“They just knew, and that makes all the difference”: Understanding positive healthcare experiences among trans people in Canada
Bibliographic record
Abstract
Background: Trans people face persistent systemic challenges and barriers in healthcare systems. Despite these obstacles, many trans people have had positive healthcare experiences, however little is known regarding their commonalities. Aims: This study aims to better understand factors that influence positive healthcare experiences among trans people. Methods: 33 longform interviews were conducted with trans individuals, with 29 being used for the final sample. Interviews were recorded, transcribed verbatim, and analyzed using NVIVO 12 software. Thematic analysis was conducted for the coding process with a combination of inductive and deductive approaches used to develop the coding frame. Results: Four healthcare provider (HCP) characteristics and two patient attributes were identified as promoters of positive healthcare experiences. Positive HCP characteristics included having a provider who was 1) a member of the LGBTQ community, 2) knowledgeable, experienced, and willing to learn about trans health, 3) transparent and empowered patients regarding their medical decisions; and 4) sensitive, accepting, and validating of patients' gender identities. The two patient attributes included: 1) engaging in self-advocacy regarding their care, and 2) being connected to a variety of supportive trans communities, both online and in-person. Discussion: Better understanding these positive healthcare experiences can help in the development of curricula and policy to facilitate improved quality healthcare for trans people in Canada.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".