Health care access among transgender and nonbinary people in Canada, 2019: a cross-sectional survey
Bibliographic record
Abstract
Background: Previous Canadian studies have identified problems regarding health care access for transgender (trans) and nonbinary people, but all-ages national data have been lacking. This study describes access to care among trans and nonbinary people in Canada, and compares health care access across provinces or regions. Methods: We conducted a bilingual, multimode cross-sectional survey (Trans PULSE Canada) from July 26 to Oct. 1, 2019. We recruited trans and nonbinary people aged 14 years and older using convenience sampling. We assessed 5 outcomes: having a primary care provider, having a primary care provider with whom the respondent was comfortable discussing trans health issues, past-year unmet health care need, medical gender affirmation status, and being on a wait-list to access gender-affirming medical care. Average marginal predictions were estimated from multivariable logistic regression models with multiply imputed data. Results: The survey included 2873 participants, and 2217 surveys were analyzed after exclusions. Of the 2217 trans and nonbinary respondents, most had a primary care provider (n = 1803; 81.4%, 95% confidence interval [CI] 79.8%–83.0%), with model-predicted probabilities from 52.1% (95% CI 20.2%–84.1%) in the territories to 92.9% (95% CI 83.5%–100.0%) in Newfoundland and Labrador. Of the respondents, 52.3% (n = 1150; 95% CI 50.3%–54.2%) had a primary care provider with whom they were comfortable discussing trans health issues, and 44.4% (n = 978; 95% CI 42.3%–46.4%) reported an unmet health care need. Among participants who needed gender-affirming medical treatment (n = 1627), self-defined treatment completion ranged from an estimated 16.8% (95% CI 0.6%–32.5%) in Newfoundland and Labrador to 59.1% (95% CI 52.5%–65.6%) in Quebec. Of those who needed but had not completed gender-affirming care at the time of the study (n = 1046), 40.7% (n = 416; 95% CI 37.8%–43.6%) were on a wait-list, most often for surgery. These outcomes, with the exception of having a provider with whom one is comfortable discussing trans issues, varied significantly by province or region (p < 0.05). Interpretation: Participants reported considerable unmet needs or delays in primary, general and gender-affirming care, with significant regional variation. Our results indicate that, despite efforts toward equity in access to care for trans and nonbinary people in Canada, inequities persist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".