A transgender medicine curriculum for Internal Medicine residents at a single academic institution
Bibliographic record
Abstract
BACKGROUND: Transgender or gender diverse patients present with health care needs as it relates to gender-affirming care, psychosocial support, and medication access. Considering this, medical education strategies should be implemented to train the next generation of Internal Medicine physicians in this area. METHODS: We adopted Kern's six step approach to curriculum design to create and implement an educational curriculum for teaching Internal Medicine residents about transgender patients at a single academic institution in Canada (Kern et al, Curriculum Development for Medical Education, 2009). Semi-structured individual interviews of Internal Medicine faculty and residents were conducted to understand the needs for delivering content related to transgender health during residency training. The results informed the development and implementation of an academic half day session and objective structured clinical examination (OSCE). Prior to the academic half day, participants were sent three journal articles highlighting key concepts related to the medical care of transgender patients (CMAJ 193:E562-5, 2021; BMC Public Health 15:525, 2015; Bourns A, Guidelines for gender-affirming primary care with trans and non-binary patients, 2019). A pre- and post-intervention numeric assessment form evaluated residents' comfort in providing gender-affirming care across five domains: general care, communication, hormone therapy, side effects, and prevention strategies. Faculty raters, equipped with training in transgender health, evaluated performance in an OSCE on key transgender health issues. Both the clinical case and OSCE scenario were developed via an iterative review process by study investigators. RESULTS: Residents reported an increase in subjective comfort of providing gender-affirming care (p = 0.042) following delivery of the academic half day content. The OSCE demonstrated that most residents achieved "acceptable" or "exemplary" ratings across multiple domains. CONCLUSIONS: We implemented a Transgender Medicine curriculum for Internal Medicine residents in the Canadian context. The curriculum improved resident comfort in providing gender-affirming care including basic knowledge on gender-affirming hormone therapy, and health promotion activities (e.g., screening for and management of chronic disease, discussion of sexual health, community supports, peer engagement). Further assessment is needed to determine whether these skills are retained in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".