Training the next generation of Canadian Clinician-Scientists: charting a path to success
Bibliographic record
Abstract
Clinician-scientists are physicians with training in both clinical medicine and research that enables them to occupy a unique niche as specialists in basic and translational biomedical research. While there is widespread acknowledgement of the importance of clinician-scientists in today's landscape of evidence-based medical practice, training of clinician-scientists in Canada has been on the decline, with fewer opportunities to obtain funding. With the increasing length of training and lower financial compensation, fewer medical graduates are choosing to pursue such a career. MD-PhD programs, in which trainees receive both medical and research training, have the potential to be an important tool in training the next generation of clinician-scientists; however, MD-PhD trainees in Canada face barriers that include an increase in medical school tuition and a decrease in the amount of financial support. We examined the available data on MD-PhD training in Canada and identified a lack of oversight, a lack of funding and poor mentorship as barriers experienced by MD-PhD trainees. Specific recommendations are provided to begin the process of addressing these challenges, starting with the establishment of an overseeing national body that would track long-term outcome data for MD-PhD trainees. This national body could then function to implement best practices from individual programs across the country and to provide further mentorship and support for early-career physician-scientists. MD-PhD programs have the potential to address Canada's growing shortage of clinician-scientists, and strengthening MD-PhD programs will help to effect positive change.
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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 | 0.007 | 0.229 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".