Training Outcomes and Satisfaction in Canadian MD/PhD and MD/MSc Programs: Findings from a National Survey
Bibliographic record
Abstract
PURPOSE: Despite the impact of physician-scientists on scientific discovery and translational medicine, several reports have signalled their declining workforce, reduced funding, and insufficient protected research time. Given the paucity of outcome data on Canadian MD/PhD programs, this study presents a national portrait of the sociodemographic characteristics, training trajectories, productivity, and satisfaction in trainees and alumni from Canadian MD/PhD and MD/MSc programs. METHODS: Quantitative data were collected in a national survey launched in 2021. Respondents included 74 MD/PhD alumni and 121 trainees across 12 Canadian MD/PhD and MD/MSc programs. RESULTS: Among MD/PhD alumni, 51% were independent practitioners/researchers while others underwent residency training. Most trainees (88%) were in MD/PhD programs. Significantly more alumni identified as men than did trainees. Significantly more alumni conducted clinical and health services research, while more trainees conducted basic science research. Average time to MD/PhD completion was 8 years, with no correlation to subsequent research outcomes. Self-reported research productivity was highest during MD/PhD training. Concerning training trajectories, most alumni completed residency, pursued additional training, and practised in Canada. Finally, regression models showed that trainees and alumni were satisfied with programs, with significant moderators in trainee models. CONCLUSION: Survey findings showed Canadian MD/PhD and MD/MSc programs recruit more diverse cohorts of trainees than before, provide productive research years, and graduate alumni who pursue training and academic employment in Canada. Both alumni and trainees are largely satisfied with these training programs. The need to collect in-depth longitudinal data on Canadian MD/PhD graduates to monitor diversity and success metrics is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".