Career and research outcomes of the physician-scientist training program at the University of Calgary: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Physician-scientists are integral to medical research, with medical programs throughout Canada invested in training hybrid physician-scientists. Few data exist as to whether these programs are generating the diversity, gender equity and numbers of trainees essential for the future of medical research and teaching. We aimed to identify factors that contribute to research productivity, diversity and retention of individuals as physician-scientists. METHODS: We completed a retrospective cohort study, for the period 1973 to 2015, of the University of Calgary Leaders in Medicine Program in Calgary, Alberta. Participants were coregistered in graduate (master's or PhD) and medical degree programs. Primary outcomes included number of publications and the eventual career paths of graduates, with individuals characterized as physicians or physician-scientists on the basis of these metrics. RESULTS: Of the 307 individuals who were coregistered in or had completed a joint graduate and medical degree, 125 (40.7%) were PhD students/graduates, and 182 (59.3%) were master's trainees/graduates. While in the joint program, male PhD students consistently published more frequently than female PhD students. There was no significant difference in publication records between male and female master's students. Of the 172 individuals who were 5 years or more beyond graduation, 47 (27.3%) were classified as physician-scientists; these individuals consisted of 28 (40.6%) of the 69 PhD graduates and 19 (18.4%) of the 103 master's graduates. INTERPRETATION: Overall, our study shows that graduates receiving both clinical and research training, through master's or PhD programs, continue to be involved in research in their subsequent careers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".