Sexual and gender minority content in undergraduate medical education in the United States and Canada: current state and changes since 2011
Bibliographic record
Abstract
PURPOSE: To characterize current lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI +) health-related undergraduate medical education (UME) curricular content and associated changes since a 2011 study and to determine the frequency and extent of institutional instruction in 17 LGBTQI + health-related topics, strategies for increasing LGBTQI + health-related content, and faculty development opportunities. METHOD: Deans of medical education (or equivalent) at 214 allopathic or osteopathic medical schools in Canada and the United States were invited to complete a 36-question, Web-based questionnaire between June 2021 and September 2022. The main outcome measured was reported hours of LGBTQI + health-related curricular content. RESULTS: Of 214 schools, 100 (46.7%) responded, of which 85 (85.0%) fully completed the questionnaire. Compared to 5 median hours dedicated to LGBTQI + health-related in a 2011 study, the 2022 median reported time was 11 h (interquartile range [IQR], 6-16 h, p < 0.0001). Two UME institutions (2.4%; 95% CI, 0.0%-5.8%) reported 0 h during the pre-clerkship phase; 21 institutions (24.7%; CI, 15.5%-33.9%) reported 0 h during the clerkship phase; and 1 institution (1.2%; CI, 0%-3.5%) reported 0 h across the curriculum. Median US allopathic clerkship hours were significantly different from US osteopathic clerkship hours (4 h [IQR, 1-6 h] versus 0 h [IQR, 0-0 h]; p = 0.01). Suggested strategies to increase content included more curricular material focusing on LGBTQI + health and health disparities at 55 schools (64.7%; CI, 54.6%-74.9%), more faculty willing and able to teach LGBTQI + -related content at 49 schools (57.7%; CI, 47.1%-68.2%), and more evidence-based research on LGBTQI + health and health disparities at 24 schools (28.2%; CI, 18.7%-37.8%). CONCLUSION: Compared to a 2011 study, the median reported time dedicated to LGBTQI + health-related topics in 2022 increased across US and Canadian UME institutions, but the breadth, efficacy, or quality of instruction continued to vary substantially. Despite the increased hours, this still falls short of the number of hours based on recommended LGBTQI + health competencies from the Association of American Medical Colleges. While most deans of medical education reported their institutions' coverage of LGBTQI + health as 'fair,' 'good,' or 'very good,' there continues to be a call from UME leadership to increase curricular content. This requires dedicated training for faculty and students.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".