MD/PhD Training in Canada: Results from a national trainee and program director review
Bibliographic record
Abstract
PURPOSE: There has been limited examination of clinician scientist training in Canada, particularly regarding training integration and funding. This study assessed program structure, funding, tuition and mentorship structures available at Canadian MD/PhD programs. METHODS: Clinician Investigator Trainee Association of Canada administered an anonymous survey to current trainees and program directors that captured program structure, trainee funding, tuition and mentorship opportunities and needs across institutions. RESULTS: In June 2015, 101/228 (44%) trainees and 9/13 (69%) program directors completed the online survey. In all programs, students completed the PhD degree prior to clerkship training. Seven programs offered research training upon completion of pre-clerkship, four offered concurrent clinical and research training, and three offered alternative structures. Nine held seminars exposing students to clinical and research integration and two offered clinician scientist skills courses. Stipend funding and tuition varied, especially during clinical training years. Regarding mentorship, all programs held regular meetings, though eight programs do not have formal mentorship opportunities. Both trainees and program directors identified the need for further career planning and development support as a student priority. CONCLUSION: MD/PhD programs varied by program structure, funding, tuition and mentorship opportunities. Mechanisms to share and spread program innovations should be instated. Students may benefit from concurrent research and clinical training as well as courses specific to clinician scientist skill development. Decreasing debt burden may attract and retain trainees in this demanding path. To ensure mentorship programs align with trainee priorities, program directors should directly collaborate with students in their development and evaluation.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.034 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".