Challenges Facing Physician Scientist Trainees: a Survey of Trainees in Canada’s Largest Undergraduate and Postgraduate Programs in a Single Centre
Bibliographic record
Abstract
PURPOSE: A number of indicators suggest that the physician scientist career track is threatened. As such, it is an opportune time to evaluate current training models. Perspectives on physician scientist education and career path were surveyed in trainees at the University of Toronto, home to Canada's longest standing physician scientist training programs. METHODS: Trainees from the Clinician Investigator Program (CIP) and MD/PhD Program at the University of Toronto were surveyed. Liekert-style closed-ended questions were used to assess future career goals, present and future perspectives and concerns about and beliefs on training. Demographic information was collected regarding year of study, graduate degree program and focus of clinical and health research. Statistical analysis included non-parametric tests for sub-group comparisons. RESULTS: Both groups of trainees were motivated to pursue a career as a physician scientist. While confident in their decision to begin and complete physician scientist training, they expressed concerns about the level of integration between clinical and research training in the current programs. They also expressed concerns about career outlook, including the ability to find stable and sustainable careers in academic medicine. Trainees highlighted a number of factors, including career mentorship, as essential for career success. CONCLUSION: These findings indicate that while trainees at different stages consistently express career motivation, they identified concerns that are program- and training stage-specific. These concerns mirror those highlighted in the medical education literature regarding threats to the physician scientist career path. Understanding these different and changing perspectives and exploring those differences could form an important basis for trainee program improvements both nationally and internationally.
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 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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| 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".