Learning by chance: Investigating gaps in transgender care education amongst family medicine, endocrinology, psychiatry and urology residents
Bibliographic record
Abstract
BACKGROUND: The transgender (trans) population is one of the most underserved in health care. Not only do they face discrimination and stigma from society as a whole, they also have difficulty accessing transition-related care, leading to adverse outcomes such as suicide. We aimed to increase understanding on how our current postgraduate education system contributes to a lack of care for trans patients. METHODS: Our study consisted of 11 semi-structured interviews conducted in 2016 with residents in the following specialties: family medicine (3), endocrinology (3), psychiatry (3), and urology (2). We used Framework Analysis to qualitatively analyze our data. RESULTS: Residents described a lack of trans care education in the core curriculum, in part due to a lack of exposure to experts in this area. They also expressed discomfort when dealing with trans patients, due to inexperience and lack of knowledge. Furthermore, residents in each specialty had false assumptions that other specialties had sufficient knowledge and expertise in trans care. DISCUSSION: This study highlights how the lack of teaching and clinical experiences with trans patients during residency contributes to the poor access to healthcare. By systematically embedding trans care in the curriculum, medical education can play a prominent role in addressing the healthcare disparities of this underserved population.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".