Experiences of Transgender and Gender Nonbinary Medical Students and Physicians
Bibliographic record
Abstract
Abstract Purpose: To explore the experiences of transgender and gender nonbinary (TGNB) medical students and physicians in the United States. Methods: The authors conducted a 79-item online survey using Likert-type and open-ended questions to assess the experiences of TGNB-identified U.S. medical students and physicians. Variables included demographic data, disclosure of TGNB status, exposure to transphobia, and descriptions of educational and professional experiences. Recruitment was conducted using snowball sampling through Lesbian, Gay, Bisexual, Transgender, Queer professional groups, list-servs, and social media. The survey was open from June 2017 through November 2017. Results: Respondents included 21 students and 15 physicians (10 transgender women, 10 transgender men, and 16 nonbinary participants). Half (50%; 18) of the participants and 60% (9) of physicians had not disclosed their TGNB identity to their medical school or residency program, respectively. Respondents faced barriers on the basis of gender identity/expression when applying to medical school (22%; 11) and residency (43%; 6). More than three-quarters (78%; 28) of participants censored speech and/or mannerisms half of the time or more at work/school to avoid unintentional disclosure of their TGNB status. More than two-thirds (69%; 25) heard derogatory comments about TGNB individuals at medical school, in residency, or in practice, while 33% (12) witnessed discriminatory care of a TGNB patient. Conclusion: TGNB medical students and physicians faced significant barriers during medical training, including having to hide their identities and witnessing anti-TGNB stigma and discrimination. This study, the first to exclusively assess experiences of TGNB medical students and physicians, reveals that significant disparities still exist on the basis of gender identity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".