Clinician Investigator Training in Canada: A Review
Bibliographic record
Abstract
PURPOSE: The Royal College of Physicians and Surgeons of Canada undertook a review of its Clinician Investigator Program (CIP), 13 years after launching the program in response to shortages in clinical investigators. The primary study goals were to determine the outcomes, impact, strengths and weaknesses of CIP. METHODS: Focus groups and telephone interviews with current and past program directors (PD) and a detailed survey of current and former trainees were conducted. Thirteen PD and 45% of current and former trainees from 10 CIP participated. RESULTS: Since 1995, 12 CIP have been accredited and 553 residents have enrolled in CIP, with 194 completing CIP and residency training by 2008. PD recognized CIP as an excellent program that produces highly qualified clinical investigators; important for faculty renewal. Both trainees and PD identified the need to improve CIP funding. Most (84%) CIP trainees did not have prior graduate degrees. Most alumni had completed Masters (58%) or Doctoral (39%) programs during CIP and published on their CIP research (97%). Among alumni who completed CIP and residency, many obtained an academic appointment with protected time for research, with 39% receiving an external career award. Many (60%) alumni reported no drawbacks to CIP and recognized the added values included Royal College recognition, structured training, pursuit of graduate studies, integration of clinical/research training and enhanced mentorship. CONCLUSION: Since the progam's inception, the number of CIP in Canada has grown. CIP are recognized as important mechanisms for integrating clinical and research training during residency to produce highly qualified clinician investigators.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.047 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".