Missed opportunities: are residents prepared to care for transgender patients? A study of family medicine, psychiatry, endocrinology, and urology residents
Bibliographic record
Abstract
BACKGROUND: The transgender (trans) population faces multiple barriers in accessing health care, with knowledge deficits of health care providers contributing substantially. Trans patients report having to teach health care professionals about their own health needs. We compared perceptions of trans-care education and training across family medicine, psychiatry, endocrinology, and urology residency training programs at the University of Toronto. METHODS: We surveyed residents to assess their perceptions of and attitudes towards trans-care, exposure to trans patients, knowledge of trans-specific clinical care, and the state of trans-care education within their training. We used Likert scale data to identify patterns across residency programs. We collected open-ended responses to further explain quantitative findings where appropriate. RESULTS: Of 556 residents approached, 319 participated (response rate = 57.4%). Nearly all endocrinology and psychiatry residents agreed that trans-care falls within their scope of practice, while only 71% and 50% of family medicine and urology residents did, respectively. Though participants were at different stages of their postgraduate training when surveyed, only 17% of all participants predicted they would feel competent to provide specialty-specific trans-care by the end of their residency and only 12% felt that their training was adequate to care for this population. CONCLUSION: Though the study revealed a willingness to serve this population, there was a lack of clinical exposure and trans-related teaching within postgraduate curricula resulting in feelings of unpreparedness to meet the health care needs 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".