“Walking on eggshells”: experiences of underrepresented women inmedical training
Bibliographic record
Abstract
INTRODUCTION: Medicine remains an inequitable profession for women. Challenges are compounded for underrepresented women in medicine (UWiM), yet the complex features of underrepresentation and how they influence women's career paths remain underexplored. This qualitative study examined the experiences of trainees self-identifying as UWiM, including how navigating underrepresentation influenced their envisioned career paths. METHODS: Ten UWiM family medicine trainees from one Canadian institution participated in semi-structured group interviews. Thematic analysis of the data was informed by feminist epistemology and unfolded during an iterative process of data familiarization, coding, and theme generation. RESULTS: Participants identified as UWiM based on visible and invisible identity markers. All participants experienced discrimination and "otherness", but experiences differed based on how identities intersected. Participants spent considerable energy anticipating discrimination, navigating otherness, and assuming protective behaviours against real and perceived threats. Both altruism and a desire for personal safety and inclusion influenced their envisioned careers serving marginalized populations and mentoring underrepresented trainees. DISCUSSION: Equity, diversity, and inclusion initiatives in medical education risk being of little value without a comprehensive and intersectional understanding of the visible and invisible identities of underrepresented trainees. UWiM trainees' accounts suggest that they experience significant identity dissonance that may result in unintended consequences if left unaddressed. Our study generated the critical awareness required for medical educators and institutions to examine their biases and meet their obligation of creating a safer and more equitable environment for UWiM trainees.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".