Diversity of Physician Faculty in Obstetrics and Gynecology.
Bibliographic record
Abstract
OBJECTIVE: To investigate physician faculty diversity in obstetrics and gynecology (ob-gyn) and how it compares with other clinical departments and medical student demographics. STUDY DESIGN: Data from the Association of American Medical College's Faculty Roster were extracted to differentiate full-time physician faculty by gender and by underrepresentation in medicine (Black, Hispanic, Native American/ Alaskans, and Pacific Islanders). Whole population data were updated on a rolling basis from the earliest year of reliable data (1973) to the most recent year (2012). RESULTS: The total number of full-time ob-gyn faculty increased from 922 in 1973 to 4,208 in 2012. The increase in proportion of faculty who were women (from 9.9% to 52.7%) contributed to the growth of underrepresented faculty (from 7.7% to 13.3%) during this period. Percentages of ob-gyn faculty who were women and underrepresented in 2012 were higher than in other core clinical departments and similar to those of current medical student matriculants. CONCLUSION: Expansion of physician faculty in ob-gyn over the past 40 years has led to greater diversity than exists in many other departments and is more reflective of medical student demographics.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".