Transgender Education in North American Family Medicine Clerkships: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Transgender persons face many barriers to accessing health care, including identifying a knowledgeable physician. Medical schools have made curricular changes addressing cultural competence in transgender medicine, but changes are inadequate to graduate physicians competent in gender-affirming health care. The aim of this study was to assess the current state of education on the comprehensive health care of transgender patients, including gender-affirming health care (GAH) strategies (hormone therapy, surgical interventions) in US and Canadian family medicine clerkships (FM clerkships) in addition to the beliefs and actions of the directors making those curricular decisions. METHODS: Questions regarding transgender education within FM clerkships were included in the 2018 Council of Academic Family Medicine's Educational Research Alliance (CERA) survey of family medicine clerkship directors. The online survey was distributed via email invitation to 128 US and 16 Canadian FM clerkship directors between June 21, 2018 and August 4, 2018. RESULTS: Seventy-two percent (68/94) of FM clerkship directors agreed transgender health care should be a required part of the medical school curriculum. Sixty-six percent report active advocacy within their institutions for increased curricular time devoted to transgender health care. Fifty-six percent (53/94) treat transgender patients in their own clinical practice, but just 26% agreed they were comfortable teaching transgender health care to medicals students. While the presence of transgender patients within the clinical practice did not have a significant impact on FM clerkship directors' comfort teaching this subject, having transgender friends or acquaintances did. CONCLUSIONS: FM clerkships are primed for inclusion of comprehensive transgender and GAH education in their curriculum. Increasing comfort of FM clerkship directors in teaching this subject area by providing accessible curriculum may encourage further uptake of this content into FM clerkships.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".