Doggone fat: An analysis of the barriers to top surgery for fat trans and nonbinary people in Canada
Bibliographic record
Abstract
The use of BMI to determine eligibility for gender-affirming surgeries, specifically for top surgery, is not based in science. The present work analyzes current policies regarding BMI limits alongside research participants’ experiences with BMI limits when attempting to access gender-affirming surgeries, arguing that BMI use presents a major barrier for fat trans and nonbinary individuals seeking transition-related surgeries. Based on previous literature and research participants’ experiences, this work further argues that BMI limits (and the lack of transparency regarding these limits) are harmful insofar as their use may result in denials or delays for these surgeries, prescriptions for weight loss, and negatively affect the mental health of those seeking surgery. The use of BMI may be grounded more in concern about aesthetic surgical outcomes rather than concern about health. Variation in coverage in Canada for all essential parts of top surgery (specifically the lack of coverage for liposuction and its classification as cosmetic in some provinces) present economic barriers that affect fat individuals more than those who are thin or average.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".