Sexual and Gender Minority Identity Disclosure During Undergraduate Medical Education
Bibliographic record
Abstract
PURPOSE: To assess identity disclosure among sexual and gender minority (SGM) students pursuing undergraduate medical training in the United States and Canada. METHOD: From 2009 to 2010, a survey was made available to all medical students enrolled in the 176 MD- and DO-granting medical schools in the United States and Canada. Respondents were asked about their sexual and gender identity, whether they were "out" (i.e., had publicly disclosed their identity), and, if they were not, their reasons for concealing their identity. The authors used a mixed-methods approach and analyzed quantitative and qualitative survey data. RESULTS: Of 5,812 completed responses (of 101,473 eligible respondents; response rate 5.7%), 920 (15.8%) students from 152 (of 176; 86.4%) institutions identified as SGMs. Of the 912 sexual minorities, 269 (29.5%) concealed their sexual identity in medical school. Factors associated with sexual identity concealment included sexual minority identity other than lesbian or gay, male gender, East Asian race, and medical school enrollment in the South or Central regions of North America. The most common reasons for concealing one's sexual identity were "nobody's business" (165/269; 61.3%), fear of discrimination in medical school (117/269; 43.5%), and social or cultural norms (110/269; 40.9%). Of the 35 gender minorities, 21 (60.0%) concealed their gender identity, citing fear of discrimination in medical school (9/21; 42.9%) and lack of support (9/21; 42.9%). CONCLUSIONS: SGM students continue to conceal their identity during undergraduate medical training. Medical institutions should adopt targeted policies and programs to better support these individuals.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".