The landscape of Medicare policies for gender-affirming surgeries in Canada: an environmental scan
Bibliographic record
Abstract
BACKGROUND: Many studies have described barriers to gender-affirming surgery (GAS) in Canada; however, few have explored why these barriers persist. To address this knowledge gap, we sought to describe documents related to public health insurance (Medicare) for GAS to identify the types of procedures covered, variations in coverage across provinces and territories, and changes in policy over time. METHODS: We conducted a descriptive cross-sectional study using an environmental scan approach. We queried 23 government websites, the Google search engine, and an online legal database between July 2022 and April 2024 to gather gray literature documents related to GAS and Medicare. Variables from relevant documents were compiled to create a present, at-glance overview of GAS Medicare coverage for all provinces and territories and a timeline of policy changes across Canada. RESULTS: Eight provinces and three territories had documents or websites related to GAS Medicare coverage (85%). We identified 15 GAS procedures that were covered variably across Canada. Yukon (n = 14) covered the most types of GAS, while Quebec and Saskatchewan covered the least (n = 6). Mastectomy and genital surgeries were covered across Canada, but other GAS were rarely covered. Five provinces and territories provided coverage for travel-related costs. Our GAS Medicare timeline showed differential expansion of GAS coverage in Canada over the last 25 years. CONCLUSIONS : We provide previously unreported information regarding GAS Medicare coverage in Canada. We hope our findings will help patients and healthcare providers navigate a complicated public healthcare system. We also highlight barriers within GAS Medicare documents and make recommendations to alleviate those barriers.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".