Ivory tower in MD/PhD programmes: sticky floor, broken ladder and glass ceiling
Bibliographic record
Abstract
OBJECTIVE: Achieving gender equity in academic medicine is not only a matter of social justice but also necessary in promoting an innovative and productive academic community. The purpose of this study was to assess gender distribution in dual MD/PhD academic programme faculty members across North America. METHODS: Academic metrics were analysed to quantify the relative career success of academic faculty members in MD/PhD programmes. Measured parameters included academic and leadership ranks along with nominal research factors such as peer-reviewed research publications, H-index, citation number and years of active research. RESULTS: Χ² analysis revealed a statistically significant (p<0.0001, χ²=114.5) difference in the gender distribution of faculty and leadership across North American MD/PhD programmes. Men held 74.2% of full professor positions, 64% of associate professor positions, 59.4% of assistant professor positions and 62.8% of lecturer positions. Moreover, men occupied a larger share of faculty leadership roles with a statistically significant disparity across all ranks (p<0.001, χ²=20.4). A higher proportion of men held positions as department chairs (79.6%), vice chairs (69.1%) and programme leads (69.4%). CONCLUSION: Gender disparity was prevalent in the MD/PhD programmes throughout North America with women achieving a lower degree of professional stature than men. Ultimately, steps must be taken to support women faculty to afford them better opportunities for academic and professional advancement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".