“Will I be able to be myself? Or will I be forced to lie all the time?”: How Trans and Non-Binary Students Balance Professionalism, Authenticity, and Safety in Canadian Medical Programs
Bibliographic record
Abstract
Introduction: Promoting the inclusion of trans and non-binary (TNB) medical trainees is a key step in building an inclusive health workforce well-positioned to provide high-quality healthcare to all patients. Existing data on the experiences of TNB physicians and trainees describe widespread challenges related to prejudice and discrimination, with most trainees concealing their gender identity for fear of discrimination. We aimed to understand how TNB medical students have experienced professionalism and professional identity formation. Methods: This was a secondary analysis of data gathered in a constructivist grounded theory study. The authors conducted semi-structured qualitative interviews in 2017 with seven current or recently graduated TNB Canadian medical students. Results and Discussion: From medical school application to graduation, TNB medical students reported feeling tensions between meeting expectations of professionalism, being their authentic selves, and seeking to avoid conscious and implicit biases. These tensions played out around issues of disclosure, foregrounding identity through impression management, and responding to identity exemplars. The tension between TNB trainees' desire to bring their whole selves to the practice of medicine and feeling pressured to de-emphasize their gender is ironic when considering the increased call for medical trainees from equity-seeking communities. The most commonly used behavioural frameworks of professionalism were inherited from prior generations and restrict students whose experiences and community-based knowledge are most needed. Demands of professionalism that are incompatible with authentic professional identity development place an inordinate burden on trainees whose identities have been excluded from normative concepts of the professional, including TNB trainees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".