Beyond inclusion politics: A critical discourse analysis of sex and gender in medical education
Bibliographic record
Abstract
INTRODUCTION: Medical training refers frequently to sex and gender, yet these terms are often used vaguely and interchangeably with little critical engagement. We aimed to better understand the key discourses around sex and gender in medical training. METHODS: All verbal and written instances of gendered/sexed language, occurring in lectures, slides and provided written materials, within 1 year of preclerkship medical education at a single institution in Canada were recorded autoethnographically by a medical student. We analysed these instances using critical discourse analysis informed by anticolonial, feminist and queer theoretical perspectives, particularly contrasting the concepts of governmentality and ungovernability to better understand how sex and gender are deployed in medical training. RESULTS: Despite the proliferation of ostensibly queer- and trans-positive content in medical curricula, sex and gender were frequently communicated inconsistently and imprecisely. In addition to, and perhaps as a result of, this inconsistent use, we uncovered three key discourses of (1) gender policing, (2) misogyny and (3) gender legibility. Collectively, these discourses can illustrate how medical education governs and reinforces gender norms. DISCUSSION: The repetitive and everyday use of ill-defined and uncritical gendered and sexed language can be seen to discursively uphold systems of cisheteropatriarchy and transphobia within medicine. Trans and intersex bodies cannot be considered addendums to be tacked on to a foundation of cisnormative and inaccurate teaching in medical education. Institutional attempts to improve curricula must go beyond the addition of new modules, which can be undermined by otherwise cisheteropatriarchal understandings of sex and gender.
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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 | 0.031 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.025 | 0.050 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".