Accessibility and Insurance Coverage for Gender-affirming Surgery in Canada: A Cross-Sectional Analysis
Bibliographic record
Abstract
Purpose: While visibility and acceptance of the transgender community have increased across Canada, barriers persist in accessing gender-affirming surgeries (GAS). This study aims to determine the current state of GAS care in Canada, focusing on insurance coverage by province and identifying regions needing the development of specialized GAS programs. Methods: This cross-sectional study was conducted by examining provincial health ministry webpages and contacting health authorities and gender clinics to collect data on GAS coverage. Information on various procedures, including chest surgery, facial GAS, and genital surgeries was collected to determine which procedures are covered in each respective province. Geographic distribution of clinics that perform GAS procedures in Canada was also collected and sorted by referrals in-province and out-of-province. Results: There are 32 unique gender-affirming procedures covered by Canadian provincial health plans. Prince Edward Island provides the highest coverage of GAS procedures, while Nunavut covers the least. Quebec offers the most comprehensive in-province GAS program, with some in-province care available in the other provinces. The three territories generally lack access to any in-province procedures. Conclusions: Coverage for gender-affirming surgical procedures in Canada varies widely. Genital procedures have the most comprehensive coverage, chest surgeries are covered by most provinces, and facial GAS were only covered in two provinces. There is also a disparity between coverage and availability of GAS in most provinces. Physicians should advocate for broader coverage and targeted training and recruitment of GAS specialized surgeons in key geographic regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".