Trans health training objectives: A scoping review
Bibliographic record
Abstract
BACKGROUND: Transgender individuals are discriminated against in health care environments and consistently experience poorer health outcomes than their cisgender counterparts. Enhancing physician training in transgender-specific health is critical to closing the transgender health gap. METHODS: We conducted a scoping review to identify transgender health training objectives in Internal Medicine and Internal Medicine Subspecialty residency programmes in Canada and the United States. A systematic search was conducted from 1946 to 15 February 2022. Studies were eligible for inclusion if they were written in English, included transgender training objectives, and were aimed at resident physicians in Internal Medicine or Internal Medicine Subspecialty training programmes in Canada or the United States. FINDINGS: We found 4048 papers, of which 11 were included for analysis. Transgender health training objectives were synthesised into five themes, including (1) terminology, physiology, and gender presentation, (2) gender-affirming care and communication, (3) hormonal and surgical management, (4) routine health management and maintenance, and (5) equity, diversity, and inclusion in clinical care. The majority of objectives pertained to equity, diversity, and inclusion in clinical care, namely, respectful communication and non-judgemental care of transgender patients. DISCUSSION: Our findings provide a comprehensive overview of published transgender health objectives in Canada and the United States and highlight existing gaps in postgraduate medical education for Internal Medicine and Subspecialty programmes. CONCLUSIONS: We argue a need for standardisation of transgender-related residency training and suggest that postgraduate Internal Medicine programmes can utilise this review as a framework to begin enhancing transgender health education for their residents.
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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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".