UNIVERSITY OF MONTREAL’S CLINICIAN-INVESTIGATOR PROGRAM: A 10-YEAR DESCRIPTIVE EVALUATION
Bibliographic record
Abstract
PURPOSE: Clinician-investigators have an important role in the development and implantation of new therapies and treatment modalities; however, there have been several reports highlighting a pending shortage in the clinician-investigators' workforce. In Canada, the Royal College has promoted the development of clinician-investigators programs (CIP) to facilitate the training of these individuals. There is currently a paucity of data regarding the outcomes of such programs. This study aims to identify the strengths and areas of improvement of the Montreal University CIP. Methods: An internet-based 51-question survey was distributed to all the alumni from the University of Montreal CIP. Participation was voluntary and no incentives were provided. The response rate was 64%. Results: Among respondents, 50% (n=16) had completed their clinical residency and all CIP requirements. The majority of these individuals (63%) had become independent investigators and had secured provincial and national funding. Satisfaction of the respondents was high regarding the overall program (85%), the research skills developed during the CIP (84%) and the financial support obtained during the program (72%). The satisfaction rate regarding career planning was lower (63%). Conclusion: This survey demonstrates that, while indicators are favorable, some areas still require improvement. Several steps to improve the CIP have been identified; notably, the transition from the CIP to early independent career has been identified as critical in the development of clinician-investigators and steps have been taken to improve this progression.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".