“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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.015 | 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".