Gender Disparities in Academic Outcomes Among Graduates of a Canadian MD-PhD Program
Bibliographic record
Abstract
PURPOSE: Women have traditionally been underrepresented in MD and MD-PhD training programs. Here, we describe the changing demographics of an MD-PhD Program over three distinct time intervals. METHODS: We designed a 64-question survey and sent it to 47 graduates of the McGill University MD-PhD program in Montréal, Québec, Canada, since its inception in 1985. We also sent a 23-question survey to the 24 students of the program in 2021. The surveys included questions related to demographics, physician-scientist training, research metrics, as well as academic and personal considerations. RESULTS: We collected responses from August 2020 to August 2021 and grouped them into three intervals based on respondent graduation year: 1995-2005 (n = 17), 2006-2020 (n = 23) and current students (n = 24). Total response rate was 90.1% (n = 64/71). We found that there are more women currently in the program compared to the 1995-2005 cohort (41.7% increase, p<0.01). In addition, women self-reported as physician-scientists less frequently than men and reported less protected research time. CONCLUSIONS: Overall, recent MD-PhD alumni represent a more diverse population compared with their earlier counterparts. Identifying barriers to training remains an important step in ensuring MD-PhD trainees become successful physician-scientists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".