Lesbian, Gay, Bisexual, and Transgender–Related Content in Undergraduate Medical Education
Bibliographic record
Abstract
CONTEXT: Lesbian, gay, bisexual, and transgender (LGBT) individuals experience health and health care disparities and have specific health care needs. Medical education organizations have called for LGBT-sensitive training, but how and to what extent schools educate students to deliver comprehensive LGBT patient care is unknown. OBJECTIVES: To characterize LGBT-related medical curricula and associated curricular development practices and to determine deans' assessments of their institutions' LGBT-related curricular content. DESIGN, SETTING, AND PARTICIPANTS: Deans of medical education (or equivalent) at 176 allopathic or osteopathic medical schools in Canada and the United States were surveyed to complete a 13-question, Web-based questionnaire between May 2009 and March 2010. MAIN OUTCOME MEASURE: Reported hours of LGBT-related curricular content. RESULTS: Of 176 schools, 150 (85.2%) responded, and 132 (75.0%) fully completed the questionnaire. The median reported time dedicated to teaching LGBT-related content in the entire curriculum was 5 hours (interquartile range [IQR], 3-8 hours). Of the 132 respondents, 9 (6.8%; 95% CI, 2.5%-11.1%) reported 0 hours taught during preclinical years and 44 (33.3%; 95% CI, 25.3%-41.4%) reported 0 hours during clinical years. Median US allopathic clinical hours were significantly different from US osteopathic clinical hours (2 hours [IQR, 0-4 hours] vs 0 hours [IQR, 0-2 hours]; P = .008). Although 128 of the schools (97.0%; 95% CI, 94.0%-99.9%) taught students to ask patients if they "have sex with men, women, or both" when obtaining a sexual history, the reported teaching frequency of 16 LGBT-specific topic areas in the required curriculum was lower: at least 8 topics at 83 schools (62.9%; 95% CI, 54.6%-71.1%) and all topics at 11 schools (8.3%; 95% CI, 3.6%-13.0%). The institutions' LGBT content was rated as "fair" at 58 schools (43.9%; 95% CI, 35.5%-52.4%). Suggested successful strategies to increase content included curricular material focusing on LGBT-related health and health disparities at 77 schools (58.3%, 95% CI, 49.9%-66.7%) and faculty willing and able to teach LGBT-related curricular content at 67 schools (50.8%, 95% CI, 42.2%-59.3%). CONCLUSION: The median reported time dedicated to LGBT-related topics in 2009-2010 was small across US and Canadian medical schools, but the quantity, content covered, and perceived quality of instruction varied substantially.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".