An undergraduate medical curriculum framework for providing care to transgender and gender diverse patients: A modified Delphi study
Bibliographic record
Abstract
INTRODUCTION: The lack of attention to transgender and gender diverse (TGD) people in undergraduate medical education (UME) is a point of concern, particularly among medical students. A project was undertaken to develop a UME curriculum framework for teaching the healthcare needs of TGD people. METHODS: Using a modified Delphi methodology, four rounds of surveys were presented to an expert stakeholder group that included content experts, generalist physicians, UME teaching faculty, and medical students. Questions covered what content should be taught, who should teach the content, and how much time should be dedicated for this teaching. Once the Delphi process was complete, feedback on the provisional framework was sought from members of the TGD community to ensure it represented their needs and perspectives. RESULTS: 71 panel members and 56 community members participated in the study. Core values included the scope of the framework, and topics such as inclusivity, and safety in practice and in teaching. The framework included terminology, epidemiology, medical and surgical treatment, mental health, sexual and reproductive health, and routine primary care. There was also guidance on who should teach, time to be allocated, and the learning environment. DISCUSSION: There is a clear need to train tomorrow's doctors to provide competent and respectful healthcare services to and for TGD patients. Although local factors will likely shape the way in which this framework will be implemented in different contexts, this paper outlines a core UME-level curriculum framework for Canada and, potentially, for use in other parts of the world.
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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 | 0.042 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".