Canadian M.D.-Ph.D. Programs Produce Impactful Physician-Scientists: The McGill Experience
Bibliographic record
Abstract
On June 18, 2015, the Canadian Institutes of Health Research (CIHR) announced that it would terminate funding to M.D.-Ph.D. programs due to budget constraints, against the recommendations from two advisory panels. CIHR’s M.D.-Ph.D. program grants, which amounted to an annual average of $1.8 million in the form of 14 six-year studentships, represent only 0.15% of CIHR’s $1.2 billion operating budget. As over half of M.D.-Ph.D. trainees are dependent on these studentships, this poses a threat to physician-scientist training in Canada. In response to the current volatile funding climate, we surveyed McGill University’s M.D.-Ph.D. program alumni to assess its success in producing physician-scientists. In this program, 60.0% of graduates who have completed training have become physician-scientists, the majority being retained in Canada. These individuals have attained positions with sufficiently protected time for research and had grant success and significant publications for early- to mid-career investigators. This suggests that the current M.D.-Ph.D. system is an effective way of producing competent physician-scientists. As physician-scientists have remarkably contributed to Canadian healthcare innovation despite making up a fraction of physicians and researchers, vulnerability in the M.D.-Ph.D. pipeline would invariably affect the health of Canadians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".