Addressing gaps in physician knowledge regarding transgender health and healthcare through medical education
Bibliographic record
Abstract
BACKGROUND: Transgender people (those people whose sex at birth does not "match" their felt gender identity) are a priority group for healthcare as they experience high rates of discrimination and related illnesses. Despite this, there is a trend of poor healthcare access for trans people due, in large part, to the denial of care on the part of physicians. A small body of literature is beginning to suggest that this denial of care may be due to a lack of physician knowledge as well as, in some cases, to transphobia. There is a dearth of research in Canada, however, exploring whether and/or how knowledge gaps create barriers to quality care, and whether medical education can attend to these gaps while and through addressing gender normativity. METHODS: =41) in Winnipeg, Manitoba. Methods included semi-structured individual interviews and focus groups. Data were transcribed and analyzed with NVivo qualitative data software using iterative methods. RESULTS: An overwhelming finding of this study was a lack of physician knowledge, as reported both by trans people and by physicians, that resulted in a denial of trans-specific care and also impacted general care. Transphobia was also identified as a barrier to quality care by both trans people and physicians. Physicians were open to learning more about trans health and healthcare. CONCLUSIONS: The findings suggest a pressing need for better medical education that exposes students to basic skills in trans health so that they can become competent in providing care to trans people. This learning must take place alongside anti-transphobia education. Based on these findings, we suggest key recommendations at the close of the paper for providing quality trans health curriculum in medical education.
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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 | 0.005 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".