the ability of the navigators to connect people to services and reliable sources of information, and their skill in directly supporting clients. A fourth subtheme, primarily found among clients, was the navigators' ability to provide connections to affirming mental health care. CONCLUSIONS: Clients and HCPs alike emphasized that the navigator's lived experience was invaluable and allowed them to empathize with PTGD and provide support. Furthermore, the navigators acted as a direct connection to health care services, which helped improve access for clients. Our findings underscore the need for navigator positions to become permanent within the provincial health system to improve the health care experiences of PTGD in Saskatchewan.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".