Transgender health content in medical education: a theory-guided systematic review of current training practices and implementation barriers & facilitators
Bibliographic record
Abstract
Health disparities faced by transgender people are partly explained by barriers to trans-inclusive healthcare, which in turn are linked to a lack of transgender health education in medical school curricula. We carried out a theory-driven systematic review with the aim to (1) provide an overview of key characteristics of training initiatives and pedagogical features, and (2) analyze barriers and facilitators to implementing this training in medical education. We used queer theory to contextualize our findings. We searched the PubMed/Ovid MEDLINE database (October 2009 to December 2021) for original studies that reported on transgender content within medical schools and residency programs (N = 46). We performed a thematic analysis to identify training characteristics, pedagogical features, barriers and facilitators. Most training consisted of single-session interventions, with varying modes of delivery. Most interventions were facilitated by instructors with a range of professional experience and half covered general LGBT+-content. Thematic analysis highlighted barriers including lack of educational materials, lack of faculty expertise, time/costs constraints, and challenges in recruiting and compensating transgender guest speakers. Facilitators included scaffolding learning throughout the curriculum, drawing on expertise of transgender people and engaging learners in skills-based training. Sustainable implementation of transgender-health objectives in medical education faces persistent institutional barriers. These barriers are rooted in normative biases inherent to biomedical knowledge production, and an understanding of categories of sex and gender as uncomplicated. Medical schools should facilitate trans-inclusive educational strategies to combat transgender-health inequities, which should include a critical stance toward binary conceptualizations of sex and gender throughout the curriculum.
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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 | 0.032 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".