Inequities Faced by Female Doctors Serving Communities of Need
Bibliographic record
Abstract
The reasons for sex inequity in medicine are complex and partly interface ethnic background, specialty choice, and practice location. Multiple factors influence career choices including cultural values, balancing family responsibilities with professional growth, and career mentoring and support. Over the last 40 years, the Sophie Davis/CUNY School of Medicine (CSOM) has pursued a mission to increase diversity in medicine at the same time in which it has fostered the importance of primary care and service in underserved areas of New York State. Data from 1524 CSOM graduates show an increase in the number of women and underrepresented groups, with about a quarter of them working in Health Professional Shortage Areas (HPSAs). When compared with their male counterparts, our female graduates report lower income for similar work hours, with this disparity increasing slightly between female and male doctors working in HPSAs. In addition, our female graduates have chosen primary care specialties at a ratio of nearly 2:1 when compared with their male peers. Despite these inequities, our female graduates report satisfaction with their career choices, primarily due to a strong commitment to serving back patients in those communities where some of them come from. More research is needed to identify specific factors that perpetuate pay inequity at the state level to minimize the implications of disparity for women doctors, particularly those working in low-income communities.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".