‘I don’t want to have to teach every medical provider’: barriers to care among non-binary people in the Canadian healthcare system
Bibliographic record
Abstract
It is well-known that trans and non-binary individuals experience worse health outcomes due to experiences of violence and discrimination. For this reason, accessible healthcare for trans and non-binary people is crucial. There is a lack of Canadian literature on the experiences of non-binary people within the healthcare system. This study sought to understand barriers to healthcare among non-binary people living in a mid-sized urban/rural region of Canada. Interviews were conducted between November 2019 to March 2020 with 12 non-binary individuals assigned female at birth, living in Waterloo Region, Ontario, Canada, as a part of a larger qualitative study exploring experiences within the community, healthcare and employment. Three broad themes were developed: erasure, barriers to access to healthcare, and assessing whether (or not) to come out. Sub-themes included institutional erasure, informational erasure, general healthcare barriers, medical transition healthcare barriers, anticipated discrimination, and assessing safety. Policy and institutional changes are needed to increase the safety and accessibility of healthcare services to non-binary individuals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".