Lessons Learned in Transgender Peer Navigation: A Year of Reflective Journaling
Bibliographic record
Abstract
People who are trans and gender-diverse are underserved by the healthcare system; one way to improve healthcare access is with peer healthcare navigators. We piloted two trans peer health navigators from April 2021 to March 2022 in a small Canadian province. The purpose of this study was to explore how trans peer navigators experienced their work and work environment through reflective journalling. The navigators journalled roughly weekly. They were encouraged to interrogate their own biases and to think about what was omitted from conversations with others. Each journal was treated as a qualitative case study, anonymized and analyzed thematically using Interpretive Phenomenological Analysis. Six themes emerged: expected work, unexpected work, teamwork, lived experience, challenges, and systemic factors. These themes were complexly interwoven with a network of subthemes that frequently fell under multiple main themes and were highly emotionally charged, many both positively and negatively. The importance of navigators being transgender themselves was highlighted. The rewards came from being able to provide meaningful help to people in their community and the challenges came from not being respected by other healthcare providers and systemic barriers that prevented them from helping clients. The navigators successfully adapted their services to bridge some systemic barriers. This research has implications for improving both navigators' and clients' experiences.
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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 | 0.061 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.019 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".