Gender-Affirming Surgery for Transgender Individuals: Perceived Satisfaction and Barriers to Care
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the perceived satisfaction and barriers to care for transgender patients after they decide to undergo gender-affirming surgery (GAS). METHOD: A survey consisting of 21 multiple-choice and short-answer questions was distributed to transgender organizations and online forums across Canada and the United States. The data were then analyzed using descriptive statistics. RESULTS: There were 32 participants, 12 who identified as female to male and 20 as male to female. The mean age was 36 years, with a range of 18 to 81 years. The mean age of their first GAS was 33 years, and the range of wait time was 6 months to 7 years. Most of the participants received information about GAS from transgender websites and transgender surgery clinics (91% and 50%, respectively). Most participants (74%) felt like they had access to appropriate care and 89% felt like their surgeons provided enough information about GAS. There were 38% of participants who would change their experience with GAS. Participants stated several barriers toward receiving GAS: financial (73%), finding a physician (65%), and access to information (63%). Surgical transition was important to the quality of life for 91% of participants and 100% were happy with their decision to undergo GAS. CONCLUSIONS: Transgender participants demonstrated that GAS is important to their quality of life and this study showed significant barriers to GAS.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".