Current State of Transgender Medical Education in the United States and Canada: Update to a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The published literature on education about transgender health within health professions curricula was previously found to be sporadic and fragmented. Recently, more inclusive and holistic approaches have been adopted. We summarize advances in transgender health education. METHODS: A 5-stage scoping review framework was followed, including a literature search for articles relevant to transgender health care interventions in 5 databases (Education Source, LGBT Source, MedEd Portal, PsycInfo, PubMed) from January 2017 to September 2019. Search results were screened to include original articles reporting outcomes of educational interventions with a transgender health component that included MD/DO students in the United States and Canada. A gray literature search identified continuing medical education (CME) courses from 12 health professional associations with significant transgender-related content. RESULTS: Our literature search identified 966 unique publications published in the 2 years since our prior review, of which 10 met inclusion criteria. Novel educational formats included interdisciplinary interventions, post-residency training including CME courses, and online web modules, all of which were effective in improving competencies related to transgender health care. Gray literature search resulted 15 CME courses with learning objectives appropriate to the 7 professional organizations who published them. CONCLUSIONS: Current transgender health curricula include an expanding variety of educational intervention formats driven by their respective educational context, learning objectives, and placement in the health professional curriculum. Notable limitations include paucity of objective educational intervention outcomes measurements, absence of long-term follow-up data, and varied nature of intervention types. A clear best practice for transgender curricular development has not yet been identified in the literature.
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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 | 0.024 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.052 | 0.068 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".