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 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 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".