The Canadian clinician-scientist training program must be reinstated
Bibliographic record
Abstract
Clinical investigators within the Canadian and international communities were shocked when the Canadian Institutes of Health Research (CIHR) announced that their funding for the MD/PhD program would be terminated after the 2015-2016 academic year. The program has trained Canadian clinician-scientists for more than two decades. The cancellation of the program is at odds with the CIHR's mandate, which stresses the translation of new knowledge into improved health for Canadians, as well as with a series of internal reports that have recommended expanding the program. Although substantial evidence supports the analogous Medical Scientist Training Program in the United States, no parallel analysis of the MD/PhD program has been performed in Canada. Here, we highlight the long-term consequences of the program's cancellation in the context of increased emphasis on translational research. We argue that alternative funding sources cannot ensure continuous support for students in clinician-scientist training programs and that platform funding of the MD/PhD program is necessary to ensure leadership in translational research.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".