Voices of Women in Medicine: Reflections on Structural Inequities, Resilience, and Pathways Forward
Bibliographic record
Abstract
Despite growing representation in medicine, women physicians continue to face persistent inequities in pay, leadership, and recognition. To gain a deeper understanding of their lived experiences, we conducted interviews and surveys with 22 women physicians from across the United States and Canada. We analyzed 309 narrative excerpts using a mixed-methods thematic analysis. Seven key themes emerged: early influences and role models, family and life-course pressures, gendered dynamics in daily practice, structural inequities, wellness and burnout, leadership and mentorship, and visions for the future of medicine. Participants described how career demands often conflicted with family responsibilities, how bias and misidentification shaped legitimacy, and how systemic burdens intensified burnout. Yet they also emphasized the strength of resilience, mentorship, and optimism regarding technology, as well as collective advocacy. These narratives suggest that achieving gender equity in medicine is essential not only to ensure justice among clinicians but also to sustain compassionate, high-quality patient care.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.026 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| 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".