The contribution of undergraduate medical education dress codes to systemic discrimination: A critical policy analysis
Bibliographic record
Abstract
PURPOSE: Critical review of institutional policies is necessary to identify and eliminate structural discrimination in medical schools. Dress code policies are well known to facilitate discrimination in other settings. METHODS: In this critical policy analysis, the authors used qualitative inquiry guided by feminist critical policy analysis (FCPA) and critical race feminism (CRF) frameworks to understand how Canadian undergraduate medical school dress code policies may contribute to discrimination and a hostile culture for marginalised groups. Dress code policies were obtained from 14 of 17 Canadian medical schools in September 2021. Deductive content analysis of dress codes was performed independently and in parallel by all four members of a racially diverse study team using Edwards and Marshalls' established framework for applying FCPA and CRF to dress code policy statements. Inductive content analysis was used to classify statements that fell outside this framework. Using a historical and contemporary legal understanding of how dress code policies have been used to discriminate against marginalised groups, the authors analysed how recommendations or restrictions may contribute to discrimination of marginalised medical students. RESULTS: Fourteen dress code policies were analysed. Overall, there were five feminine-coded restrictions for every one masculine-coded restriction (n = 77/213 and n = 16/213, respectively). Some policies prohibited feminine-coded items (e.g. perfumes and bracelets) while specifically allowing masculine-coded items (e.g. cologne and watches). A discourse of 'professionalism' based on patient preferences prioritised Eurocentric patriarchal norms for appearance, potentially penalising racially and culturally diverse students. Most policies did not include a policy for appeals or accommodations. CONCLUSION: Canadian undergraduate medical school dress code policies overregulate women and gender, racially and culturally diverse students by explicitly and implicitly enforcing white patriarchal social norms. Administrators should apply best practices to these policies to avoid discrimination and a hostile culture to marginalised groups.
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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 | 0.179 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.026 | 0.052 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| 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".