INTERNATIONAL TRAINING CONSIDERATIONS OF CANADIAN CLINICIAN-SCIENTIST TRAINEES—A NATIONAL SURVEY
Bibliographic record
Abstract
PURPOSE: Canadian clinician-scientist trainees enrolled in dual degree programs often pursue an extended training route following completion of MD and MSc or PhD degrees. However, the proportion, plans and reasoning of trainees who intend to pursue training internationally following dual degree completion has not been investigated. In this study, we assessed the international training considerations of current clinician-scientist trainees. METHODS: We designed an 11-question survey, which was sent out by program directors to all current MDPhD program and Clinician Investigator Program (CIP) trainees. Responses were collected from July 8, 2019 to August 8, 2019. RESULTS: We received a total of 191 responses, with representation from every Canadian medical school and both MD-PhD program and CIP trainees. The majority of trainees are considering completing additional training outside Canada, most commonly post-doctoral and/or clinical fellowships. The most common reasons for considering international training include those related to quality and prestige of training programs. In contrast, the most common reasons for considering staying in Canada for additional training are related to personal and ethical reasons. Irrespective of intentions to pursue international training, the majority of trainees ultimately intend to establish a career in Canada. CONCLUSION: While most trainees are considering additional training outside of Canada due to prestige and quality of training, the majority of trainees intend to pursue a career as a clinician-scientist back in Canada. Trainees would likely benefit from improved guidance and mentorship on the value of international training, as well as enhanced support in facilitating cross-border mobility.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".